1. A smart plant growth monitoring and fertilizer recommendation system (100), the system (100) comprising: a sensor unit (102) configured to collect soil and environmental data including nitrogen level, phosphorus level, potassium level, soil moisture, temperature, humidity, and light intensity; a microcontroller (114) configured to: receive the soil and environmental data from the sensor unit (102); and transmit the soil and environmental data through a wireless communication unit (116); and a cloud platform (118) configured to receive the soil and environmental data transmitted from the microcontroller (114), characterized in that the cloud platform (118) is configured to: receive the soil and environmental data from the microcontroller (114); process the received soil and environmental data, using a machine learning model, and determine soil nutrient condition and plant growth condition; generate a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and display the soil and environmental data and the fertilizer recommendation to a user using a web-based dashboard (122).

2. The system (100) as claimed in claim 1, wherein the sensor unit (102) includes a Nitrogen, Phosphorus, Potassium (NPK) sensor (104), a soil moisture sensor (106), a temperature sensor (108), a humidity sensor (110), and a light intensity sensor (112).

3. The system (100) as claimed in claim 1, wherein the microcontroller (114) is configured to convert sensor signals received from the sensor unit (102) into digital data before transmission to the cloud platform (118).

4. The system (100) as claimed in claim 1, wherein the microcontroller (114) is an Espressif 32 (ESP32) microcontroller.

5. The system (100) as claimed in claim 1, wherein the wireless communication unit (116) comprise a Wi-Fi communication chip embedded with Long Range (LoRa) communication protocols.

6. The system (100) as claimed in claim 1, wherein the machine learning model is trained using historical agricultural data including soil characteristics, crop type information, and environmental parameters.

7. The system (100) as claimed in claim 1, wherein the cloud platform (118) is configured to determine a quantity of nitrogen, phosphorus, and potassium required for crop growth based on analysis of the soil and environmental data.

8. The system (100) as claimed in claim 1, wherein the web-based dashboard (122) provides real-time visualization of soil moisture, temperature, humidity, light intensity, and nutrient levels.

9. A method (300) for smart plant growth monitoring and fertilizer recommendation, the method (300) is characterized by steps of: collecting soil and environmental data through a sensor unit (102); transmitting the collected soil and environmental data to a cloud platform (118) through a microcontroller (114) using a wireless communication unit (116); processing the transmitted soil and environmental data through a machine learning model located in the cloud platform (118); determining soil nutrient condition and plant growth condition based on analysis of the soil and environmental data by the machine learning model; generating a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and displaying the soil and environmental data and the fertilizer recommendation on a web-based dashboard (122) for access by a user.

10. The method (300) as claimed in claim 9, wherein the sensor unit (102) includes a Nitrogen, Phosphorus, Potassium (NPK) sensor (104), a soil moisture sensor (106), a temperature sensor (108), a humidity sensor (110), and a light intensity sensor (112). Date: March 16, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a fertilizer recommendation system and particularly to a smart plant growth monitoring and fertilizer recommendation system.
Description of Related Art
[002] Agriculture depends heavily on proper soil nutrition and balanced fertilizer use for healthy crop growth. Many farmers rely on manual observation of soil conditions or follow fixed fertilizer schedules without precise knowledge of nutrient levels in the soil. Such practices often lead to inaccurate fertilizer application. Excess fertilizer damages soil quality and contaminates surrounding ecosystems, while insufficient fertilizer reduces crop yield and plant health. Therefore, agriculture faces a need for accurate assessment of soil conditions and appropriate fertilizer selection to support efficient crop production.
[003] Several agricultural tools and advisory methods exist for fertilizer management. Traditional approaches include manual soil testing through agricultural laboratories, use of printed fertilizer charts, and consultation with agricultural experts. Some digital agricultural platforms provide general fertilizer recommendations based on crop type and regional soil characteristics. In addition, certain sensor-based agricultural devices measure limited environmental parameters such as soil moisture or temperature and provide basic information to farmers for crop management. These solutions attempt to assist farmers in fertilizer planning and plant growth management.
[004] Despite such developments, existing approaches still exhibit multiple limitations. Manual soil testing requires significant time, laboratory access, and periodic sample collection, that restricts frequent assessment of soil conditions. Fertilizer charts and generalized recommendations fail to reflect actual nutrient levels within specific fields. Sensor-based devices that measure limited parameters lack comprehensive soil nutrient evaluation and often do not provide precise fertilizer guidance. As a result, farmers still encounter difficulty in determination of accurate fertilizer quantity and nutrient balance, that leads to inefficient fertilizer use, reduced soil fertility, and inconsistent crop productivity.
[005] There is thus a need for an improved and advanced smart plant growth monitoring and fertilizer recommendation system that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a smart plant growth monitoring and fertilizer recommendation system. The system comprising a sensor unit configured to collect soil and environmental data including nitrogen level, phosphorus level, potassium level, soil moisture, temperature, humidity, and light intensity. The system further comprising a microcontroller. The microcontroller is configured to receive the soil and environmental data from the sensor unit and transmit the soil and environmental data through a wireless communication unit. The system further comprising a cloud platform configured to receive the soil and environmental data transmitted from the microcontroller. The cloud platform is configured to receive the soil and environmental data from the microcontroller; process the received soil and environmental data, using a machine learning model, and determine soil nutrient condition and plant growth condition; generate a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and display the soil and environmental data and the fertilizer recommendation to a user using a web-based dashboard.
[007] Embodiments in accordance with the present invention further provide a method for smart plant growth monitoring and fertilizer recommendation. The method comprising steps of collecting soil and environmental data through a sensor unit; transmitting the collected soil and environmental data to a cloud platform through a microcontroller using a wireless communication unit; processing the transmitted soil and environmental data through a machine learning model located in the cloud platform; determining soil nutrient condition and plant growth condition based on analysis of the soil and environmental data by the machine learning model; generating a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and displaying the soil and environmental data and the fertilizer recommendation on a web-based dashboard for access by a user.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a smart plant growth monitoring and fertilizer recommendation system.
[009] Next, embodiments of the present application may provide a smart plant growth system that provides accurate fertilizer recommendations based on soil nutrient conditions, that improves crop productivity and plant health.
[0010] Next, embodiments of the present application may provide a smart plant growth system that reduces excessive fertilizer application, that lowers input costs and minimizes environmental impact on soil and surrounding ecosystems.
[0011] Next, embodiments of the present application may provide a smart plant growth system that enables real-time monitoring of soil and environmental parameters, that supports timely agricultural decision-making.
[0012] Next, embodiments of the present application may provide a smart plant growth system that assists farmers in efficient nutrient management, that improves soil fertility and promotes sustainable agricultural practices.
[0013] Next, embodiments of the present application may provide a smart plant growth system that presents soil condition information and fertilizer guidance through a user-friendly digital interface, that improves accessibility and ease of use for farmers.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 illustrates a schematic diagram of a smart plant growth monitoring and fertilizer recommendation system, according to an embodiment of the present invention;
[0016] FIG. 2 illustrates a block diagram of a cloud platform of the smart plant growth system, according to an embodiment of the present invention; and
[0017] FIG. 3 depicts a flowchart of a method for smart plant growth monitoring and fertilizer recommendation, according to an embodiment of the present invention.
DETAILED DESCRIPTION
[0018] FIG. 1 illustrates a schematic diagram of a smart plant growth monitoring and fertilizer recommendation system 100 (hereinafter referred to as the system 100), according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to obtain soil and environmental data associated with agricultural conditions. The collected information may include parameters related to soil nutrients and environmental factors that influence plant growth. The information may be obtained from an agricultural field and may represent real-time conditions associated with crop cultivation.
[0019] In an embodiment of the present invention, the system 100 may further be adapted to transmit the obtained soil and environmental data to a remote processing environment through wireless communication. The transmitted information may be converted into digital data prior to transmission. The digital data may thereafter be analyzed using computational techniques that may determine soil nutrient condition and plant growth condition based on the received information.
[0020] Based on the processed soil and environmental data, the system 100 may be adapted to determine nutrient requirements associated with plant growth. The determined nutrient requirements may correspond to fertilizer elements including nitrogen, phosphorus, and potassium. The system 100 may generate a fertilizer recommendation that reflects nutrient conditions of the agricultural field and may assist in identifying an appropriate fertilizer quantity for crop cultivation.
[0021] In an embodiment of the present invention, the system 100 may be configured to prevent excessive fertilizer application and insufficient fertilizer application within the agricultural field. The system 100 may evaluate the soil and environmental data and determine nutrient availability associated with nitrogen, phosphorus, and potassium. Based on the evaluated nutrient availability, the system 100 may generate the fertilizer recommendations corresponding to required nutrient quantities such that the fertilizer recommendation may assist in maintaining balanced soil nutrient conditions during crop cultivation.
[0022] Further, the system 100 may be adapted to present the analyzed soil information and the fertilizer recommendation to a user through a digital interface. The presented information may provide visualization of soil and environmental parameters together with the fertilizer recommendation. Such presentation may assist farmers or agricultural practitioners in understanding field conditions and may support informed agricultural decision making.
[0023] In an embodiment of the present invention, the system 100 may be configured to support precision agriculture operations through continuous monitoring of the soil and environmental data. The system 100 may obtain the soil and environmental data associated with nutrient concentration and environmental conditions, and the system 100 may further analyze the obtained soil and environmental data through the machine learning model. Based on the analytical outputs generated by the machine learning model, the system 100 may generate the fertilizer recommendations that correspond to the actual nutrient requirements of the agricultural field, thereby enabling precise fertilizer application during crop cultivation.
[0024] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the system 100 may comprise a sensor unit 102, a microcontroller 114, a wireless communication unit 116, a cloud platform 118, a computing unit 120, and a web-based dashboard 122. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0025] In an embodiment of the present invention, the sensor unit 102 may be configured to collect the soil and environmental data associated with agricultural land. The soil and environmental data may be, but not limited to, nitrogen level, phosphorus level, potassium level, soil moisture, temperature, humidity, light intensity, and so forth. The sensor unit 102 may be positioned in proximity to a crop field such that the sensor unit 102 may obtain field conditions that influence plant growth. The sensor unit 102 may be placed partially within soil layers of the agricultural land or may be positioned above a ground surface depending on monitoring requirements. The sensor unit 102 may be installed in a protective enclosure adapted to withstand outdoor agricultural environments including moisture, dust, and temperature variations.
[0026] In another embodiment of the present invention, the sensor unit 102 may be mounted on a support structure within an agricultural field. The support structure may include a pole, a frame, or an embedded holder installed within the field. The sensor unit 102 may be adapted to maintain stable contact with surrounding soil conditions while continuously obtaining the soil and environmental data from the surrounding area. The installation arrangement may allow the sensor unit 102 to operate over extended periods within crop fields without frequent relocation.
[0027] In a further embodiment of the present invention, the sensor unit 102 may be deployed in distributed agricultural zones within a farm. Multiple installation points may be selected across different sections of the agricultural field such that variations in soil conditions across the land may be observed. The sensor unit 102 may therefore assist in capturing the representative soil and environmental data associated with field conditions across various crop cultivation zones.
[0028] In an embodiment of the present invention, the sensor unit 102 may be configured to obtain the soil and environmental data from multiple locations within the agricultural field. The microcontroller 114 may receive the soil and environmental data generated from distributed sensing locations and transmit the soil and environmental data to the cloud platform 118. The data processing module 202 may analyze the received soil and environmental data corresponding to different sensing locations and may determine location-specific soil nutrient conditions associated with the agricultural field.
[0029] In another embodiment of the present invention, the sensor unit 102 may be integrated with portable agricultural monitoring equipment. The sensor unit 102 may be temporarily inserted into soil regions for short-duration observation of field conditions. Such portable deployment may allow agricultural practitioners to relocate the sensor unit 102 across different crop plots for comparative soil condition analysis. The sensor unit 102 may therefore support flexible installation configurations across multiple agricultural environments including open farms, greenhouse cultivation systems, and controlled agricultural test plots.
[0030] The sensor unit 102 may comprise sensors such as, but not limited to, a Nitrogen, Phosphorus, Potassium (NPK) sensor 104, a soil moisture sensor 106, a temperature sensor 108, a humidity sensor 110, and a light intensity sensor 112. In an embodiment of the present invention, the sensor unit 102 may be configured to continuously obtain the soil and environmental data from the agricultural field through periodic sensing cycles. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104, the soil moisture sensor 106, the temperature sensor 108, the humidity sensor 110, and the light intensity sensor 112 may periodically measure respective environmental parameters and generate sensor signals at predefined sampling intervals. The microcontroller 114 may therefore receive continuously generated sensor signals and convert the signals into digital data representing real-time soil and environmental data associated with the agricultural field.
[0031] The sensor unit 102 may be, but not limited to, soil sensing units, integrated agricultural sensing modules, environmental monitoring assemblies, multi-parameter sensing platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the sensor unit 102, including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be adapted to obtain nutrient-related soil and environmental data associated with agricultural soil conditions. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be positioned in contact with soil layers of an agricultural field and may be adapted to detect nutrient concentrations corresponding to nitrogen, phosphorus, and potassium present in the soil. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be configured to generate electrical signals corresponding to the detected nutrient levels. The generated signals may represent relative or absolute concentration values associated with soil nutrient composition.
[0033] In another embodiment of the present invention, the Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be adapted to employ electrochemical sensing principles for detection of nutrient levels. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may include sensing electrodes that may interact with ions present in the surrounding soil medium. Variations in ionic concentration corresponding to nitrogen compounds, phosphorus compounds, and potassium ions may produce measurable electrical potential or conductivity variations. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may convert the measured electrical variations into digital or analog signals representing soil nutrient levels.
[0034] In a further embodiment of the present invention, the Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be adapted to periodically measure nutrient conditions of the soil and transmit the obtained soil and environmental data for further analysis. The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may operate through repeated sampling cycles in which the sensor probes interact with soil particles and moisture content to estimate nutrient availability. The obtained soil and environmental data may therefore reflect nutrient distribution within the agricultural field and may support accurate assessment of soil fertility conditions.
[0035] The Nitrogen, Phosphorus, Potassium (NPK) sensor 104 may be, but not limited to, electrochemical nutrient sensors, ion-selective electrode sensors, optical nutrient detection sensors, soil nutrient probe sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the Nitrogen, Phosphorus, Potassium (NPK) sensor 104, including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the soil moisture sensor 106 may be adapted to obtain the soil and environmental data associated with moisture content present within soil layers of an agricultural field. The soil moisture sensor 106 may be positioned partially within the soil such that the soil moisture sensor 106 may interact with surrounding soil particles and water content. The soil moisture sensor 106 may be configured to detect variations in soil water concentration and may generate electrical signals corresponding to the detected moisture levels within the soil.
[0037] In another embodiment of the present invention, the soil moisture sensor 106 may be adapted to determine soil moisture conditions based on electrical property variations associated with soil media. The soil moisture sensor 106 may operate through capacitive sensing or resistive sensing techniques in which electrical characteristics of soil change in response to water presence. The soil moisture sensor 106 may detect variations in capacitance or electrical resistance caused by moisture content within the soil and may convert the detected variations into measurable signals representing soil moisture levels.
[0038] In a further embodiment of the present invention, the soil moisture sensor 106 may be adapted to periodically measure soil moisture conditions and generate the soil and environmental data reflecting water availability within the agricultural soil. The soil moisture sensor 106 may perform repeated sensing cycles over time to monitor dynamic moisture variations caused by irrigation, rainfall, or evaporation. The obtained soil and environmental data may therefore provide an indication of soil hydration conditions associated with plant growth and agricultural management.
[0039] The soil moisture sensor 106 may be, but not limited to, capacitive moisture sensors, resistive soil moisture probes, dielectric moisture sensors, time-domain reflectometry soil sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the soil moisture sensor 106, including known, related art, and/or later developed technologies.
[0040] In an embodiment of the present invention, the temperature sensor 108 may be adapted to obtain the soil and environmental data associated with temperature conditions present within an agricultural field. The temperature sensor 108 may be positioned in proximity to soil layers or surrounding air regions such that the temperature sensor 108 may detect temperature variations that influence plant growth and soil processes. The temperature sensor 108 may be configured to sense thermal variations and generate electrical signals corresponding to detected temperature values.
[0041] In another embodiment of the present invention, the temperature sensor 108 may be adapted to determine temperature conditions based on temperature-sensitive electronic elements. The temperature sensor 108 may include thermistor-based sensing elements, semiconductor temperature sensing elements, or similar temperature-sensitive components that may exhibit electrical property variations in response to temperature changes. The temperature sensor 108 may convert the detected electrical variations into measurable signals representing temperature conditions associated with the surrounding agricultural environment.
[0042] In a further embodiment of the present invention, the temperature sensor 108 may be adapted to periodically measure temperature conditions and generate the soil and environmental data representing thermal characteristics of the agricultural field. The temperature sensor 108 may perform repeated sensing cycles over time to observe temperature fluctuations caused by environmental factors such as sunlight exposure, irrigation conditions, and atmospheric variations. The obtained soil and environmental data may therefore assist in evaluation of environmental conditions that influence plant growth and nutrient availability.
[0043] The temperature sensor 108 may be, but not limited to, thermistor sensors, semiconductor temperature sensors, resistance temperature detectors, digital temperature sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the temperature sensor 108, including known, related art, and/or later developed technologies.
[0044] In an embodiment of the present invention, the humidity sensor 110 may be adapted to obtain the soil and environmental data associated with moisture content present within the surrounding air of an agricultural field. The humidity sensor 110 may be positioned above the soil surface or within a monitoring enclosure such that the humidity sensor 110 may interact with ambient air conditions. The humidity sensor 110 may be configured to detect relative humidity levels and may generate electrical signals corresponding to atmospheric moisture conditions present around crop cultivation areas.
[0045] In another embodiment of the present invention, the humidity sensor 110 may be adapted to determine humidity conditions based on variations in electrical properties of humidity-sensitive materials. The humidity sensor 110 may include capacitive sensing elements or resistive sensing elements that may respond to water vapor concentration present in the surrounding air. Variations in humidity levels may cause measurable changes in capacitance or electrical resistance of the sensing material. The humidity sensor 110 may convert the detected variations into measurable signals representing atmospheric humidity levels.
[0046] In a further embodiment of the present invention, the humidity sensor 110 may be adapted to periodically measure humidity conditions and generate the soil and environmental data associated with atmospheric moisture variations within the agricultural field. The humidity sensor 110 may perform repeated sensing cycles over time to observe humidity fluctuations caused by environmental conditions such as rainfall, irrigation, evaporation, or temperature variation. The obtained soil and environmental data may therefore assist in understanding environmental conditions that influence plant growth and crop development.
[0047] The humidity sensor 110 may be, but not limited to, capacitive humidity sensors, resistive humidity sensors, thermal humidity sensors, digital relative humidity sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the humidity sensor 110, including known, related art, and/or later developed technologies.
[0048] In an embodiment of the present invention, the light intensity sensor 112 may be adapted to obtain the soil and environmental data associated with light conditions present within an agricultural field. The light intensity sensor 112 may be positioned above the soil surface or mounted on a support structure such that the light intensity sensor 112 may receive incident light from natural or artificial sources. The light intensity sensor 112 may be configured to detect variations in light intensity and may generate electrical signals corresponding to the detected illumination levels.
[0049] In another embodiment of the present invention, the light intensity sensor 112 may be adapted to determine light intensity conditions based on photoelectric sensing principles. The light intensity sensor 112 may include a photodiode, phototransistor, or photo resistive sensing element that may respond to incident light radiation. Exposure to light may cause variations in electrical characteristics of the sensing element, which may be detected as changes in current, voltage, or resistance. The light intensity sensor 112 may convert the detected variations into measurable signals representing illumination levels within the agricultural environment.
[0050] In a further embodiment of the present invention, the light intensity sensor 112 may be adapted to periodically measure illumination conditions and generate the soil and environmental data reflecting light availability for crop growth. The light intensity sensor 112 may perform repeated sensing cycles to observe variations in sunlight exposure caused by environmental conditions such as cloud cover, shading, or seasonal changes. The obtained soil and environmental data may therefore assist in evaluation of light conditions associated with plant photosynthetic activity and agricultural field management.
[0051] The light intensity sensor 112 may be, but not limited to, photodiode light sensors, phototransistor light sensors, photo resistive light sensors, digital illumination sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the light intensity sensor 112, including known, related art, and/or later developed technologies.
[0052] In an embodiment of the present invention, the microcontroller 114 may be communicatively connected to the sensor unit 102. In turn, the microcontroller 114 may operatively be coupled to the Nitrogen, Phosphorus, Potassium (NPK) sensor 104, the soil moisture sensor 106, the temperature sensor 108, the humidity sensor 110, and the light intensity sensor 112. The operative communication may be, but not limited to, receiving, transmitting, processing, synchronizing, querying, updating, encrypting, decrypting, storing, retrieving, validating, logging, monitoring, alerting, authenticating, authorizing, compressing, decompressing, streaming, and rendering data or commands between the sensor unit 102 and the microcontroller 114.
[0053] In an embodiment of the present invention, the microcontroller 114 may be configured to receive the soil and environmental data from the sensor unit 102 through electrical signal interfaces. The microcontroller 114 may be operatively coupled with sensing elements of the sensor unit 102 through analog input channels or digital communication interfaces such as serial communication protocols. Electrical signals generated by the sensing elements may correspond to measured soil and environmental parameters, and the microcontroller 114 may be configured to acquire the signals through internal input circuitry. The microcontroller 114 may be configured to process the acquired signals through embedded processing logic and analog-to-digital conversion circuitry such that the soil and environmental data may be represented in a digital format suitable for subsequent transmission and computational analysis.
[0054] The microcontroller 114 may be configured to convert sensor signals received from the sensor unit 102 into digital data through internal signal acquisition circuitry. The sensor signals may be received in analog electrical form corresponding to measured soil and environmental parameters. The microcontroller 114 may include analog-to-digital conversion circuitry that may sample the received electrical signals and translate the sampled signals into corresponding digital representations. The converted digital data may therefore represent quantified soil and environmental data that may be stored temporarily within internal memory or may be prepared for subsequent transmission and computational processing.
[0055] Further, the microcontroller 114 may be configured to transmit the digital data of the soil and environmental data to the cloud platform 118 through the wireless communication unit 116. The microcontroller 114 may be configured to format the digital data into structured data packets through embedded firmware logic. The wireless communication unit 116 may utilize wireless communication protocols such as Wi-Fi or Long Range (LoRa) communication protocols to establish a communication link with the cloud platform 118. The microcontroller 114 may be configured to therefore relay the structured data packets through the wireless communication unit 116 such that the soil and environmental data may be delivered to the cloud platform 118 for further computational processing and analysis.
[0056] The microcontroller 114 may be, but not limited to, embedded microcontroller units, single-chip microcontroller systems, Internet-of-Things microcontroller boards, programmable embedded processing controllers, and so forth. In a preferred embodiment of the present invention, the microcontroller 114 may be an Espressif 32 (ESP32) processing system. Embodiments of the present invention are intended to include or otherwise cover any type of the microcontroller 114, including known, related art, and/or later developed technologies.
[0057] In an embodiment of the present invention, the wireless communication unit 116 may be adapted to relay the soil and environmental data to the cloud platform 118 through a wireless network interface. The wireless communication unit 116 may establish a communication link with a remote server environment using wireless communication protocols such as Wi-Fi or long-range communication mechanisms. The wireless communication unit 116 may encapsulate the received digital data into communication frames and may transmit the frames through a network gateway or internet connectivity infrastructure. The transmitted soil and environmental data may therefore be delivered to the cloud platform 118 for subsequent computational analysis and generation of the fertilizer recommendation .
[0058] The wireless communication unit 116 may be, but not limited to, Wi-Fi communication modules, long-range communication modules, radio-frequency communication transceivers, wireless IoT communication chipsets, and so forth. In a preferred embodiment of the present invention, the wireless communication unit 116 comprise a Wi-Fi communication chip embedded with Long Range (LoRa) communication protocols. Embodiments of the present invention are intended to include or otherwise cover any type of the wireless communication unit 116, including known, related art, and/or later developed technologies.
[0059] In an embodiment of the present invention, the cloud platform 118 may be coupled to the microcontroller 114 via the wireless communication unit 116. The coupling may be, but not limited to, receiving, transmitting, processing, synchronizing, querying, updating, encrypting, decrypting, storing, retrieving, validating, logging, monitoring, alerting, authenticating, authorizing, compressing, decompressing, streaming, and rendering data or commands between the cloud platform 118 and the microcontroller 114. The cloud platform 118 may be, but not limited to, cloud computing infrastructures, distributed cloud servers, remote data processing environments, cloud-based analytical platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud platform 118, including known, related art, and/or later developed technologies. The cloud platform 118 may further be explained in detail in conjunction with FIG. 2.
[0060] In an embodiment of the present invention, the computing unit 120 may be an electronic device adapted to be used by a user. The computing unit 120 may be, but not limited to, desktop computing systems, laptop computing systems, tablet computing devices, mobile computing devices, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing unit 120, including known, related art, and/or later developed technologies.
[0061] In an embodiment of the present invention, the computing unit 120 may be installed with the web-based dashboard 122. The web-based dashboard 122 may be adapted to display and visualize the soil and environmental data. Further, the web-based dashboard 122 may be adapted to display and visualize the fertilizer recommendation, generated by the cloud platform 118. Additionally, the web-based dashboard 122 provides real-time visualization of soil moisture, temperature, humidity, light intensity, and nutrient levels.
[0062] In an embodiment of the present invention, the web-based dashboard 122 may function as a decision support interface for agricultural users. The data display module 206 may transmit the processed soil and environmental data and fertilizer recommendation outputs to the web-based dashboard 122, and the computing unit 120 may display the information through graphical visualization elements. The presented information may therefore assist farmers in evaluating soil nutrient conditions and selecting appropriate fertilizer inputs during crop cultivation.
[0063] The web-based dashboard 122 may be, but not limited to, web application dashboards, browser-based monitoring interfaces, cloud-hosted visualization dashboards, interactive data visualization interfaces, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the web-based dashboard 122, including known, related art, and/or later developed technologies.
[0064] In an embodiment of the present invention, the system 100 may operate as an Internet-of-Things (IoT) based agricultural monitoring architecture. The sensor unit 102 may obtain the soil and environmental data from the agricultural field and transmit the soil and environmental data to the microcontroller 114. The microcontroller 114 may transmit the soil and environmental data through the wireless communication unit 116 to the cloud platform 118, where computational analysis may be performed through the machine learning model for generation of fertilizer recommendations.
[0065] FIG. 2 illustrates a block diagram of the cloud platform 118 of the smart plant growth system 100, according to an embodiment of the present invention. The cloud platform 118 may comprise the computer-executable instructions in form of programming modules such as a data receiving module 200, a data processing module 202, a data generation module 204, and a data display module 206.
[0066] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the soil and environmental data from the microcontroller 114. The data receiving module 200 may be configured to establish a communication interface with the microcontroller 114 through a network communication protocol. The transmitted soil and environmental data may be received in the form of structured data packets through the communication interface. Further, the data receiving module 200 may be configured to decode and validate the received data packets and may store the received soil and environmental data in a temporary data repository such that the soil and environmental data may be made available for further processing by subsequent modules. The data receiving module 200 may be configured to transmit the soil and environmental data to the data processing module 202
[0067] In an embodiment of the present invention, the data processing module 202 may be activated upon receipt of the soil and environmental data from the data receiving module 200. The data processing module 202 may be configured to deploy a machine learning model for analysis of the received soil and environmental data. The data processing module 202 may be configured to load the machine learning model from a computational environment or storage resource and may initialize the machine learning model for data analysis operations. Further, the data processing module 202 may be configured to supply the received soil and environmental data as input parameters to the machine learning model such that the machine learning model may perform computational evaluation of the input data to determine soil nutrient condition and plant growth condition.
[0068] The machine learning model may be configured to process the received soil and environmental data through computational analysis techniques. The machine learning model may be configured to apply trained prediction algorithms to the received soil and environmental data such that patterns associated with soil nutrient condition and plant growth condition may be identified. Further, the machine learning model may be configured to evaluate correlations between the soil and environmental data parameters and agricultural growth indicators, thereby enabling determination of nutrient requirements associated with crop cultivation.
[0069] The machine learning model may further be configured to determine soil nutrient condition and plant growth condition based on the processed soil and environmental data. The machine learning model may be configured to evaluate relationships between nutrient concentration parameters and environmental parameters through trained predictive models. Further, the machine learning model may be configured to generate analytical outputs that represent estimated nutrient availability and growth suitability conditions associated with the agricultural field. The determined soil nutrient condition and plant growth condition may therefore form a computational basis for subsequent generation of the fertilizer recommendation.
[0070] The machine learning model may be trained using historical agricultural data including soil characteristics, crop type information, and environmental parameters. The historical agricultural data may include previously recorded soil nutrient values, environmental condition records, and crop growth outcomes obtained from agricultural datasets. The machine learning model may be configured to apply supervised training techniques in which the historical agricultural data may be utilized to establish predictive relationships between soil conditions and crop nutrient requirements. Through iterative model training and parameter optimization, the machine learning model may be adapted to generate predictive outputs that may assist in evaluation of soil nutrient condition and plant growth condition based on newly received soil and environmental data.
[0071] In an embodiment of the present invention, the machine learning model may be configured to generate crop-specific fertilizer recommendations based on crop type information included within the historical agricultural data. The data processing module 202 may provide crop type parameters along with the soil and environmental data as input parameters to the machine learning model. Based on the crop type parameters and the soil nutrient condition, the data generation module 204 may determine nitrogen requirement, phosphorus requirement, and potassium requirement associated with the specific crop cultivation conditions.
[0072] The data processing module 202 may be configured to relay the determined soil nutrient condition and plant growth condition to the data generation module 204.
[0073] In an embodiment of the present invention, the data generation module 204 may be activated upon receipt of the soil nutrient condition and plant growth condition from the data processing module 202. The data generation module 204 may be configured to determine a quantity of nitrogen, phosphorus, and potassium required for crop growth based on analysis of the soil and environmental data. The data generation module 204 may be configured to receive processed analytical outputs associated with soil nutrient condition and plant growth condition and may evaluate nutrient requirement parameters through computational decision logic. Further, the data generation module 204 may be configured to apply nutrient estimation rules or predictive outputs generated by the machine learning model such that quantitative values corresponding to nitrogen, phosphorus, and potassium requirements may be derived for the agricultural field. The determined nutrient quantities may therefore represent fertilizer input requirements associated with optimal crop growth conditions.
[0074] The data generation module 204 may be configured to generate the fertilizer recommendation. The data generation module 204 may be configured to utilize the determined nutrient requirement parameters associated with nitrogen, phosphorus, and potassium and may apply computational decision logic to derive the fertilizer recommendation corresponding to the agricultural field conditions. Further, the data generation module 204 may be configured to structure the fertilizer recommendation in a digital output format such that the fertilizer recommendation may represent suggested nutrient quantities or fertilizer composition suitable for crop growth based on the analyzed soil and environmental data. The fertilizer recommendation may include recommendations such as, but not limited to, nitrogen requirement, phosphorus requirement, potassium requirement, and so forth. The data generation module 204 may be configured to transmit the fertilizer recommendation to the data display module 206.
[0075] In an embodiment of the present invention, the data display module 206 may be activated upon receipt of the fertilizer recommendation from the data generation module 204. The data display module 206 may be configured to display the soil and environmental data and the fertilizer recommendation to the user using the web-based dashboard 122 installed in the computing unit 120. The data display module 206 may be configured to retrieve the processed soil and environmental data along with the generated fertilizer recommendation from internal data storage and may format the retrieved information into visual display elements. Further, the data display module 206 may be configured to transmit the formatted information to the web-based dashboard 122 through a network interface such that the soil and environmental data and the fertilizer recommendation may be visualized on the computing unit 120 for user observation and agricultural decision support.
[0076] FIG. 3 depicts a flowchart of a method 300 for the smart plant growth monitoring and the fertilizer recommendation, according to an embodiment of the present invention.
[0077] At step 302, the system 100 may collect the soil and environmental data through the sensor unit 102.
[0078] At step 304, the system 100 may transmit the collected soil and environmental data to the cloud platform 118 through the microcontroller 114 using the wireless communication unit 116.
[0079] At step 306, the system 100 may process the transmitted soil and environmental data through the machine learning model located in the cloud platform 118.
[0080] At step 308, the system 100 may determine the soil nutrient condition and plant growth condition based on the analysis of the soil and environmental data by the machine learning model.
[0081] At step 310, the system 100 may generate the fertilizer recommendation including the nitrogen requirement, the phosphorus requirement, and the potassium requirement based on the determined soil nutrient condition.
[0082] At step 312, the system 100 may display the soil and environmental data and the fertilizer recommendation on the web-based dashboard 122 for access by the user. , Claims:CLAIMS
I/We Claim:
1. A smart plant growth monitoring and fertilizer recommendation system (100), the system (100) comprising:
a sensor unit (102) configured to collect soil and environmental data including nitrogen level, phosphorus level, potassium level, soil moisture, temperature, humidity, and light intensity;
a microcontroller (114) configured to:
receive the soil and environmental data from the sensor unit (102); and
transmit the soil and environmental data through a wireless communication unit (116); and
a cloud platform (118) configured to receive the soil and environmental data transmitted from the microcontroller (114), characterized in that the cloud platform (118) is configured to:
receive the soil and environmental data from the microcontroller (114);
process the received soil and environmental data, using a machine learning model, and determine soil nutrient condition and plant growth condition;
generate a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and
display the soil and environmental data and the fertilizer recommendation to a user using a web-based dashboard (122).
2. The system (100) as claimed in claim 1, wherein the sensor unit (102) includes a Nitrogen, Phosphorus, Potassium (NPK) sensor (104), a soil moisture sensor (106), a temperature sensor (108), a humidity sensor (110), and a light intensity sensor (112).
3. The system (100) as claimed in claim 1, wherein the microcontroller (114) is configured to convert sensor signals received from the sensor unit (102) into digital data before transmission to the cloud platform (118).
4. The system (100) as claimed in claim 1, wherein the microcontroller (114) is an Espressif 32 (ESP32) microcontroller.
5. The system (100) as claimed in claim 1, wherein the wireless communication unit (116) comprise a Wi-Fi communication chip embedded with Long Range (LoRa) communication protocols.
6. The system (100) as claimed in claim 1, wherein the machine learning model is trained using historical agricultural data including soil characteristics, crop type information, and environmental parameters.
7. The system (100) as claimed in claim 1, wherein the cloud platform (118) is configured to determine a quantity of nitrogen, phosphorus, and potassium required for crop growth based on analysis of the soil and environmental data.
8. The system (100) as claimed in claim 1, wherein the web-based dashboard (122) provides real-time visualization of soil moisture, temperature, humidity, light intensity, and nutrient levels.
9. A method (300) for smart plant growth monitoring and fertilizer recommendation, the method (300) is characterized by steps of:
collecting soil and environmental data through a sensor unit (102);
transmitting the collected soil and environmental data to a cloud platform (118) through a microcontroller (114) using a wireless communication unit (116);
processing the transmitted soil and environmental data through a machine learning model located in the cloud platform (118);
determining soil nutrient condition and plant growth condition based on analysis of the soil and environmental data by the machine learning model;
generating a fertilizer recommendation including nitrogen requirement, phosphorus requirement, and potassium requirement based on the determined soil nutrient condition; and
displaying the soil and environmental data and the fertilizer recommendation on a web-based dashboard (122) for access by a user.
10. The method (300) as claimed in claim 9, wherein the sensor unit (102) includes a Nitrogen, Phosphorus, Potassium (NPK) sensor (104), a soil moisture sensor (106), a temperature sensor (108), a humidity sensor (110), and a light intensity sensor (112).
Date: March 16, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant