Description:FIELD OF THE INVENTION
This invention relates to AI-Powered Precision Agriculture Platform for Real-Time Plant Health Monitoring Using GAN-Based Image Enhancement
BACKGROUND OF THE INVENTION
Decline in plant health and disease tends to go unnoticed in its initial stages due to insufficient eye scrutiny and absence of expert knowledge at farm level. Even prior to symptoms being visible, productivity and quality of yield may already have been lost. Furthermore, small-holder farmers are most likely not in a position to consult expert agronomists and laboratory facilities.
Environmental factors like inhomogeneous lighting, dust, and climatic changes further deteriorate the image quality and render the computer-aided diagnosis inappropriate. Additionally, the conventional methods of image classification are not extendable to new crops or new disease patterns and may generate false alarms or missed detections.
This is done by using GANs in an attempt to attain the best image improvement, where even low-quality images are put through rigorous testing. This also enhances the capacity of the system in embryonic disease feature recognition in poor conditions. The system also utilizes AI models that can be trained to attain new inputs with minimal in the form of retraining needed.
Apart from that, it also offers real-time feedback and reduces response-sensing time. Such pre-emptive intervention functionality can potentially reduce chemical use significantly, boost production, and lead agriculture towards sustainability. It bridges the connect gap between cutting-edge AI technology and remote rural community farmers and translates the best diagnostic ability to cost-effective down-to-earth rural farm settings.
US12342746B2: The present invention discloses a method for selective crop management in real time. The method comprises steps of: (a) producing a biosensor plant, said biosensor plant comprises a visual biomarker, said biomarker is encoded by at least one modified genetic locus comprising (i) preselected reporter gene allele having a phenotype detectable by a sensor, and (ii) a regulatory region of a preselected gene allele responsive to at least one parameter or condition of said plant or its environment, said regulatory region is operably linked to said reporter gene, such that the expression of said reporter gene phenotype is correlated with the status of said at least one parameter or condition of said biosensor plant or its environment; (b) acquiring image data of a target area comprising a plurality of said biosensor plants via said sensor and processing said data to generate a signal indicative of the phenotypic expression of said reporter gene allele of said biosensor plant; and (c) communicating said signal to an execution unit communicably linked to the sensor, said execution unit is capable of exerting in real time a selective monitoring and/or treatment of said target area or a portion thereof comprising said biosensor plants, said treatment is being responsive to said status of said parameter or condition of the biosensor plant or its environment. The present invention further discloses systems and plants related to the aforementioned method.
US20250049365A1: A system includes a pump dispensing liquid to a solid alloy to generate hydrogen gas in a sealed chamber; a chamber to store hydrogen gas; and an engine to power a vehicle or a converter coupled to the chamber to power an electronic/electrical device.
Present invention discloses an AI-powered precision agriculture platform for real-time monitoring of plant health using Generative Adversarial Network (GAN)-based image enhancement. The system integrates RGB, near-infrared, and thermal imaging with environmental sensors to capture plant and contextual data under varying field conditions.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention relates to an AI-powered precision agriculture platform that integrates multi-spectral imaging, environmental sensors, and advanced deep learning techniques to provide real-time monitoring of plant health. The system employs a Generative Adversarial Network (GAN)-based image enhancement module that restores and improves low-quality images captured under adverse environmental conditions such as poor lighting, shadows, or occlusion. Enhanced images are processed by AI diagnostic models, which identify diseases, nutrient deficiencies, and stress indicators with high accuracy. Data fusion with environmental sensor readings provides contextual insights into plant health, while cloud interfaces enable reporting, storage, and decision support. The system is deployable on drones, fixed monitoring stations, or handheld devices, making it adaptable across farm sizes and crop types. By providing timely alerts and actionable recommendations, the invention helps farmers reduce chemical usage, optimize resources, and increase yields, thereby supporting sustainable agriculture and improving food security.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention proposes an artificial intelligence precision agriculture platform based on Generative Adversarial Networks (GANs) to monitor crops and detect diseases in real-time during flight. The system integrates high-resolution images, environmental sensors, and machine learning models in real-time crop health processing. Image enhancement with GAN-based methods improves transparency and clarity of plant photos under varying lighting and meteorological conditions. The enhanced image data is as input to deep-learning classifiers for identification of onset of disease, nutrition stress, or weather stress at an early point. It is deployable on drones, robots, or fixed monitoring on open fields and greenhouses and provides actionable feedback to the farmer and agronomist.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Agriculture faces challenges in early and accurate detection of plant health issues such as diseases, nutrient deficiencies, and environmental stress. Traditional monitoring methods rely on manual observation or simple imaging, which are time-consuming, subjective, and often inaccurate under field conditions.
The invention addresses these challenges by introducing an AI-powered platform designed for real-time monitoring of plant health. At its core, the system combines multi-spectral imaging, environmental sensing, GAN-based image enhancement, and AI-based diagnostic algorithms into a unified platform.
The image acquisition unit includes RGB, near-infrared (NIR), and thermal cameras mounted on drones, robots, or fixed stands. These capture high-resolution images of crops under different lighting and environmental conditions. Environmental sensors measure parameters such as temperature, humidity, and soil moisture, offering contextual information about crop conditions.
A GAN-based image enhancement module processes the acquired images to restore clarity, reduce blur, and adjust lighting. Unlike conventional filters, GANs reconstruct missing or degraded image features, producing high-fidelity data even in adverse conditions.
The enhanced images are then fused with sensor data in a data fusion module, ensuring a holistic representation of plant health. This integration improves the reliability of diagnosis by correlating visual symptoms with environmental stress factors.
The AI diagnostic engine processes the fused data using deep learning models such as CNNs or transformers trained on large datasets of plant diseases and stress patterns. The engine outputs predictions on disease type, severity, nutrient deficiencies, and other plant anomalies.
A decision support and alert module generates recommendations such as pesticide spraying, irrigation adjustments, or shading interventions. These alerts are transmitted to farmers via mobile apps or dashboards, providing location-tagged and severity-based notifications.
The system includes a cloud interface and storage unit that archives diagnostic results, sensor readings, and image data. This enables long-term monitoring, trend analysis, and continuous retraining of AI models to improve accuracy.
The invention supports adaptive learning, wherein user feedback and new image datasets are used to retrain GANs and diagnostic models, ensuring adaptability to new crops, diseases, and conditions.
Deployment flexibility is a key feature. The platform can be mounted on drones for large-scale monitoring, fixed installations for greenhouse environments, or handheld devices for small farms. It can also integrate with irrigation or climate control systems for automated intervention.
For resource-limited settings, the system supports lightweight hardware implementations on Raspberry Pi or Jetson devices powered by solar energy. Communication modules support Wi-Fi, Zigbee, LoRa, or 4G, ensuring connectivity across diverse farm environments.
The invention provides advantages over existing solutions by offering robust image enhancement, multi-modal data fusion, and explainable AI diagnosis. Unlike conventional systems, it generalizes across crop types and geographies using transfer learning and zero-shot detection.
Environmental benefits include reduced pesticide usage and optimized irrigation, while societal benefits include improved yield and reduced crop losses for farmers. At the national level, the invention supports sustainable agriculture and food security goals.
Overall, the invention represents a scalable, affordable, and intelligent platform bridging cutting-edge AI with practical agricultural needs.
Best Method of Working
The best method of working the invention involves deploying the system on drones equipped with RGB, NIR, and thermal cameras along with environmental sensors. Data captured during flight is processed in real-time by an edge computing device such as NVIDIA Jetson. The GAN-based module enhances low-quality images, which are fused with environmental sensor data. The AI diagnostic engine then analyzes the fused dataset to detect plant stress and diseases, generating actionable recommendations. Alerts are transmitted to farmers via a mobile dashboard, while processed data is stored in the cloud for monitoring and retraining. Integration with irrigation or spraying systems can allow automatic corrective actions. This configuration ensures optimal scalability, real-time operation, and accuracy.
The invention proposes an artificial intelligence precision agriculture platform based on Generative Adversarial Networks (GANs) to monitor crops and detect diseases in real-time during flight. The system integrates high-resolution images, environmental sensors, and machine learning models in real-time crop health processing. Image enhancement with GAN-based methods improves transparency and clarity of plant photos under varying lighting and meteorological conditions. The enhanced image data is as input to deep-learning classifiers for identification of onset of disease, nutrition stress, or weather stress at an early point. It is deployable on drones, robots, or fixed monitoring on open fields and greenhouses and provides actionable feedback to the farmer and agronomist.
Step-by-Step Functionality Working
Step 1: Capture of Data
RGB, NIR, and thermal high-resolution crop images are captured from drones, hand-held systems, or fixed imagers. Environmental sensors capture ambient temperature, humidity, and moisture in the ground.
Step 2: GAN-Based Image Enhancement
Embedded images are optimized by the GAN-based enhancement module, enhancing resolution, despeckling blurs, and light difference adjustment. It also makes low-resolution images actionable.
Step 3: Data Fusion
These are processed and combined with environmental sensor measurements to produce an high-fidelity data set indicating the true state of the health of the plant.
Step 4: Plant Health Diagnosis through AI
Combinatorial data is inputted into a deep learning classifier which decides the onset of disease symptoms, water or heat stress, or deficiency. All of them are given a severity score.
Step 5: Generation of Decision Support and Alerts
It alerts the farmer's website or mobile app by severity and location by geo-coordinate by the system. It can even recommend particular interventions such as pesticide spraying, watering, or shade control.
Step 6: Reporting and Storage
Diagnostic output is stored in the cloud with timestamped and GPS-tagged data for future retrieval. Time-series analysis detects repeated faults.
Step 7: Adaptive Learning
User inputs and newer image databases are used to retune the diagnosis models and GAN for better prediction performance in the long run.
Step 8: Optional System Integration with Automation Systems
Optional system integration with automated climate control or irrigation systems enables the system to operate automatically without human intervention taking proactive measures.
The above pipeline is deployed, accurate, and automated knowledge about crop health.
10) Five Expected Patent Claims
A farm image data-specific real-time imagery enhancement module with GAN technology.
A crop health monitoring multi-modal vision and environmental data fusion platform.
A light-weight deployment AI-powered diagnostic system on different crops and climatic conditions.
A self-sustaining decision support system with capability to alert and suggest the extent of disease.
A compact and modular hardware to be deployed in drones, fixed mounting, or hand-held mode.
Key Components
The platform of interest here is the use of hardware, software, and artificial intelligence algorithms for effective monitoring of plant health. The major elements are:
Image Acquisition Unit: RGB, NIR (Near-Infrared), and thermal cameras mounted on drones, tripods, or mobile vehicles to capture multi-spectral images of the plants.
Environmental Sensors: Temperature, humidity, and soil moisture sensors to provide contextual data to determine plant health.
Edge Computing Device: Embedded boards like NVIDIA Jetson or Raspberry Pi to execute image enhancement and AI inference locally with minimal reliance on the cloud.
GAN-Based Image Enhancement Module: Software module that takes in low-light, occlusion, or blur images and provides high-resolution and clear images to be inspected.
AI Diagnosis Engine: Transformer or CNN classifier model over an enormous dataset of plant diseases to provide predictions on disease type, severity, or nutrient deficiency.
Cloud Interface and Dashboard: Remote device for monitoring, visualization, alerting, and decision support of farmers or agronomists.
Power Supply: Powered with solar or battery based on the deployment scenario.
Communication Module: Wi-Fi, LoRa, Zigbee, or 4G for field-to-cloud or mobile phone data communication.
All these are built to work as a single, independent smart farm assistant.
Technology Used
Technologies used include RGB cameras, NIR cameras, and hybrid thermal sensors to take high-resolution images of canopy crop and soil surface. Multi-spectral images contain information which is used for abnormality identification not seen with the naked eye.
Environmental sensors like DHT11 (humidity/temperature), capacitive soil moisture sensor, and photodiodes monitor the environment conditions to gain context information for diagnosis.
Processing Unit: Edge devices like NVIDIA Jetson Nano, Google Coral, or Raspberry Pi perform real-time inference to avoid latency and dependency on internet connectivity.
Image Enhancement Module: The system's novelty relies on GAN-based architecture for restoring motion-blurred, low-light, or occluded images. Conditional GANs or CycleGANs are used for reconstructing and normalizing visual information to carry out precise analysis.
Diagnostic Algorithm: Plant health conditions are categorized upon enrichment with a deep neural network (CNN, EfficientNet, or Vision Transformers). They are trained on a labeled plant disease, stress pattern, and nutrient deficiency dataset.
Software and Integration: Python-based back-end with TensorFlow/PyTorch for AI models. Real-time update through web-based front-end dashboard via web technologies (React or Angular). REST APIs and MQTT protocols support easy data transfer between cloud platforms and field devices.
Power and Communication: Solar panels or battery packs power to support long-duration deployments. LoRaWAN, Zigbee, Wi-Fi, and 4G LTE communication protocols are chosen based on terrain and connectivity requirements.
Cloud and Data Storage: AWS or Azure cloud facilitates long-term model evaluation, safe storage, and retraining of models. Cloud interface enables user authentication, geolocation tagging, and aggregations at the farm level.
Such convergence of edge AI, wireless communications, cloud computing, and next-generation imaging makes the invention field-deployable and scalable and gives it strength.
ADVANTAGES OF THE INVENTION
Environment: Saves water and soil by preventing excessive use of pesticides through correct usage.
Society: Provides farmers with professional-grade diagnosis, increases production of crops, and minimizes loss of crops.
Country: Increases food security, supports sustainable agriculture, and energizes the economy through AI technology in agriculture.
Precision Functionality
This technology is used in the shape of an AI diagnosis system on the basis of real-time views of plant images and environmental data, GAN-based image processing, and plant disease diagnosis on the basis of deep learning. It helps farmers in early diagnosis, reminding, and remedial treatment in a single automated system.
Invention employs a novel GAN-based pipeline for image processing tailored specifically to agricultural scenes. Unlike image-based conventional crop monitoring systems, the platform provides real-time high-quality image improvement of occluded or poor-quality images to provide consistent disease detection even in adverse environmental conditions. Other than that, the learning ability of the platform is such that it can learn and adapt to identify different crops, climatic conditions, and seasons without the need for retraining. With multi-spectral imaging, GAN restoration, and AI classifiers, the system offers more accuracy for plant health anomaly detection. Combined with real-time feedback, location tagging, and auto alerts, the above further distinguishes the system from existing agricultural monitoring systems, and therefore the system is very efficient in remote or under-sourced areas.
 
, Claims:1. A system for AI-powered precision agriculture and real-time plant health monitoring, comprising:
i. an image acquisition unit including RGB, near-infrared, and thermal cameras for capturing plant images;
ii. an environmental sensing unit including temperature, humidity, and soil moisture sensors;
iii. a GAN-based image enhancement module configured to restore low-quality or degraded plant images;
iv. a data fusion module configured to integrate enhanced image data with environmental sensor readings;
v. an AI diagnostic engine employing deep learning models to identify plant diseases, nutrient deficiencies, or stress;
vi. a decision support and alert module to generate recommendations and notify farmers;
vii. a cloud interface and storage unit for data archiving, visualization, and model retraining;
viii. an edge computing device for local processing; and
ix. a communication module supporting Wi-Fi, Zigbee, LoRa, or 4G for data transmission.
2. A method for real-time plant health monitoring using the system as claimed in claim 1, comprising the steps of:
i. capturing plant images using RGB, NIR, and thermal cameras along with environmental sensor readings;
ii. enhancing degraded images through the GAN-based image enhancement module;
iii. fusing the enhanced images with environmental sensor data;
iv. analyzing the fused dataset using the AI diagnostic engine;
v. generating disease detection and stress prediction outputs;
vi. transmitting alerts and recommendations through mobile or dashboard interfaces; and
vii. storing the results in the cloud for monitoring and retraining.
3. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the GAN-based image enhancement module employs conditional GANs or CycleGANs to restore low-light, blurred, or occluded plant images.
4. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the AI diagnostic engine is trained on plant disease datasets using convolutional neural networks, vision transformers, or EfficientNet models.
5. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the decision support and alert module provide geo-tagged notifications with severity scores and intervention suggestions.
6. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the cloud interface enables time-series analysis and adaptive model retraining.
7. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the edge computing device comprises NVIDIA Jetson, Google Coral, or Raspberry Pi to perform local inference.
8. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the communication module supports multi-protocol connectivity for remote or rural deployment.
9. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the system integrates with automated irrigation or climate control systems for corrective actions.
10. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the platform supports handheld, drone-based, and fixed installations adaptable to farm size and crop type.