Influencing factors analysis plays an important role in plant disease identification. This paper explores the key influencing factors and severity recognition of pear diseases using deep learning based on our established pear disease database (PDD2018), which contains 4944 pieces of diseased leaves. Using the deep learning neural networks, including VGG16, Inception V3, ResNet50 and ResNet101, we developed a “DL network + resolution” scheme that can be used in influencing factors analysis and diseases recognition at six different levels. The experimental results demonstrated that the resolution is directly proportional to disease recognition accuracy and training time and the recognition accuracies for pear diseases are up to 99.44%,98.43%, and 97.67% for Septoria piricola (SP), Alternaria alternate (AA), and Gymnosporangium haracannum (GYM), respectively. The results also shown that a forward suggestion on disease sample collection can significantly reduce the false recognition accuracy.

影响因素分析在植物病害鉴定中起着重要作用。本文基于我们建立的梨病数据库（PDD2018），其中包含4944片病叶，通过深度学习探索了梨病的关键影响因素和严重性识别。使用包括VGG16，Inception V3，ResNet50和ResNet101在内的深度学习神经网络，我们开发了一种“ DL网络+解决方案”方案，该方案可用于六个不同级别的影响因素分析和疾病识别。实验结果表明，分辨率与疾病识别的准确性和训练时间成正比，梨病的识别精度可达99 。44％， 98 。43％和97。分别为皮氏霉菌（SP），链格孢菌（AA）和裸子草（GYM）的67％。结果还表明，对疾病样本收集的前瞻性建议可以大大降低错误识别的准确性。