Climate Change Perception and Adaptation Strategies for Crop Growth with CNN and BiGRU Models

Population increase has already caused arable land shortages in the agricultural business. Sustainable farming strategies that increase crop yields without harming the environment or depleting natural resources are needed to meet rising food demand. Since climate change will affect different regions differently, filling the knowledge gap will help policymakers, decision-makers, and farmers achieve sustainable agriculture. The suggested method includes preprocessing, feature selection, and model training. Preprocessing prepares data for relevant analysis. Optimize the model's features and use the Pairwise Separability Index and Global Separability Index during feature selection to improve performance. For model training, ABiCNRN is employed. The suggested model outperforms CNN and RNN with a 94.69% average score. This improvement shows how well the suggested technique improves agricultural forecasts and decisions. A realistic alternative is presented to better predict agricultural output and encourage sustainable agriculture. The method uses advanced preprocessing, feature selection, and model training to improve resource management and agricultural issues.