Description:FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to the field of aquaculture, fish farming, automation and more particularly, an aquaculture management system and a method thereof.
BACKGROUND
[0002] Aquaculture has emerged as a vital industry to meet the global demand for seafood, which continues to increase due to overfishing in natural habitats and the growing global population. Fish farming operations have been widely adopted in both inland and coastal regions, aiming to ensure a consistent supply of fish while reducing pressure on wild aquatic ecosystems. However, traditional aquaculture practices still face several challenges that impact operational efficiency, environmental sustainability, and profitability.
[0003] One of the major challenges in conventional fish farming is maintaining optimal water quality. Key parameters such as temperature, pH, turbidity, and dissolved oxygen levels must be carefully regulated to ensure healthy fish growth and prevent diseases. Manual monitoring of these variables is time-consuming, labour-intensive, and prone to inaccuracies, often resulting in delayed interventions when adverse conditions arise.
[0004] Feeding is another crucial factor in aquaculture management. Underfeeding or overfeeding can significantly affect fish health and increase operational costs. Excess feed not consumed by the fish may decay, leading to a drop in water quality and posing environmental risks.
[0005] Structural integrity of fish cages and pens is often compromised due to prolonged exposure to water, mechanical stress, or biological factors like biofouling. Regular inspections are necessary to prevent issues such as holes, loose netting, or damaged anchors, which may lead to fish escape or predator entry. However, these inspections are typically performed manually and may not identify problems early enough to prevent significant losses.
[0006] Another challenge is the lack of data-driven decision-making in aquaculture. Fish farmers often rely on experience or periodic assessments rather than real-time insights, which can limit responsiveness to dynamic environmental changes or emerging health concerns. Additionally, varying market trends, consumer demand, and seasonal fluctuations make it difficult for farmers to align production with market requirements and optimize profitability.
[0007] Hence, there is a need for an improved aquaculture management system and the method thereof which addresses the aforementioned issue(s).
OBJECTIVES OF THE INVENTION
[0008] The primary objective of the invention is to provide an intelligent aquaculture management system that utilizes artificial intelligence (AI) and Internet of Things (IoT) technologies to automate and optimize fish farming operations in real time.
[0009] Another objective of the invention is to enable continuous monitoring of critical water quality parameters such as temperature, pH, turbidity, and dissolved oxygen levels to maintain a stable aquatic environment conducive to healthy fish growth.
[0010] Yet another objective of the invention is to implement an AI-driven automated feeding mechanism that dispenses appropriate quantities of food based on fish behaviour, growth stage, and environmental conditions, thereby reducing waste and ensuring optimal nutrition.
[0011] Another objective is to detect and predict anomalies or harmful trends in the aquatic environment through AI-based analysis of sensor data and image processing, allowing for timely preventive actions.
[0012] Yet another objective is to identify structural weaknesses in the fish cage, such as holes, loose nets, and anchor issues, using image-based detection and predictive maintenance alerts to prevent escape or damage.
[0013] Another objective of the invention is to provide species-specific recommendations based on environmental conditions, dietary requirements, and breeding guidelines to support efficient and tailored aquaculture practices.
[0014] Yet another objective is to analyse real-time market trends, consumer demand, seasonal variations, and pricing to guide fish procurement, production planning, and trading strategies for maximizing profitability.
[0015] Another objective is to facilitate user interaction and control through an in-app chat interface that delivers actionable insights, alerts, and operational controls for simplified and intelligent farm management.
BRIEF DESCRIPTION
[0016] In accordance with an embodiment of the present disclosure, an aquaculture management system is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an IoT module comprising an artificial intelligence model. The artificial intelligence model is configured to analyze sensor data received from a plurality of IoT sensors and a plurality of images received from a camera mounted in a fish cage, wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions. The artificial intelligence model is configured to detect one or more anomalies from the sensor data. The artificial intelligence model is configured to predict one or more harmful trends based on the one or more anomalies. The artificial intelligence model is configured to notify a user through an in-app chat interface with a plurality of actions to overcome the harmful trends. The processing subsystem includes an automated feeder module operatively coupled to the IoT module. The automated feeder module is configured to automate dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model based on behaviour and growth stages of a plurality of fishes. The automated feeder module is configured to optimize the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding. The automated feeder module is configured to enable the user to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface. The processing subsystem includes a cage damage detection module operatively coupled the IoT module. The cage damage detection module is configured to detect one or more structural weaknesses to identify wear and tear of the fish cage upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies. The cage damage detection module is configured to predict the one or more structural weaknesses of the fish cage. The cage damage detection module is configured to generate one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction. The processing subsystem includes a fish variety-based advisory module operatively coupled to the IoT module. The fish variety-based advisory module is configured to analyse a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity. The fish variety-based advisory module is configured to provide one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds. The fish variety-based advisory module is configured to identify one or more disease risks based on the images and the behaviour of the plurality of fishes. The fish variety-based advisory module is configured to provide a treatment to the plurality of fishes to cure the one or more disease risks. The processing subsystem includes a demand analysis module operatively coupled to the fish variety-based advisory module. The demand analysis module is configured to analyse a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply. The demand analysis module is configured to suggest a plurality of fish species for procurement based on a profitability and a least farming costs. The demand analysis module is configured to compare a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends. The demand analysis module is configured to forecast the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply. The demand analysis module is configured to identify an optimal time and one or more geolocations for the trade of the plurality of fishes. The demand analysis module is configured to align production of the plurality of fishes with the consumer demand and the real-time market trends.
[0017] In accordance with another embodiment of the present disclosure, a method for aquaculture management is provided. The method includes analysing, by an artificial intelligence model of an IoT module, sensor data received from a plurality of IoT sensors and a plurality of images received from a camera mounted in a fish cage, wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions. The method includes detecting, by the artificial intelligence model of the IoT module, one or more anomalies from the sensor data. The method includes predicting, by the artificial intelligence model of the IoT module, one or more harmful trends based on the one or more anomalies. The method includes notifying, by the artificial intelligence model of the IoT module, a user through an in-app chat interface with a plurality of actions to overcome the harmful trends. The method includes automating, by an automated dispensing module, dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model based on behaviour and growth stages of a plurality of fishes. The method includes optimizing, by an automated dispensing module, the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding. The method includes enabling, by an automated dispensing module, the user to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface. The method includes detecting, by a cage damage detection module, one or more structural weaknesses to identify wear and tear of the fish cage upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies. The method includes predicting, by the cage damage detection module, the one or more structural weaknesses of the fish cage. The method includes generating, by the cage damage detection module, one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction. The method includes analysing, by a fish variety-based advisory module, a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity. The method includes providing, by the fish variety-based advisory module, one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds. The method includes identifying, by the fish variety-based advisory module, one or more disease risks based on the images and the behaviour of the plurality of fishes. The method includes providing, by the fish variety-based advisory module, a treatment to the plurality of fishes to cure the one or more disease risks. The method includes analysing, by a demand analysis module, a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply. The method includes suggesting, by the demand analysis module, a plurality of fish species for procurement based on a profitability and a least farming costs. The method includes comparing, by the demand analysis module, a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends. The method includes forecast, by the demand analysis module, the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply. The method includes identifying, by the demand analysis module, an optimal time and one or more geolocations for the trade of the plurality of fishes. The method includes aligning, by the demand analysis module, production of the plurality of fishes with the consumer demand and the real-time market trends.
[0018] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0020] FIG. 1 is a block diagram representation of an aquaculture management system in accordance with an embodiment of the present disclosure;
[0021] FIG. 2 (a) illustrates a perspective view of a fish cage comprising a plurality of components mounted on the fish cage in accordance with an embodiment of the present disclosure;
[0022] FIG. 2 (b) illustrates a front view of the sprinkler embedded with a plurality of IoT sensors in accordance with an embodiment of the present disclosure;
[0023] FIG. 3 is a block diagram representation of an exemplary embodiment of an aquaculture management system of FIG. 1 in accordance with an embodiment of the present disclosure;
[0024] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;
[0025] FIG. 5 (a) illustrates a flow chart representing the steps involved in a method for aquaculture management in accordance with an embodiment of the present disclosure; and
[0026] FIG. 5 (b) illustrates continued steps of the method of FIG. 5 (a) in accordance with an embodiment of the present disclosure.
[0027] FIG. 5 (c) illustrates continued steps of the method of FIG. 5 (b) in accordance with an embodiment of the present disclosure.
[0028] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0029] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0030] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0032] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0033] Embodiments of the present disclosure relate to an aquaculture management system. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an IoT module comprising an artificial intelligence model. The artificial intelligence model is configured to analyze sensor data received from a plurality of IoT sensors and a plurality of images received from a camera mounted in a fish cage, wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions. The artificial intelligence model is configured to detect one or more anomalies from the sensor data. The artificial intelligence model is configured to predict one or more harmful trends based on the one or more anomalies. The artificial intelligence model is configured to notify a user through an in-app chat interface with a plurality of actions to overcome the harmful trends. The processing subsystem includes an automated feeder module operatively coupled to the IoT module. The automated feeder module is configured to automate dispensing of a predetermined quantity of a plurality of food grains feeding based on a schedule suggested by the artificial intelligence model based on behaviour and growth stages of a plurality of fishes. The automated feeder module is configured to optimize the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding. The automated feeder module is configured to enable the user to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface. The processing subsystem includes a cage damage detection module operatively coupled the IoT module. The cage damage detection module is configured to detect one or more structural weaknesses to identify wear and tear of the fish cage upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies. The cage damage detection module is configured to predict the one or more structural weaknesses of the fish cage. The cage damage detection module is configured to generate one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction. The processing subsystem includes a fish variety-based advisory module operatively coupled to the IoT module. The fish variety-based advisory module is configured to analyse a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity. The fish variety-based advisory module is configured to provide one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds. The fish variety-based advisory module is configured to identify one or more disease risks based on the images and the behaviour of the plurality of fishes. The fish variety-based advisory module is configured to provide a treatment to the plurality of fishes to cure the one or more disease risks. The processing subsystem includes a demand analysis module operatively coupled to the fish variety-based advisory module. The demand analysis module is configured to analyse a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply. The demand analysis module is configured to suggest a plurality of fish species for procurement based on a profitability and a least farming costs. The demand analysis module is configured to compare a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends. The demand analysis module is configured to forecast the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply. The demand analysis module is configured to identify an optimal time and one or more geolocations for the trade of the plurality of fishes. The demand analysis module is configured to align production of the plurality of fishes with the consumer demand and the real-time market trends.
[0034] FIG. 1 is a block diagram of an aquaculture management system (100) in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (110). In one embodiment, the server (110) may include a cloud-based server. In another embodiment, parts of the server (110) may be a local server coupled to a user (118) device. The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. In one example, the network (115) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (115) may include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, the network (115) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (115) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and/or enhanced data for global evolution (EDGE) network.
[0035] The processing subsystem (105) includes an IoT module (120) comprising an artificial intelligence model (125). The artificial intelligence model (125) is configured to analyze sensor data received from a plurality of IoT sensors (205) and a plurality of images received from a camera (210, FIG. 2 (a)) mounted in a fish cage (200, FIG. 2 (a)). The sensor data includes a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions. In one exemplary embodiment, the IoT sensors (205, FIG. 2 (a)) include, but are not limited to, dissolved oxygen sensors, temperature sensors, pH sensors, and turbidity sensors. These sensors are calibrated to continuously monitor the aquatic environment and transmit data to the processing subsystem. For example, a dissolved oxygen sensor may be configured to detect oxygen concentrations in milligrams per liter (mg/L), whereas a temperature sensor may record fluctuations in water temperature in degrees Celsius. Similarly, a pH sensor monitors the alkalinity or acidity of the water environment, and a turbidity sensor measures the relative clarity of the water by detecting the presence of suspended particulate matter.
[0036] In another embodiment, the IoT module (120) is configured to monitor the plurality of ideal water quality parameters in real-time. The plurality of ideal water quality parameters includes dissolved oxygen levels, water temperature, pH, and turbidity, which are critical for maintaining stable environmental conditions within the fish cage (200, FIG. 2 (a)). The artificial intelligence model (125) embedded within the IoT module (120) continuously processes the incoming sensor data and compares it against predefined thresholds of ideal aquatic conditions. This enables precise detection of even minor fluctuations in water quality, ensuring timely corrective measures.
[0037] For example, a non-limiting embodiment of the IoT sensors includes, but is not limited to, temperature sensors (250) for monitoring heat variations, oximeter sensors for dissolved oxygen levels, pH sensors for acidity/alkalinity balance, and turbidity sensors for water clarity.
[0038] It is noted that continuous real-time monitoring ensures a proactive approach to aquaculture management, reducing the likelihood of fish stress, disease outbreaks, or mortality due to deteriorating water conditions. It is further noted that the system provides actionable insights to farmers, allowing effective decision-making with minimal manual intervention.
[0039] In another embodiment, the IoT module (120) is further configured to maintain the plurality of ideal water quality parameters in real-time for ensuring a stable environmental condition within the fish cage (200 FIG. 2 (a)). The artificial intelligence model (125) not only monitors the parameters such as dissolved oxygen, water temperature, pH, and turbidity but also initiates corrective actions when deviations are detected. These corrective actions may include triggering the plurality of sprinklers (245, FIG. 2 (b)) to regulate oxygen and temperature, adjusting feeding schedules via the automated feeder module (130), or alerting the user (118) through the in-app interface.
[0040] For example, a non-limiting embodiment of maintaining water quality includes, but is not limited to, the activation of sprinklers to stabilize oxygen levels, adjustment of aeration devices, or controlled water exchange mechanisms.
[0041] It is noted that maintaining the ideal water quality parameters in real-time ensures continuous support for fish growth and reduces the risk of environmental stress. It is further noted that such automation minimizes manual monitoring efforts while enhancing aquaculture sustainability and productivity.
[0042] In one example, the camera includes, but is not limited to, a high-resolution waterproof camera with night vision and underwater capability, adapted to capture real-time footage under varying lighting and turbidity conditions. The camera may be integrated with image recognition software to detect changes in fish behavior, such as sluggish movement or clustering, which could correlate with anomalies in dissolved oxygen levels or pH imbalances.
[0043] The artificial intelligence model (125) is configured to detect one or more anomalies from the sensor data. The term “anomalies” herein refers to any deviation or inconsistency in the monitored water quality parameters namely dissolved oxygen, water temperature, pH, and turbidity that falls outside of a predefined optimal range necessary to sustain healthy aquatic life.
[0044] In one example, anomaly detection includes, but is not limited to, identifying sudden spikes or drops in sensor readings, detecting unusual fluctuation patterns, or recognizing gradual but concerning trends over time. For instance, an increasing turbidity over a 24-hour window, while still within permissible limits, may suggest a build-up of waste or algae bloom, which is indicative of deteriorating water quality.
[0045] In one embodiment, the artificial intelligence model (125) is configured to continuously evaluate how a particular sensor reading evolves over time. If the pH levels, for example, exhibit a slowly declining trend over several days perhaps due to rainwater dilution or feeding residue the model detects this pattern as a slow-building anomaly even though no single reading crosses a red flag threshold.
[0046] The artificial intelligence model (125) is configured to predict one or more harmful trends based on the one or more anomalies. These harmful trends may include environmental degradation, fish health risks, or potential structural damage, which if not addressed in time, can adversely impact aquaculture productivity and sustainability
[0047] In one example, harmful trend prediction includes, but is not limited to, identifying a pattern of declining dissolved oxygen levels over several hours or days, which could indicate the onset of eutrophication or algal bloom.
[0048] The artificial intelligence model (125) is configured to notify a user (118) through an in-app chat interface with a plurality of actions to overcome the harmful trends. This real-time notification system acts as an interactive assistant, proactively alerting the user to take corrective actions and mitigate the risks associated with deteriorating water conditions, fish health issues, or structural threats in the fish cage (200).
[0049] The in-app chat interface is designed to deliver contextual and actionable insights, which are dynamically generated based on the type and severity of the harmful trend identified. An example of a notification includes, but is not limited to, a message such as: “Dissolved oxygen levels are critically low. Recommended actions: 1) Activate water sprinklers; 2) Reduce feed for the next 3 hours; 3) Increase water circulation.” These recommendations are tailored based on AI analysis of real-time sensor data and historical patterns stored in the database module (155).
[0050] The processing subsystem (105) includes an automated feeder module (130) operatively coupled to the IoT module (120). The automated feeder module (130) is configured to automate dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model (125) based on behaviour and growth stages of a plurality of fishes. This dispensing operation is based on a feeding schedule generated by the artificial intelligence model (125). The artificial intelligence model (125) determines the appropriate timing and amount of feed by analyzing various parameters, including the growth stages, behavioral patterns, and physiological needs of a plurality of fishes being cultivated within the system (100).
[0051] Examples of the plurality of food grains include, but is not limited to, floating pellets, sinking pellets, micronized feed for fry, soybean meal, wheat bran, or customized protein-rich feed formulations. These may vary in type and size depending on the species and age of the fish and are selected to ensure optimal nutrition and digestion. The automated feeder module (130) is programmed to adjust feed type and dosage based on real-time data inputs, thus ensuring personalized feeding strategies for different species and life stages.
[0052] The automated feeder module (130) is configured to optimize the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding. These sensors monitor multiple environmental and biological parameters such as dissolved oxygen levels, turbidity, water temperature, and fish movement all of which serve as indicators of the fishes’ health, appetite, and environmental readiness for feeding.
[0053] An example of sensor data includes, but is not limited to, readings from oximeter sensors that detect oxygen saturation levels in the water, temperature sensors that monitor fluctuations in thermal conditions, and accelerometer or sonar-based fish behavior sensors that detect activity levels and feeding patterns of the fish. This data is processed by the artificial intelligence model (125), which dynamically recalibrates the amount of food dispensed per cycle based on the real-time nutritional needs and digestive capacity of the fish population at that moment.
[0054] The automated feeder module (130) is configured to enable the user (118) to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface. The in-app chat interface interface is operatively linked with the artificial intelligence model (125), allowing the user (118) to manually trigger the dispensing of a predetermined quantity of food grains at any time, independent of the AI-suggested feeding schedule. The in-app chat interface supports command recognition, real-time system feedback, and visual confirmation of actions executed.
[0055] An example of the in-app chat interface includes, but is not limited to, a mobile or web-based application where the user interacts with a conversational assistant or dashboard-style interface to send text-based or button-triggered commands. For instance, the user (118) may input “Dispense 200g feed now” or select a “Feed Now” button, which is then processed by the system to actuate the dispensing mechanism within the feeder module.
[0056] The processing subsystem (105) includes a cage damage detection module (135) operatively coupled to the IoT module (120). The cage damage detection module (135) is configured to detect one or more structural weaknesses to identify wear and tear of the fish cage (200, FIG. 2 (a)) upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies The damage detection process is primarily executed through the analysis of a plurality of images captured by the camera (210, FIG. 2 (a)) strategically mounted to monitor the entire cage infrastructure. The plurality of images are transmitted to the artificial intelligence model (125), which processes them using trained vision algorithms to detect visual patterns that signify structural deterioration.
[0057] The detection of one or more structural weaknesses includes, but is not limited to, identifying holes in the netting walls, loosening or detachment of the PVC pipes (225, FIG. 2 (a)), corrosion of support joints, anchor instability, and any irregular deformation or displacement of the cage elements. For example, a tear in the mesh net may appear as a high-contrast void in the image data, triggering an alert upon recognition by the image processing algorithm. Similarly, anchor issues may be identified when the camera observes abnormal movement patterns or misalignment of cage boundaries.
[0058] In one embodiment, the cage damage detection module is configured to predict the one or more structural weaknesses of the fish cage. This prediction functionality is enabled by a combination of historical image analysis, anomaly detection, and artificial intelligence (AI)-driven pattern recognition integrated into the artificial intelligence model (125). The prediction mechanism uses real-time and archived sensor data in tandem with image inputs to identify early indicators of structural stress, strain, or fatigue that could eventually evolve into significant damage.
[0059] The prediction of structural weaknesses includes, but is not limited to, forecasting potential ruptures in the netting walls, loosening of joints or pipe connections, anchor dislodgement due to water current shifts, and corrosive degradation due to prolonged environmental exposure. For example, if recurring patterns of tension or sagging are identified in the netting across successive image frames, the system may classify this as a precursor to a tear or collapse. Likewise, increased fish collisions against specific cage walls (detected via behavior sensors or motion patterns in video feed) may indicate weak spots caused by environmental stressors.
[0060] The cage damage detection module (135) is configured to generate one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction. These alerts are automatically triggered based on a dynamic threshold system defined by the artificial intelligence model (125), which continually learns from sensor inputs and image analysis to improve alert precision. The alerts act as immediate warnings or proactive maintenance signals to aquaculture operators, thus supporting early intervention and reducing the risk of structural failures.
[0061] The real-time alerts include, but are not limited to, notifications regarding detected holes in netting, early signs of pipe displacement, anchor instability, corrosion in metallic fixtures, or excessive stress from water currents. For example, if image processing detects repetitive shifts in the alignment of a corner joint over several frames, combined with abnormal vibrations picked up from motion sensors, an alert may be triggered indicating a potential anchor loosening issue. These alerts are delivered directly to the in-app chat interface, as well as through optional push notifications and email systems, ensuring the user (118) is promptly informed across multiple communication channels.
[0062] The processing subsystem (105) includes a fish variety-based advisory module (140) operatively coupled to the IoT module (120). The fish variety-based advisory module (140) to analyse a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity. These environmental parameters include water temperature, pH level, dissolved oxygen concentration, and turbidity each of which plays a critical role in maintaining the health, growth rate, and breeding efficiency of the fishes housed within the fish cage (200, FIG. 2 (a)).
[0063] The analysis process includes, but is not limited to, acquiring continuous streams of sensor data from a network of IoT sensors (205, FIG. 2 (b)) deployed within and around the fish cage. The plurality of sensors includes temperature sensors for monitoring thermal conditions, pH sensors to detect acidity or alkalinity levels, oximeter sensors for measuring dissolved oxygen levels, and turbidity sensors to assess the clarity and particulate content of the water. For example, a sharp increase in turbidity detected by a sensor may indicate sediment disturbances or plankton blooms, both of which could negatively affect certain sensitive fish species.
[0064] In one embodiment, the fish variety-based advisory module (140) is further configured to provide one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds. These recommendations are dynamically tailored to the particular species being farmed within the fish cage (200, FOIG. 2 (a)), ensuring optimal growth, survival, and productivity.
[0065] The recommendations include, but are not limited to, ideal feeding schedules, temperature ranges, dissolved oxygen levels, pH balance, and specific breeding conditions. For instance, tilapia may thrive at a water temperature between 25°C and 30°C and require higher oxygen levels during active feeding periods, while catfish may tolerate slightly murkier and warmer waters.
[0066] The fish variety-based advisory module (140) is configured identify one or more disease risks based on the images and the behaviour of the plurality of fishes. The system monitors real-time visual cues such as erratic swimming, unusual clustering, lethargy, or surface gasping, which are indicative of stress or potential illness.
[0067] The analysis includes, but is not limited to, detecting common aquaculture diseases such as fin rot, white spot (Ich), fungal infections, or parasitic infestations. For instance, if the artificial intelligence model (125) identifies pale gills or sluggish movement in tilapia based on multiple image frames and corroborates this with low dissolved oxygen levels and high turbidity readings from the sensors, it may classify the behavior as a respiratory distress condition and raise a disease risk alert.
[0068] In one embodiment, the fish variety-based advisory module (140) is configured to provide a treatment to the plurality of fishes to cure the one or more disease risks. The treatment recommendations are automatically generated by the artificial intelligence model (125) based on a comprehensive set of disease profiles, environmental conditions, and species-specific sensitivities. These recommendations are curated from a pre-stored knowledge base that includes veterinary best practices, dosage guidelines, and recovery protocols tailored for each type of disease and fish variety.
[0069] The treatment provided includes, but is not limited to, suggestions for chemical or natural remedies, quarantine procedures, modifications to water quality parameters, and adjustments to feeding protocols. For instance, if the artificial intelligence model (125) detects symptoms of fin rot in catfish such as frayed fins and abnormal isolation behavior the system may recommend antibacterial treatment, temporary isolation of affected fish, and enhanced aeration using the sprinkler system to maintain optimal oxygen levels.
[0070] The processing subsystem (105) includes a demand analysis module (145) is operatively coupled to the fish variety-based advisory module (140). The demand analysis module (145) is configured to is configured analyse a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply. The demand analysis module (145) collects and processes data streams from multiple sources, including online marketplaces, regional demand patterns, wholesale distributors, and retail points of sale. It applies statistical models and predictive analytics to detect changes in consumer preferences and shifts in buying behavior.
[0071] Additionally, the plurality of market trends includes, but is not limited to, fluctuations in fish prices across seasons, spikes in demand linked to festivals or local events, shortages in supply due to climatic disruptions, and emerging interest in specific fish breeds driven by consumer health trends. For example, during summer months, the demand for Tilapia may increase in coastal regions due to its lighter texture and faster cooking time. The module captures such insights and uses them to generate actionable suggestions for farmers.
[0072] The demand analysis module (145) is further configured to suggest a plurality of fish species for procurement based on a profitability and a least farming costs. This function utilizes a multi-criteria decision-making model that considers species-specific growth rates, feed conversion ratios, disease resistance, environmental adaptability, and market price trends. The module performs a cost-benefit analysis for each fish species and ranks them based on expected returns relative to input costs.
[0073] The profitability and farming cost considerations include, but are not limited to, factors such as the cost of fingerlings, type and quantity of feed required, susceptibility to local diseases, maintenance needs, growth cycle duration, and water quality tolerance. For example, species like Catla may be more profitable in inland regions with access to abundant freshwater, while Milkfish may be better suited for brackish water environments with lower maintenance requirements.
[0074] The demand analysis module (145) is configured to compare a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends. This comparison is performed in real-time or near real-time by leveraging internet-connected APIs, web scraping tools, and user-input datasets, depending on availability. The module correlates this pricing data with consumer demand trends to identify potential gaps, price advantages, or oversupplied niches.
[0075] The competitor pricing structure includes, but is not limited to, wholesale rates, retail prices, volume discounts, and bundled pricing strategies implemented by other aquaculture producers in the same geographic area or serving similar consumer markets. For example, if a nearby competitor offers Tilapia at a lower price point but at a lower volume and inconsistent supply, the module might suggest a pricing model that balances quality and availability to appeal to premium buyers or consistent demand segments.
[0076] The demand analysis module (145) is configured to forecast the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply. This data includes, but is not limited to, past records of consumer purchase patterns, fluctuations in seasonal demand, historical pricing at both wholesale and retail levels, and supply volumes reported from regional and national aquaculture markets. The system utilizes machine learning models trained on time-series data to anticipate future market behavior with a high degree of accuracy.
[0077] The historical sales data includes, but is not limited to, datasets such as prior transactions between farmers and distributors, e-commerce and physical store sales logs, previous years’ consumption trends across different geographies, and government or third-party reports on aquaculture production volumes and fish trade patterns.
[0078] The demand analysis module (145) is configured to identify an optimal time and one or more geolocations for the trade of the plurality of fishes. These datasets include fish species demand fluctuations, regional consumer preferences, seasonal variations, logistics cost trends, regional pricing indices, and fish festival or cultural consumption cycles. By correlating these factors demand analysis module (145) determines the most favorable market windows and trading hubs to maximize profitability and minimize overhead costs such as transportation and storage.
[0079] The identification of geolocations includes, but is not limited to, district-level markets, state-level hubs, coastal auction points, international seaports, and online seafood trade zones.
[0080] In one embodiment, the demand analysis module (145) is configured to align production of the plurality of fishes with the consumer demand and the real-time market trends. This involves analyzing patterns from historical demand cycles, current pricing fluctuations, stocking densities, environmental conditions, and growth rates of different fish species. The system then provides actionable recommendations to aquaculture farmers regarding which species to prioritize, scale, delay, or phase out in production to meet the anticipated market requirements.
[0081] For example, the alignment of fish production includes, but is not limited to, increasing the breeding of species such as Rohu during monsoon months in areas with rising market demand, or adjusting the harvesting schedule of Tilapia to align with export timelines identified through international market analytics.
[0082] Consider a non-limiting scenario at a large-scale inland aquaculture facility located near the backwaters of a place “X”, where multiple floating fish cages (200) are used to culture high-value species such as sea bass and tilapia. Early one morning, the farm operator receives a notification from the IoT module (120) via the in-app chat interface used by the user (118). The notification is triggered after the artificial intelligence model (125) detects a steady drop in dissolved oxygen levels and rising water temperature, as captured by the oximeter sensors (255) and temperature sensors (250) integrated into the IoT sensors (205), which are embedded within the sprinklers (245). The sensor data and accompanying images from the camera (210) inside the fish cage suggest the fish are clustering near the surface an early sign of environmental stress.
[0083] The artificial intelligence model (125) instantly analyses the situation, identifies it as a potentially harmful trend, and predicts a risk of hypoxia due to stagnating water. The system (100) notifies the farm manager and suggests activating water aeration systems and temporarily reducing feed. Simultaneously, the automated feeder module (130), which was scheduled to dispense feed shortly, postpones the feeding process and recalibrates the quantity of food grains based on the current stress behavior and historical feeding patterns. The manager, via the in-app chat interface, reviews the insights and remotely approves the revised feeding action, ensuring that no excess food is wasted and fish health is not compromised.
[0084] Meanwhile, the cage damage detection module (135) processes the latest image data and flags a loose net segment on one of the cage corners, likely caused by high tide the previous night. The system (100) predicts this could become a structural weakness and alerts the maintenance team to inspect and repair the area before fish can escape. Concurrently, the fish variety-based advisory module (140) correlates the environmental data with breed-specific health thresholds and confirms the current water conditions are suboptimal for tilapia but within tolerable limits for sea bass, prompting the system to prioritize tilapia cages for oxygenation and care.
[0085] To optimize planning, the demand analysis module (145) fetches and processes real-time market data and identifies an upcoming seasonal spike in demand for tilapia due to an approaching festival. It advises the farm to delay harvest for another week to meet peak pricing, thereby maximizing profitability. All historical trends, sensor values, advisory decisions, and feeding logs are securely saved in the database module (155), building a robust dataset for future AI training and farm analytics.
[0086] Powered by solar panels (240) mounted on each cage, the system continues to function without interruption, even in this remote location. The PVC pipes (225) provide structural durability against waves and support the netting walls (235), allowing optimal water flow to maintain a healthy aquatic environment.
[0087] In one embodiment, the various functional components of the system (100) may reside on a single computer, or they may be distributed across several computers in various arrangements. The various components of the system may, furthermore, access one or more databases, and each of the various components of the system may be in communication with one another. Further, while the components of FIG. 1 are discussed in the singular sense, it will be appreciated that in other embodiments multiple instances of the components may be employed.
[0088] FIG. 2 (a) illustrates a perspective view of a fish cage comprising a plurality of components mounted on the fish cage in accordance with an embodiment of the present disclosure;
[0089] In one embodiment, the fish cage (200) of the aquaculture management system comprises a plurality of PVC pipes (225) arranged in a direction adapted to provide structural durability to the entire cage assembly. The structural design of the PVC pipes is configured to withstand aquatic pressures, water currents, and environmental factors such as wind or wave action. These pipes form the primary skeletal framework of the cage, enabling it to maintain shape, resist deformation, and provide a reliable base for the integration of netting, sensors, sprinklers, and other modules.
[0090] For example, the use of PVC pipes (225) includes, but is not limited to, high-density polyvinyl chloride (HD-PVC) tubes arranged in a rectangular or cylindrical configuration. The arrangement may be horizontal around the perimeter to define the boundary of the cage, with vertical and diagonal members installed for reinforcement.
[0091] In one embodiment, the aquaculture management system comprises a dispensing unit (260) mounted directly on the fish cage (200) and configured to dispense a plurality of food grains to the aquatic environment. The dispensing unit (260) is operatively linked to the automated feeder module and may be electronically or mechanically controlled based on feeding schedules determined by the artificial intelligence model. The positioning of the dispensing unit (260) on the cage allows for uniform distribution of feed across the area occupied by the fish population, ensuring optimal reach and minimizing feed clustering. For example, the dispensing unit (260) includes, but is not limited to, an automated rotary or vibratory dispenser with a hopper to store food grains and a motorized mechanism to control release.
[0092] In one embodiment, the aquaculture management system comprises a fish cage (200) that includes a plurality of netting walls (235) structurally coupled to a framework formed by a plurality of PVC pipes (225). These netting walls (235) serve as permeable boundaries that allow continuous water flow into and out of the fish cage (200), thereby facilitating a natural aquatic environment within the cage structure. The configuration is adapted to maintain optimal water exchange, allowing fresh water, nutrients, and dissolved oxygen to flow freely while enabling waste and uneaten feed to be carried away by the surrounding currents.
[0093] For example, the netting walls (235) include, but are not limited to, polymeric or nylon mesh materials that are corrosion-resistant, UV-stabilized, and treated with anti-fouling coatings to prevent biofouling over time.
[0094] In one embodiment, the aquaculture management system comprises one or more solar panels (240) strategically mounted on the fish cage (200), which are adapted to generate renewable electrical energy to power essential electronic components, including a plurality of IoT sensors (205) and at least one camera (210). These solar panels (240) are positioned to maximize exposure to sunlight and are integrated into the design of the cage in a manner that does not obstruct fish movement or water circulation.
[0095] FIG. 2 (b) illustrates a front view of the sprinkler embedded with a plurality of IoT sensors in accordance with an embodiment of the present disclosure;
[0096] In another embodiment, the aquaculture management system comprises a plurality of sprinklers (245) integrated with a corresponding plurality of IoT sensors (205), strategically embedded within the fish cage (200) to monitor and regulate the aquatic environment in real time. The plurality of sprinklers (245) serve a dual function: environmental regulation and sensor housing. By embedding IoT sensors directly within the sprinklers, the system ensures seamless synchronization between sensing operations and immediate environmental responses.
[0097] The plurality of IoT sensors (205) embedded within the sprinklers (245) includes, but is not limited to, a plurality of temperature sensors (250) and oximeter sensors (255). For example, temperature sensors (250) may include thermocouples, resistance temperature detectors (RTDs), or digital thermal sensors capable of withstanding constant submersion in aquaculture environments.
[0098] Likewise, the oximeter sensors (255) are adapted to monitor the dissolved oxygen levels in the water. Examples of oximeter sensors (255) include, but are not limited to, optical dissolved oxygen sensors or electrochemical probes, each capable of transmitting real-time oxygen data to the IoT module (120).
[0099] In another embodiment, the plurality of sprinklers (245) is adapted to detect a rise or drop in the water temperature and the dissolved oxygen level beyond a plurality of ideal water quality parameters. The sprinklers (245) are configured to detect a rise or drop in the water temperature and the dissolved oxygen level beyond a plurality of ideal water quality parameters essential for the maintenance of a stable aquatic environment. The plurality of sprinklers (245) is further embedded with the IoT sensors (205), including temperature sensors (250) and oximeter sensors (255), thereby enabling real-time monitoring of fluctuations. Upon detection of deviations from the ideal range, the plurality of sprinklers (245) is adapted to automatically trigger water sprinkling to restore and stabilize the water temperature and dissolved oxygen level, thereby ensuring optimum conditions for fish growth and survival.
[0100] An example of the sprinklers includes, but is not limited to, a micro-sprinkler arrangement adapted to evenly disperse water across the fish cage (200) to minimize localized thermal fluctuations. Another example includes, but is not limited to, the plurality of sprinklers (245) integrated with oxygen diffusers that not only sprinkle water but also aerate the aquatic environment to counteract sudden drops in dissolved oxygen levels.
[0101] In another embodiment, upon detection of one or more fluctuations, the plurality of sprinklers (245) automatically triggers water sprinkling to stabilize the plurality of ideal water quality parameters corresponding to the stable environmental conditions. The plurality of sprinklers (245), in communication with the IoT sensors (205), receive real-time input regarding deviations in the water temperature and dissolved oxygen level. Based on this input, the sprinklers (245) initiate corrective measures without requiring manual intervention, thereby restoring balance in the aquatic environment.
[0102] For example, a non-limiting embodiment of the sprinklers (245) includes, but is not limited to, an automated control system that activates the sprinkling action upon sensing an oxygen drop below 5 mg/L or a temperature rise above 30°C. Another example includes, but is not limited to, sprinklers (245) programmed with thresholds unique to specific fish species, thereby ensuring species-specific stabilization of water quality.
[0103] In another embodiment, the plurality of sprinklers (245) is powered by the one or more solar panels (240, FIG. 2(a)). The one or more solar panels (240, FIG. 2(a)) are configured to harness renewable solar energy and convert it into electrical power required for operating the plurality of sprinklers (245) along with the associated IoT sensors (205). This arrangement ensures uninterrupted functionality of the plurality of sprinklers (245), even in remote aquaculture environments where conventional power sources may not be readily available.
[0104] For example, the solar panels includes, but is not limited to, photovoltaic modules integrated on the top of the fish cage (200), wherein the generated power is stored in rechargeable batteries. Another example includes, but is not limited to, hybrid solar systems configured to power both the plurality of sprinklers (245) and other auxiliary modules, ensuring round-the-clock operation.
[0105] FIG. 3 is a block diagram representation of an exemplary embodiment of an aquaculture management system (100) in accordance with an embodiment of the present disclosure. The system (100) has the following modules: an IoT module (120), an artificial intelligence model (125), an automated feeder module (130), a cage damage detection (135), a fish variety-based advisory module (140) and a demand analysis module (145). Further, the system (100) includes a database module (155) operatively coupled to the fish variety-based advisory module (140). The database module (155) is configured to store the sensor data, the plurality environmental parameters, the dietary requirement, and the breeding guideline pertaining to the plurality of fish breeds. The database module (155) is also operatively linked to the IoT module (120) and the artificial intelligence model (125), serving as the central repository for sensor data, environmental parameters, fish dietary requirements, and breeding guidelines. This structured data storage enables historical data tracking, predictive analytics, and personalized fish management strategies.
[0106] The sensor data stored includes, but is not limited to, time-stamped readings from the plurality of IoT sensors (205) such as dissolved oxygen levels, water temperature, pH, and turbidity. For example, water temperature data captured by temperature sensors (250) and dissolved oxygen readings from oximeter sensors (255) are logged in real time. This allows the artificial intelligence model (125) to detect trends, seasonal variations, or sudden anomalies that might indicate system faults or environmental threat.
[0107] FIG. 4 is a block diagram of a computer or a server (110) in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (330), and memory (310) operatively coupled to the bus (320). The processor(s) (330), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0108] The memory (310) includes several subsystems stored in the form of executable program which instructs the processor (330) to perform the method steps illustrated in FIG. 1. The memory (310) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: an IoT module (120), an artificial intelligence model (125), an automated feeder module (130), a cage damage detection module (135), a fish variety-based advisory module (140) and a demand analysis module (145).
[0109] In accordance with an embodiment of the present disclosure, an aquaculture management system (100). The system (100) includes a processing subsystem (105) hosted on a server (110). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. The processing subsystem (105) includes an IoT module (120) comprising an artificial intelligence model (125). The artificial intelligence model (125) is configured to analyze sensor data received from a plurality of IoT sensors (205) and a plurality of images received from a camera (210) mounted in a fish cage (200), wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions. The artificial intelligence model (125) is configured to detect one or more anomalies from the sensor data. The artificial intelligence model (125) is configured to predict one or more harmful trends based on the one or more anomalies. The artificial intelligence model (125) is configured to notify a user (118) through an in-app chat interface with a plurality of actions to overcome the harmful trends. The processing subsystem (105) includes an automated feeder module (130) operatively coupled to the IoT module (120). The automated feeder module (130) is configured to automate dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model (125) based on behaviour and growth stages of a plurality of fishes. The automated feeder module (130) is configured to optimize the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding. The automated feeder module (130) is configured to enable the user (118) to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface. The processing subsystem (105) includes a cage damage detection module (135) operatively coupled the IoT module (120). The cage damage detection module (135) is configured to detect one or more structural weaknesses to identify wear and tear of the fish cage (200) upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies. The cage damage detection module (135) is configured predict the one or more structural weaknesses of the fish cage. The cage damage detection module is configured to generate one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction. The processing subsystem (105) includes a fish variety-based advisory module (140) operatively coupled to the IoT module. The fish variety-based advisory module (140) is configured to analyse a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity. The fish variety-based advisory module (140) is configured to provide one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds. The fish variety-based advisory module (140) is configured to identify one or more disease risks based on the images and the behaviour of the plurality of fishes. The fish variety-based advisory module (140) is configured to provide a treatment to the plurality of fishes to cure the one or more disease risks. The processing subsystem (105) includes a demand analysis module (145) operatively coupled to the fish variety-based advisory module. The demand analysis module (145) is configured to analyse a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply. The demand analysis module (145) is configured to suggest a plurality of fish species for procurement based on a profitability and a least farming costs. The demand analysis module (145) is configured to compare a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends. The demand analysis module (145) is configured to forecast the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply. The demand analysis module (145) is configured to identify an optimal time and one or more geolocations for the trade of the plurality of fishes. The demand analysis module (145) is configured to align production of the plurality of fishes with the consumer demand and the real-time market trends.
[0110] The bus (320) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (320) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (320) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.
[0111] FIG. 5 (a) illustrates a flow chart representing the steps involved in a method (500) for aquaculture management in accordance with an embodiment of the present disclosure. FIG. 5 (b) illustrates continued steps of the method (500) of FIG. 5 (a) in accordance with an embodiment of the present disclosure. FIG. 5 (c) illustrates continued steps of the method (500) of FIG. 5 (b) in accordance with an embodiment of the present disclosure. The method (500) includes analysing, by an artificial intelligence model of an IoT module, sensor data received from a plurality of IoT sensors and a plurality of images received from a camera mounted in a fish cage, wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions in step 505. This enables intelligent, real-time, and data-driven decision-making throughout the aquaculture process. The artificial Intelligence (125) serves as the central processing unit for interpreting multi-source data and deriving actionable insights from it.
[0112] The sensor data analyzed includes, but is not limited to, readings from IoT sensors that monitor dissolved oxygen levels, water temperature, pH, and turbidity, which are critical environmental parameters for aquaculture stability. For example, the dissolved oxygen levels may be recorded via oximeter sensors, while water temperature is captured using temperature sensors. These readings are transmitted in real time to the AI model, which cleans, aggregates, and prepares the data for further inference.
[0113] The method (500) includes detecting, by the artificial intelligence model of the IoT module, one or more anomalies from the sensor data in step 510. These anomalies refer to deviations or irregularities from the predefined optimal thresholds of environmental parameters that are crucial to fish health and sustainable aquaculture practices.
[0114] The artificial intelligence model employs advanced anomaly detection techniques such as threshold-based alerts, statistical outlier detection, and machine learning models including isolation forests or clustering algorithms (e.g., k-means) to identify abnormal data points. For example, if the dissolved oxygen level suddenly drops below a safe limit, or if the pH value deviates sharply from the expected range, the system flags these as anomalies. Similarly, abrupt changes in turbidity could indicate the presence of pollutants or overfeeding waste, while unusual spikes in temperature could suggest equipment malfunction or adverse weather effects.
[0115] An example of anomaly detection includes identifying when a temperature sensor (250) detects a sudden rise from 26°C to 34°C within an hour, which falls outside the fish’s thermal tolerance range. Likewise, if oximeter sensors (255) report oxygen levels below 4 mg/L, the AI system recognizes it as a critical anomaly that may lead to fish stress or mortality.
[0116] The method (500) includes predicting, by the artificial intelligence model of the IoT module, one or more harmful trends based on the one or more anomalies in step 515. This prediction capability enables the system to transition from reactive to proactive aquaculture management by forecasting possible risks and outcomes before they escalate.
[0117] An example of harmful trend prediction includes identifying a rising trend in water temperature over several days, surpassing the ideal range for a specific fish variety. This temperature trend, if left unaddressed, could lead to increased fish metabolism, stress, or disease susceptibility.
[0118] The method (500) includes notifying, by the artificial intelligence model of the IoT module, a user through an in-app chat interface with a plurality of actions to overcome the harmful trends in step 520. The user interface is integrated within the aquaculture management platform and allows for real-time interaction and responsive guidance, ensuring that the farmer is made aware of critical situations and can take informed actions.
[0119] An example of in-app chat notification includes a message such as: “Warning: Dissolved oxygen levels are predicted to fall below optimal range in the next 4 hours. Recommended actions: 1) Activate sprinklers; 2) Delay next feed cycle; 3) Check aeration system.” This notification may also include clickable buttons or toggles that allow the user to execute the recommended actions directly through the chat interface.
[0120] The method (500) includes automating, by an automated dispensing module, dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model based on behaviour and growth stages of a plurality of fishes in step 525. The automated dispensing module is operatively integrated with the AI-driven IoT module to intelligently manage feed distribution within the aquaculture system. The system is configured to automate the release of a predetermined quantity of food grains, such as pelleted fish feed or floating micro-particles, at optimal intervals throughout the day. This schedule is not static but is dynamically generated by the artificial intelligence model based on behavioral insights and the biological growth stage of the fish stock.
[0121] An example of a feeding routine includes, but is not limited to, the scheduled release of 250 grams of high-protein pellets at 9 AM, 1 PM, and 5 PM for juvenile tilapia, where each dose is timed to coincide with peak feeding behavior observed through movement analytics. The dispensing module may comprise a mechanical auger or vibration-based release system enclosed in a waterproof housing, mounted above the fish cage or at its periphery.
[0122] The method (500) includes optimizing, by an automated dispensing module, the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding in step 530. The automated dispensing module functions not only as a mechanical delivery system but also as an intelligent decision-making unit that dynamically adjusts the quantity of food grains dispensed. This optimization process is guided by real-time sensor data, which includes environmental variables such as water temperature, dissolved oxygen levels, pH, and turbidity each of which directly impacts the fish’s metabolic rate and feeding behavior
[0123] An example of optimization includes, but is not limited to, a scenario where the water temperature drops from 28°C to 22°C, triggering the AI model to reduce the default feeding amount for tilapia from 300 grams to 180 grams per cycle.
[0124] The method (500) includes enabling, by an automated dispensing module, the user to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface in step 535. The automated dispensing module is configured to interface with a user-friendly in-app chat interface that allows remote manual control of feeding operations. While the system is capable of autonomous feeding based on artificial intelligence-driven predictions, this feature provides users with the flexibility to override or supplement automated decisions based on subjective observations or changing conditions not yet reflected in the sensor data.
[0125] An example of this functionality includes, but is not limited to, a farmer using a mobile app to remotely feed a specific cage after observing, through live camera feeds, that fish are actively swimming near the surface a behavior often associated with hunger. The user types a simple command such as “Dispense 150 grams” into the chat interface, which is parsed and executed by the system’s control logic, triggering the dispensing unit located above the respective cage to deliver the food accordingly.
[0126] The method (500) includes detecting, by a cage damage detection module, one or more structural weaknesses to identify wear and tear of the fish cage upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies in step 540. The cage damage detection module is operatively coupled to both the IoT module and a camera system installed around or within the fish cage. The camera system captures periodic or event-driven images of the fish cage structure, including its joints, netting, floaters, and anchor lines. These images are transmitted to the artificial intelligence model within the IoT module for automated visual analysis. This analysis uses computer vision techniques, including pattern recognition, edge detection, and anomaly detection algorithms to examine physical changes or irregularities in the cage.
[0127] An example of the cage damage detection includes, but is not limited to, identifying a frayed section of the netting wall located near the bottom of the cage. The camera mounted on the frame captures images periodically. Once a fraying pattern is detected in sequential frames, the system flags it as an anomaly.
[0128] The method (500) includes predicting, by the cage damage detection module, the one or more structural weaknesses of the fish cage in step 545. The cage damage detection module not only identifies existing physical anomalies but also leverages predictive analytics to forecast potential structural weaknesses of the fish cage before they manifest. This is accomplished through continuous analysis of historical and real-time image data, combined with sensor data that tracks environmental conditions such as water current speed, wave height, and external impact pressure.
[0129] An example of the predictive capability includes, but is not limited to, the identification of an anchor point that is loosening over time. Sensor readings show a gradual decline in tension force, while image analysis reflects widening gaps at the fastener. The AI module forecasts that this anchor point may fully detach within the next 72 hours under current weather conditions. Consequently, the system alerts the farm operator and recommends preemptive anchoring or reinforcement to avoid catastrophic failure.
[0130] The method (500) includes generating, by the cage damage detection module, one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction in step 550. The cage damage detection module is further configured to generate real-time alerts that notify users of both current and predicted structural weaknesses in the fish cage. These alerts are based on the identification of immediate damage such as holes in the netting or loose anchors and the prediction of future structural failures, as determined by the AI model’s analysis of sensor data and image inputs. The alerts are automatically transmitted to the user interface via in-app notifications, emails, or SMS depending on user preference, and are time-stamped with the specific issue and location on the cage structure.
[0131] An example of a real-time alert includes, but is not limited to, a message stating: “Warning: Anchor point A3 tension force reduced by 27% over 48 hours. Risk of dislodgement in 36 hours based on current wave activity. Recommend reinforcement or replacement.
[0132] The method (500) includes analysing, by a fish variety-based advisory module, a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity in step 555. The fish variety-based advisory module is designed to continuously monitor and analyze key environmental parameters within the aquaculture environment. These parameters include water temperature, pH levels, dissolved oxygen concentration, and turbidity each of which plays a critical role in determining the health, growth, and well-being of various fish species. The module uses real-time sensor data transmitted from the IoT sensors deployed in the fish cage to assess whether the current aquatic environment aligns with the optimal habitat conditions required for each specific fish variety being cultivated.
[0133] An example of this module’s operation includes, but is not limited to, detecting a drop in dissolved oxygen below 5 mg/L, which is suboptimal for species like trout. The system recognizes this condition as unsuitable and immediately flags it, correlating the data with the current water temperature and turbidity to suggest whether aeration or a change in water flow is needed.
[0134] The method (500) includes providing, by the fish variety-based advisory module, one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds in step 560. The fish variety-based advisory module is configured to generate species-specific recommendations by processing the analyzed environmental parameters in conjunction with a set of dietary requirements and breeding guidelines specific to each fish variety. These recommendations aim to maintain optimal living conditions, feeding schedules, and breeding cycles, enhancing fish health and maximizing aquaculture productivity. The method cross-references real-time data with predefined biological profiles and behavior models for various fish species, including but not limited to tilapia, catfish, and carp.
[0135] An example of this includes, but is not limited to, recommending a dietary change for tilapia when water temperatures rise above 30°C. Based on the sensor data, the module may suggest higher protein feed to maintain metabolism. For catfish, the module may identify favorable breeding conditions when water pH is stable between 6.5 and 8.0, and the oxygen level exceeds 5 mg/L, prompting the system to alert the farmer to prepare for spawning.
[0136] The method (500) includes identifying, by the fish variety-based advisory module, one or more disease risks based on the images and the behaviour of the plurality of fishes in step 565. The fish variety-based advisory module uses artificial intelligence to detect disease risks by analyzing visual patterns captured through cameras and behavioral anomalies detected through sensor inputs. The module is trained on a database of image datasets and behavioral signatures that correspond to specific diseases, allowing it to identify symptoms such as discoloration, erratic swimming, lethargy, fin rot, or unusual surface behavior. This proactive identification aids in early intervention before the disease can spread or cause significant loss.
[0137] An example includes, but is not limited to, identifying bacterial infections such as columnaris in catfish by detecting characteristic white patches on the head or fins through the camera feed. In another case, if tilapia shows reduced movement and congregate near oxygen sources, the system may associate this behavior with gill-related infections or low dissolved oxygen tolerance due to parasitic infestation.
[0138] The method (500) includes providing, by the fish variety-based advisory module, a treatment to the plurality of fishes to cure the one or more disease risks in step 570. Once the fish variety-based advisory module identifies the disease risks through image and behavior analysis, it proceeds to recommend or automatically provide appropriate treatment protocols. These treatments are species-specific and depend on the type and stage of the disease detected. The module accesses an integrated treatment database that includes pharmaceutical, probiotic, or environmental correction strategies aligned with regulatory guidelines for aquaculture health management.
[0139] An example includes, but is not limited to, recommending the administration of oxytetracycline in a specific dosage for bacterial gill disease in salmon, along with instructions for application via medicated feed over a defined duration.
[0140] The method (500) includes analysing, by a demand analysis module, a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply in step 575. The demand analysis module is designed to interpret and process dynamic market data to assist aquaculture farmers in making informed business decisions. By using real-time data feeds and integrated historical datasets, the module assesses consumer demand trends, seasonal patterns, prevailing price points, and regional supply metrics to present a comprehensive market outlook. This enables the system to identify market fluctuations that could affect sales or production volumes.
[0141] An example includes, but is not limited to, the module detecting an increase in consumer demand for tilapia in coastal markets during the summer season due to festive consumption habits. It simultaneously analyzes supply shortages in nearby regions, thereby indicating a favorable selling opportunity. The module may also detect a price hike trend in catfish during specific months aligned with local religious festivals or export schedules, guiding the farmer to ramp up production accordingly.
[0142] The method (500) includes suggesting, by the demand analysis module, a plurality of fish species for procurement based on a profitability and a least farming costs in step 580. The demand analysis module intelligently evaluates multiple factors including operational costs, historical pricing, market trends, and current supply-demand ratios to recommend optimal fish species for procurement. These suggestions are tailored to enhance the farmer’s profitability while keeping farming costs at a minimum. The module uses AI models to cross-reference profitability metrics with environmental adaptability, disease resistance, and feeding efficiency of various species.
[0143] An example includes, but is not limited to, recommending pangasius for procurement due to their low feed conversion ratio (FCR) and high survival rates in the farmer’s specific water conditions. At the same time, the system may avoid suggesting high-cost or low-yield species such as barramundi if current market conditions indicate poor profit margins or elevated farming risks.
[0144] The method (500) includes compare, by the demand analysis module, a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends in step 585. The demand analysis module is equipped with artificial intelligence algorithms that scrape, aggregate, and analyze pricing structures from various competitor aquaculture producers. These insights are then juxtaposed against current consumer demand, helping the system identify under- or over-priced species, pricing gaps in the market, and potential strategic opportunities for price optimization.
[0145] An example includes, but is not limited to, the module detecting that tilapia is being sold at significantly lower prices by competitors in a nearby region, despite a rising demand for tilapia in the user’s local market. The system may then suggest adjusting the farmer’s tilapia price slightly below average to gain competitive advantage while maintaining profitability.
[0146] The method (500) includes forecasting, by the demand analysis module, the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply in step 590. forecasting, by the demand analysis module, the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply volumes.
[0147] An example includes, but is not limited to, the module forecasting a spike in consumer demand and pricing for catfish in a particular region during the pre-monsoon season, based on patterns from the past five years. The system may alert the user to prepare larger volumes of catfish during the upcoming cycle to meet the forecasted surge, maximizing revenue opportunities.
[0148] The method (500) includes identifying, by the demand analysis module, an optimal time and one or more geolocations for the trade of the plurality of fishes in step 595. The demand analysis module is configured to analyze geospatial data, sales performance trends, logistical routes, and regional consumer preferences to identify the most advantageous time and location for trading specific varieties of fish. This allows aquaculture farmers to align their harvest and distribution strategies with market demand peaks, minimizing holding costs and maximizing profitability.
[0149] An example includes, but is not limited to, identifying that tilapia yields higher returns when traded in coastal city markets during early summer months due to local festival demand and favorable weather conditions for transport.
[0150] The method (500) includes aligning, by the demand analysis module, production of the plurality of fishes with the consumer demand and the real-time market trends in step 599. The demand analysis module continuously correlates production cycles with current and forecasted market demands. It evaluates factors such as fish growth rate, optimal harvest windows, market saturation levels, and pricing trends to provide dynamic guidance for scaling up or down the production of specific fish varieties. This helps aquaculture operators maintain a steady supply chain aligned with profitable market opportunities.
[0151] An example includes, but is not limited to, recommending an increase in the production of catfish in the second quarter of the year based on market trend data indicating rising consumer demand during that period. Conversely, it may suggest reducing the output of carp in regions where recent pricing trends and competitor supply indicate potential oversaturation.
[0152] It is noted that the alignment of fish production with demand trends not only ensures better profitability but also reduces wastage, improves resource allocation, and minimizes ecological impact. The artificial intelligence model considers regional market volatility, seasonal fish breeding patterns, feed availability, and environmental sustainability to make balanced production recommendations in real-time.
[0153] Various embodiments of the aquaculture management system and the method thereof as described above provide numerous advantages. The IoT module (120) integrated with the artificial intelligence model (125) enables real-time monitoring and analysis of crucial environmental parameters, thereby ensuring proactive anomaly detection and trend prediction. The automated feeder module (130) improves feeding efficiency by dispensing food grains based on fish behavior and growth stage, minimizing waste and enhancing nutritional balance. Structural integrity is maintained using the cage damage detection module (135), which identifies potential issues like loose nets or anchor problems before they escalate. The fish variety-based advisory module (140) provides tailored guidance on ideal habitat conditions, feeding routines, and disease prevention for various fish breeds, thus optimizing yield quality. Furthermore, the demand analysis module (145) empowers farmers with real-time market insights, seasonal trends, and profitability forecasts to align production with consumer demands. These intelligent, interconnected modules collectively enhance operational efficiency, sustainability, and profitability in aquaculture management.
[0154] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing subsystem” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure.
[0155] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.
[0156] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0157] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0158] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
, C , Claims:WE CLAIM:
1. An aquaculture management system (100), comprising:
a processing subsystem (105) hosted on a server (110) and configured to execute on a network (115) to control bidirectional communications among a plurality of modules, wherein the plurality of modules comprising:
characterized in that,
an IoT module (120) comprising an artificial intelligence model (125) configured to:
analyze sensor data received from a plurality of IoT sensors (205) and a plurality of images received from a camera (210) mounted in a fish cage (200), wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions;
detect one or more anomalies from the sensor data;
predict one or more harmful trends based on the one or more anomalies;
notify a user (118) through an in-app chat interface with a plurality of actions to overcome the harmful trends;
an automated feeder module (130) operatively coupled to the IoT module (120), wherein the automated feeder (130) is configured to:
automate dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model (125) based on behaviour and growth stages of a plurality of fishes;
optimize the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding;
enable the user (118) to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface;
a cage damage detection module (135) operatively coupled the IoT module (120), wherein the cage damage detection module (135) is configured to:
detect one or more structural weaknesses to identify wear and tear of the fish cage (200) upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies;
predict the one or more structural weaknesses of the fish cage;
generate one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction;
a fish variety-based advisory module (140) operatively coupled to the IoT module (120), wherein the fish variety based advisory module (140) is configured to:
analyse a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity;
provide one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds;
identify one or more disease risks based on the images and the behaviour of the plurality of fishes;
provide a treatment to the plurality of fishes to cure the one or more disease risks;
a demand analysis module (145) operatively coupled to the fish variety-based advisory module (140), wherein the demand analysis module (145) is configured to:
analyse a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply;
suggest a plurality of fish species for procurement based on a profitability and a least farming costs;
compare a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends;
forecast the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply;
identify an optimal time and one or more geolocations for the trade of the plurality of fishes; and
align production of the plurality of fishes with the consumer demand and the real-time market trends.
2. The aquaculture management system (100) as claimed in claim 1, wherein the fish cage (200) comprises a plurality of PVC pipes (225) arranged in a direction adapted to provide structural durability.

3. The aquaculture management system (100) as claimed in claim 1, wherein a dispensing unit (260) is mounted on the fish cage (200) to dispense the plurality of food grains.

4. The aquaculture management system (100) as claimed in claim 1, wherein the fish cage (200) comprises a plurality of netting walls (235) coupled to the plurality of PVC pipes (225),
wherein the plurality of netting walls (235) are adapted to facilitate a water flow into the fish cage (200).

5. The aquaculture management system (100) as claimed in claim 1, comprises one or more solar panels (240) mounted on the fish cage (200) adapted to power the plurality of IoT sensors (205) and the camera (210).

6. The aquaculture management system (100) as claimed in claim 1, comprises a plurality of sprinklers (245) embedded with the plurality of IoT sensors (205),
wherein the plurality of IoT sensors (205) comprises a plurality of temperature sensors (250) adapted to monitor the water temperature and a plurality of oximeter sensors (255) adapted to monitor the dissolved oxygen level.

7. The aquaculture management system (100) as claimed in claim 6, wherein the plurality of sprinklers (245) is adapted to detect a rise or drop in the water temperature and the dissolved oxygen level beyond a plurality of ideal water quality parameters,
wherein upon detection of one or more fluctuations, the plurality of sprinklers (245) automatically triggers water sprinkling to stabilize the plurality of ideal water quality parameters corresponding to the stable environmental conditions.

8. The aquaculture management system (100) as claimed in claim 6, wherein the plurality of sprinklers (245) is powered by the one or more solar panels (240).

9. The aquaculture management system (100) as claimed in claim 1, wherein the IoT module is configured to:
monitor the plurality of ideal water quality parameters in real-time; and
maintain the plurality of ideal water quality parameters in real-time for the stable environmental condition.

10. The aquaculture management system (100) as claimed in claim 1, further comprises a database module (155) is operatively coupled to the fish variety-based advisory module (140), wherein the database module (155) is configured to store the sensor data, the plurality environmental parameters, the dietary requirement, and the breeding guideline pertaining to the plurality of fish breeds.

11. A method (500) for aquaculture management, comprising:
characterized in that,
analysing, by an artificial intelligence model of an IoT module, sensor data received from a plurality of IoT sensors and a plurality of images received from a camera mounted in a fish cage, wherein the sensor data comprises a dissolved oxygen level, a water temperature, a pH, and a turbidity of a stable environmental conditions; (505)
detecting, by the artificial intelligence model of the IoT module, one or more anomalies from the sensor data; (510)
predicting, by the artificial intelligence model of the IoT module, one or more harmful trends based on the one or more anomalies; (515)
notifying, by the artificial intelligence model of the IoT module, a user through an in-app chat interface with a plurality of actions to overcome the harmful trends; (520)
automating, by an automated dispensing module, dispensing of a predetermined quantity of a plurality of food grains for feeding based on a schedule suggested by the artificial intelligence model based on behaviour and growth stages of a plurality of fishes; (525)
optimizing, by an automated dispensing module, the predetermined quantity of the plurality of food grains based on the sensor data to prevent excess feeding; (530)
enabling, by an automated dispensing module, the user to dispense the predetermined quantity of the plurality of food grains through the in-app chat interface; (535)
detecting, by a cage damage detection module, one or more structural weaknesses to identify wear and tear of the fish cage upon completion of analysis of the plurality of images wherein the one or more structural weaknesses are holes, loose nets, and anchor issues caused by the one or more anomalies; (540)
predicting, by the cage damage detection module, the one or more structural weaknesses of the fish cage; (545)
generating, by the cage damage detection module, one or more real-time alerts corresponding to a preventive maintenance and a repair requirement based on the identification and prediction; (550)
analysing, by a fish variety-based advisory module, a plurality of environmental parameters comprising the water temperature, the pH, the dissolved oxygen, and the turbidity; (555)
providing, by the fish variety-based advisory module, one or more species-specific recommendations based on the sensor data, the plurality of environmental parameters, a dietary requirement, and a breeding guideline pertaining to a plurality of fish breeds; (560)
identifying, by the fish variety-based advisory module, one or more disease risks based on the images and the behaviour of the plurality of fishes; (565)
providing, by the fish variety-based advisory module, a treatment to the plurality of fishes to cure the one or more disease risks; (570)
analysing, by a demand analysis module, a plurality of real-time market trends based on a consumer demand, a plurality of seasonal variations, a pricing and a supply; (575)
suggesting, by the demand analysis module, a plurality of fish species for procurement based on a profitability and a least farming costs; (580)
compare, by the demand analysis module, a plurality of competitors pricing structure and the consumer demand based on the plurality of real-time market trends; (585)
forecasting, by the demand analysis module, the consumer demand and the pricing for trading the plurality of fishes based on a historical sales data comprising the consumer demand, the plurality of seasonal variations, the pricing, and the supply; (590)
identifying, by the demand analysis module, an optimal time and one or more geolocations for the trade of the plurality of fishes; and (595)
aligning, by the demand analysis module, production of the plurality of fishes with the consumer demand and the real-time market trends. (599)
Dated this 09th Day of October 2025
Signature

Manish Kumar
Patent Agent (IN/PA-5059)
Agent for the Applicant