This study addresses key challenges in optimizing agricultural industry structures and facilitating intelligent transformation through the application of deep learning algorithms and advanced optimization techniques. An intelligent system for agricultural industry optimization is developed, with convolutional neural networks, recurrent neural networks, Long Short-Term Memory networks, and generative adversarial networks introduced for tasks such as image recognition, time series forecasting, and synthetic data generation. Subsequently, a hybrid optimization method is designed, combining the Genetic Algorithms with particle swarm optimization to improve the model’s global search capability and local convergence speed. The performance of these techniques is rigorously evaluated through extensive experimentation. The results demonstrate that the proposed method outperforms conventional algorithms in regression tasks, particularly in terms of computational efficiency, data processing speed, and model training stability, while also exhibiting high scalability. In crop yield prediction, the proposed method achieves superior performance, as evidenced by reductions in both absolute error and mean squared error, along with attaining the highest R
2
value (0.93). Additionally, in pest and disease detection, the proposed method exceeds other models in accuracy (97.5%), precision (96.8%), recall (97.2%), and F1 score (0.97), underscoring its superior performance in detecting agricultural pests and diseases. The method also significantly surpasses traditional algorithms in crop disease identification accuracy, climate change prediction precision, and the quality of synthetic data generation. This study offers novel technical solutions and decision-making tools for advancing intelligent agriculture.
