Authors

Abstract

The key to diagnosing the types and degree of apple leaf diseases is to correctly segment apple leaf disease spots. Therefore, in order to effectively solve the problem of poor segmentation of leaves and diseased areas, the U2Net semantic segmentation network model was used in the research of apple leaf disease identification and disease diagnosis, and compared with the classic semantic segmentation network model DeepLabV3+ and UNet. In addition, the effects of different learning rates (0.01, 0.001, 0.0001) and optimizers (Adam, SGD) on the performance of U2Net network model were compared and analyzed. The experimental results showed that the learning rate is 0.001 and the optimizer is Adam, the average pixel accuracy (MPA) and mean intersection over union (MIoU) of the research model for lesion segmentation reach 98.87% and 84.43%, respectively. The results of this study were expected to provide the theoretical basis for the precise control of apple leaf disease.

Abstract in Chinese

由于苹果叶片病斑的准确分割是识别苹果叶部病害类别及病害等级划分的关键。因此，为有效解决叶片及病斑区域分割效果不佳的问题，提出将U2Net 语义分割网络模型用于苹果叶片病害识别及病害诊断的研究，并与经典语义分割网络模型DeepLabV3+和UNet进行对比分析。此外，分析了学习率、优化器对表现效果最佳的U2Net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98.87%和84.43%。该研究结果以期为苹果叶片病害的精准化防治提供理论依据。