Authors

Abstract

In view of the obvious differences in the manifestations of the same diseases in apples at different stages of the disease, different diseases show certain similarities, and the early symptoms of the disease are not obvious. For these problems, a new model attention residual network (ARNet) was introduced based on the combination of attention and residual thought. The model introduces the multi-layer attention modules to solve the problems of early disease location dispersion and features that are difficult to extract. In order to avoid network degradation, a residual module was constructed to effectively integrate high and low-level features, and data augment technology was introduced to prevent the model from over-fitting. The proposed model (ARNet) achieved an average accuracy of 99.49% on the test set of 4 kinds of apple leaf diseases with real complex backgrounds. Compared with the models ResNet50 (99.19%) and MobileNetV2 (98.17%), it had better classification performance. The model proposed in this paper had strong robustness and high stability and can provide a reference for the intelligent diagnosis of apple leaf diseases in practical applications.

Abstract in Chinese

苹果同种病害在不同发病阶段表征差异明显，不同病害又表现出一定的相似性，且在病害早期症状不明显。针对该问题，在ResNet的基础上引入注意力机制提出一种新的模型（ARNet）。通过引入多层注意力模块，层次化提取病害分类信息，解决早期病害部位分散、特征难以提取等问题，为避免网络训练出现退化现象，构建残差模块有效融合高低阶特征，同时引入数据扩充技术以防止模型过拟合。研究表明，提出的模型(ARNet)在4种具有真实复杂背景的苹果叶病害测试集上的平均准确率达到99.49%，与现有模型ResNet50（99.19%）和MobileNetV2（98.17%）相比，ARNet具有更好的分类效果。本文提出的模型具有较强鲁棒性和较高稳定性，在实际应用中可为苹果病害智能诊断提供参考。