基于深度学习的小麦条锈病病害等级识别

目的 为提高小麦条锈病危害程度分级精度，开展小麦条锈病病害等级自动化、准确、快速识别方法研究。方法 在复杂田间条件下，使用手机拍摄图像，构建含有不同等级条锈病的小麦叶片数据集，利用GrabCut与YOLOv5s相结合的方法进行小麦叶片与复杂背景自动化分割。为了增强ResNet50对表型特征的提取能力，增加Inception模块，依据划分的小麦条锈病病害等级标准，对小麦条锈病病害等级进行识别。采用准确率、查全率、查准率等评价指标分析改进的ResNet50模型(B-ResNet50)在数据集上的表现。结果 GrabCut与YOLOv5s相结合对大田复杂背景下的小麦叶片图像实现了自动、准确、快速地分割。B-ResNet50识别小麦条锈病叶片的平均准确率为97.3%，与InceptionV3(87.8%)、DenseNet121(87.6%)、ResNet50(88.3%)相比，准确率大幅提升，比原始模型(ResNet50)高出9个百分点。结论 利用深度学习对小麦条锈病病害等级进行识别，对防治小麦条锈病的精准施药具有重要意义，可为田间复杂条件下小麦条锈病的防治提供技术支持。

Grade classification of wheat stripe rust disease based on deep learning

Objective In order to improve the grade classification accuracy of damage degree by wheat stripe rust, the automatic, accurate and rapid identification method of damage degree by wheat stripe rust was studied.Method Under complex field conditions, images were taken by mobile phones, and data sets of wheat leaves with different grades of stripe rust were constructed. The combination of GrabCut and YOLOv5s was used to automatically segment wheat leaves from complex background. The Inception module was added to enhance the ability of ResNet50 in extracting phenotypic features. The disease grades of wheat stripe rust were identified according to the classified disease grade standards of wheat stripe rust. The performance of the improved ResNet50 model (B-ResNet50) on the data set was analyzed using evaluation indexes such as accuracy, recall and precision. Result Wheat leaf images were segmented automatically, accurately and quickly by the combination of GrabCut and YOLOv5s under complex background in the field. The recognition rate of B-ResNet50 on the data set of wheat stripe rust leaves was 97.3%, which was obviously higher than that of InceptionV3 (87.8%), DenseNet121 (87.6%) and ResNet50 (88.3%). The accuracy rate was greatly improved, and nine percentage points more than that of the original model(ResNet50). Conclusion Using deep learning to identify the disease grade of wheat stripe rust is of great significance to applying accurate pesticide for its control, and provides technical support for the control of wheat stripe rust under complex field conditions.