Crop Yield Prediction Using With The Simulated Annealing Convolutional Neural Networks (SACNNS) Optimization Algorithm

Abstract

This paper introduces an innovative approach for precise crop yield prediction in Tamil Nadu, India, a region with diverse agricultural conditions. The Simulated Annealing Convolutional Neural Networks (SACNNs) optimization algorithm is employed to enhance the performance of Convolutional Neural Networks (CNNs). By leveraging a comprehensive agricultural dataset that includes weather information, soil characteristics, and historical crop yields, SACNNs fine-tunes CNN architecture and parameters for improved prediction accuracy. The model excels at predicting crop yields at a local level, accommodating the unique agricultural conditions of different districts in Tamil Nadu. Experimental results highlight SACNNs' superiority in prediction accuracy and robustness compared to conventional CNNs and other optimization methods. These findings offer valuable insights for informed decision-making in agricultural planning and resource allocation, benefiting farmers, policymakers, and stakeholders in the region.