Research on Intelligent Diagnosis of Crop Leaf Diseases and Interpretable Report Generation Based on Multi-Task Collaborative Learning

Currently, intelligent crop disease diagnosis based on multi-task learning is a prominent research focus in smart agriculture. This approach utilizes a shared feature extraction network to simultaneously handle multiple tasks-including disease identification and severity classification-effectively enhancing the model's generalization capability and diagnostic accuracy. By incorporating attention mechanisms, optimizing network architectures (e.g., integrating CNNs with Transformers), and implementing dynamic weight adjustment techniques, the study achieves synergistic optimization across tasks. Experimental results demonstrate that the multi-task model outperforms traditional single-task models in recognition accuracy, lesion detection, and segmentation precision. This technology provides an efficient and reliable solution for precision agriculture, advancing the intelligent transformation of agricultural production.