Pear leaf disease segmentation method based on improved DeepLabv3+

Agricultural modernization urgently requires precise control of pear tree leaf diseases, with accurate identification and segmentation of disease spots becoming crucial aspects for ensuring crop health. Addressing potential issues such as missed segmentation, missegmentation, and low segmentation accuracy in the traditional DeepLabv3+ model for this task, this study proposes an innovative approach based on an improved DeepLabv3+ network model. To enhance computational efficiency, MobileNetV2 is introduced as the backbone network. This reduces the model's computational load and significantly improves segmentation speed, making it more suitable for real-time applications. Secondly, after the ASPP (Atrous Spatial Pyramid Pooling) convolution, the Squeeze-and-Excitation (SE) attention mechanism is introduced, integrating the features of pear tree leaf diseases to make the network focus more on the critical components of disease spots, thereby enhancing segmentation accuracy. Finally, the loss function is optimized by employing a linear combination of the cross-entropy loss function and Dice loss, encompassing comprehensive overall image loss and accurate loss calculation for the target area. Experimental validation demonstrates a significant improvement in the enhanced DeepLabv3+ across metrics such as MIoU, mPA, mPr, and mRecall, reaching 86.32%, 88.97%, 91.10%, and 88.97%, respectively. The improved model excels in pear tree leaf disease segmentation compared to traditional methods like DeepLabv3+, SegNet, FCN, and PSPNet. This confirms the enhanced model's outstanding performance and generalization ability in segmenting pear tree leaf lesions, highlighting its potential value in practical applications.