WATER AVAILABILITY-BASED CROP SELECTION AND ADVISORY SYSTEM FOR FARMERS USING MACHINE LEARNING.

Abstract

This invention introduces a mobile application designed to provide farmers with real-time crop selection advisories based on water availability, soil health, and weather conditions. Leveraging data from sources such as satellite imagery, local groundwater records, and rainfall forecasts, the system employs machine learning algorithms, specifically Long Short-Term Memory (LSTM) and Decision Trees, to generate location-specific crop recommendations. The application retrieves soil health data through REST APIs, updates rainfall forecasts via HTTP requests, and obtains water level information from government databases. By integrating these data points, the system predicts optimal crops, aiming to increase agricultural yields and promote sustainable farming practices. The application is user-friendly, supporting 11 languages, and offers an intuitive interface allowing farmers to register, input their GPS location, and receive tailored crop advisories. Additional features include soil health status reports, rainfall forecasting, and water availability information, all accessible in a mobile and web-based format developed with technologies like Flutter, Python, and Django. This invention addresses the critical gap in accessible, data-driven agricultural tools for rural farmers, promoting informed decision-making based on environmental and resource availability.

Specification

Description:
Field of the Invention:
The invention pertains to the field of computer science, specifically to agricultural technology utilizing machine learning, data analytics, and satellite data retrieval. It focuses on developing a mobile application to provide farmers with real-time insights into soil health, rainfall forecasts, groundwater levels, and crop selection advisories based on water availability, using algorithms like Long Short-Term Memory (LSTM) and Decision Trees.


Background of the Invention:
Agriculture is heavily dependent on water availability and soil health, which vary by location and season. Farmers often lack timely and accurate information about local rainfall, groundwater levels, and soil conditions, leading to poor crop selection and reduced yields. Traditional methods for gathering such data are time-consuming, inefficient, and prone to errors.

With advancements in machine learning, satellite data retrieval, and mobile technology, there is an opportunity to provide farmers with real-time, location-specific recommendations to optimize crop selection based on water availability and soil conditions. However, existing systems fail to integrate multiple data sources such as satellite data, local groundwater information, and weather forecasts in a user-friendly manner, especially for farmers in rural areas who may have limited access to technology.

Summary of the Invention:
The present invention is a mobile application designed to assist farmers in selecting suitable crops based on water availability, soil health, and weather conditions. The application retrieves data from various sources, including satellite imagery, local groundwater boards, and weather portals, to provide real-time, location-specific recommendations.

The system utilizes LSTM and Decision Tree algorithms to predict the most suitable crops based on water availability, soil health, and rainfall forecasts. Farmers can register and access the application in multiple languages, input their location via GPS, and receive a detailed advisory on which crops to plant.
Key features of the application include:
• Soil Health Status: Retrieval of state-wise and area-wise soil health data from the Soil Health Portal through REST API.
• Rainfall Forecasting: Real-time rainfall forecast updates via HTTP requests.
• Water Availability: Information on local surface and groundwater levels from the Central Water Board and the Ministry of Jal Shakti.
• Crop Selection Advisory: The application uses machine learning models (LSTM and Decision Tree) to recommend optimal crops based on the available water and soil conditions.

The system is designed to be user-friendly for farmers, offering support in 11 languages, and aims to improve crop yields by providing data-driven recommendations tailored to each farmer's specific conditions.


Brief Description of the Invention:
The invention consists of a mobile and web-based application developed using Flutter, Python, Django, JavaScript, and other modern technologies. Farmers register on the app, input their location through GPS, and receive detailed advisory based on soil health, water availability, and weather forecasts.

The app retrieves data from the following sources:
• Soil Health Portal: Provides state-wise and area-wise soil health data via REST API.
• Rainfall Forecast Portals: Updates rainfall data based on location.
• Central Water Board: Supplies local surface and groundwater levels.
• Crop Selection Dataset: Uses historical data on crop yields and water requirements to make predictions.

Machine learning models such as LSTM and Decision Trees are employed to analyze the collected data and predict the most suitable crop for planting. The app ensures ease of use by offering multi-language support and intuitive navigation.

Field of the Invention:
The invention pertains to the field of computer science, specifically to agricultural technology utilizing machine learning, data analytics, and satellite data retrieval. It focuses on developing a mobile application to provide farmers with real-time insights into soil health, rainfall forecasts, groundwater levels, and crop selection advisories based on water availability, using algorithms like Long Short-Term Memory (LSTM) and Decision Trees.


Background of the Invention:
Agriculture is heavily dependent on water availability and soil health, which vary by location and season. Farmers often lack timely and accurate information about local rainfall, groundwater levels, and soil conditions, leading to poor crop selection and reduced yields. Traditional methods for gathering such data are time-consuming, inefficient, and prone to errors.

With advancements in machine learning, satellite data retrieval, and mobile technology, there is an opportunity to provide farmers with real-time, location-specific recommendations to optimize crop selection based on water availability and soil conditions. However, existing systems fail to integrate multiple data sources such as satellite data, local groundwater information, and weather forecasts in a user-friendly manner, especially for farmers in rural areas who may have limited access to technology.

Summary of the Invention:
The present invention is a mobile application designed to assist farmers in selecting suitable crops based on water availability, soil health, and weather conditions. The application retrieves data from various sources, including satellite imagery, local groundwater boards, and weather portals, to provide real-time, location-specific recommendations.

The system utilizes LSTM and Decision Tree algorithms to predict the most suitable crops based on water availability, soil health, and rainfall forecasts. Farmers can register and access the application in multiple languages, input their location via GPS, and receive a detailed advisory on which crops to plant.
Key features of the application include:
• Soil Health Status: Retrieval of state-wise and area-wise soil health data from the Soil Health Portal through REST API.
• Rainfall Forecasting: Real-time rainfall forecast updates via HTTP requests.
• Water Availability: Information on local surface and groundwater levels from the Central Water Board and the Ministry of Jal Shakti.
• Crop Selection Advisory: The application uses machine learning models (LSTM and Decision Tree) to recommend optimal crops based on the available water and soil conditions.

The system is designed to be user-friendly for farmers, offering support in 11 languages, and aims to improve crop yields by providing data-driven recommendations tailored to each farmer's specific conditions.


Brief Description of the Invention:
The invention consists of a mobile and web-based application developed using Flutter, Python, Django, JavaScript, and other modern technologies. Farmers register on the app, input their location through GPS, and receive detailed advisory based on soil health, water availability, and weather forecasts.

The app retrieves data from the following sources:
• Soil Health Portal: Provides state-wise and area-wise soil health data via REST API.
• Rainfall Forecast Portals: Updates rainfall data based on location.
• Central Water Board: Supplies local surface and groundwater levels.
• Crop Selection Dataset: Uses historical data on crop yields and water requirements to make predictions.

Machine learning models such as LSTM and Decision Trees are employed to analyze the collected data and predict the most suitable crop for planting. The app ensures ease of use by offering multi-language support and intuitive navigation.
, Claims:We Claim:
1. A system for crop selection advisory, comprising:
• Retrieval of soil health data through a REST API from the Soil Health Portal.
• Real-time rainfall forecast updates via HTTP requests.
• Retrieval of local surface and groundwater data from the Central Water Board and the Ministry of Jal Shakti.
• Use of LSTM and Decision Tree algorithms to predict optimal crops based on water availability, soil conditions, and weather forecasts.

2. A method for providing location-specific agricultural recommendations, wherein:
• Farmers register on the app, input their location via GPS, and receive crop selection advisories based on real-time data.
• The system retrieves and processes data on soil health, water availability, and rainfall to provide personalized recommendations for crop planting.

3. A mobile application, developed using Flutter, Python, and Django, designed to assist farmers by offering multi-language support and seamless navigation for crop selection based on data-driven insights.

4. A method for integrating machine learning models, specifically LSTM and Decision Tree algorithms, to analyze data on soil health, water availability, and weather forecasts to make crop selection recommendations.

5. A method wherein farmers receive real-time notifications about changing weather conditions, water availability, and soil health status, ensuring timely and accurate decision-making.

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