Published Paper Details:

AgroBuddy, the paper presents an integrated framework using ML and DL to change agricultural decision-making completely. AgroBuddy is the result of three modules designed to tackle problems such as crop selection, soil nutrient management and plant disease detection, namely (a) Crop Recommendation, which gives the best crops depending on the soil and the climate, (b) Fertilizer Suggestion, offering the best fertilizing recommendations for improving soil nutrition and (c) Plant Disease Prediction, which utilizes CNN-based image analysis of plants to detect early signs of disease and make treatment recommendations. The algorithms used are Random Forest, XGBoost, and ResNet, and the data is processed based on soil properties, weather conditions, and crop health datasets. AgroBuddy was essentially designed to empower farmers with data-driven insights for improving productivity and sustainability while building resilience against climate & market volatility. The disease prediction model is experimented with and demonstrates very high accuracy across all modules with up to 99 % efficacy. The platform's technical, economic and operational analysis validates its feasibility to transform the traditional farming practice in India and beyond.