Li Lili, Wang Bin, Li Yanwen, Yang Hua
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Plants (Basel). 2023 Feb 9;12(4):786. doi: 10.3390/plants12040786.
The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP) was proposed to achieve effective extraction of apple leaf lesion features and to improve the apple leaf disease recognition and disease severity diagnosis compared with the classical semantic segmentation network models PSPNet and GCNet. In addition, the effects of the learning rate, optimizer, and backbone network on the performance of the DeepLabV3+ network model with the best performance were analyzed. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the model reached 97.26% and 83.85%, respectively. After being deployed into the smartphone platform, the detection time of the detection system was 9s per image for the portable and intelligent diagnostics of apple leaf diseases. The transfer learning method provided the possibility of quickly acquiring a high-performance model under the condition of small datasets. The research results can provide a precise guide for the prevention and precise control of apple diseases in fields.
苹果树叶病斑的准确分割是识别苹果树叶病害分类和病害严重程度的关键。因此，提出了一种带有深度可分离卷积空间金字塔池化模块（ASPP）的DeepLabV3+语义分割网络模型，以实现对苹果树叶病斑特征的有效提取，并与经典语义分割网络模型PSPNet和GCNet相比，提高苹果树叶病害识别和病害严重程度诊断能力。此外，还分析了学习率、优化器和骨干网络对性能最佳的DeepLabV3+网络模型性能的影响。实验结果表明，该模型的平均像素准确率（MPA）和平均交并比（MIoU）分别达到了97.26%和83.85%。在部署到智能手机平台后，该检测系统对苹果树叶病害进行便携式智能诊断时，每张图像的检测时间为9秒。迁移学习方法为在小数据集条件下快速获取高性能模型提供了可能。研究结果可为田间苹果病害的预防和精准防治提供精确指导。
