Summary
FarmPredict 360 is a full-stack agricultural intelligence web application developed on the MERN stack — MongoDB, Express.js, React.js, and Node.js — extended with a Python FastAPI microservice that integrates LangChain and the Groq large language model API. The platform delivers three core capabilities to farmers, traders, and agribusinesses across Telangana, India. The Crop Advisor module accepts soil nutrient parameters and GPS coordinates, retrieves live weather data from the OpenWeather API, and applies the Llama3-70b large language model to recommend the top three most suitable crops with agronomic justifications, yield estimates, and fertilizer guidance. The Price Forecasting module predicts tomorrow’s minimum and maximum market prices and generates a seven-day forward forecast for a
Regional applicability
This study was conducted in Telangana, India, and focuses on Indian crop varieties, APMC market structures, and local agro-climatic conditions. Direct application to United Kingdom farming would require retraining models on UK-relevant crop disease datasets, domestic market price patterns, and soil classification systems; the underlying technical architecture and decision-support logic may be transferable, but validation on UK crops, pests, and farm types would be necessary.
Key measures
CNN classification accuracy (92.4% overall accuracy across 38 disease classes); response generation time for LLM-based advisory (under 3 seconds average); geographic coverage (10 Telangana districts and APMC markets)
Outcomes reported
The study reports development and validation of FarmPredict 360, a web-based agricultural intelligence platform delivering three core capabilities: crop recommendation based on soil and weather data, market price forecasting, and CNN-based plant disease detection. Experimental validation showed CNN disease detection achieved 92.4% accuracy across 38 disease classes, with LLM-based advisory responses generated in under three seconds.
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