DL Powered Seasonal Crop Recommendation Using Weather Prediction and Soil Analysis for Sustainable Agriculture in the Cauvery Delta Region

Tamil Nadu, India’s Cauvery Delta Zone is highly fertile and is referred to as the “Land of Paddy Cultivation.” However, agriculture in the Cauvery Delta Zone is extremely fragile due to climate change, erratic precipitation, and climatic factors. Farmers in this scenario need to employ advanced crop-suggesting technologies and smart agriculture techniques. An AI-driven paradigm for sustainable development is proposed by this study in order to maximize agricultural productivity and make effective use of resources. A GRU-based weather prediction model is used in the study to estimate current and future weather conditions for a maximum of one year using historical weather data. Using a range-based mapping technique, the forecasted weather is shown against crop data and soil compositions unique to the Cauvery delta region. The method suggests appropriate crops tailored to the soil nutrients and crop requirements of the Cauvery delta region based on the weather forecast. Three seasons-Kharif, Rabi, and All seasons-are used to group the recommended crops. For weather forecasting one year in advance, the GRU model demonstrated improved prediction accuracy of $\mathbf{9 3 \%}$. Using information from the public works department and an authorized soil testing laboratory, the suggested system offers accuracy and dependability.