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基于机器学习的不同信源小麦叶部病害单病斑图像识别
Machine-learning-based recognition of single lesion images of wheat leaf diseases acquired by using different photographic equipments

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梁佳慧蒋倩王红丽王海光 ^*

文摘	小麦条锈病、叶锈病和白粉病是小麦叶部重要病害,严重影响小麦的产量和品质,其及时识别和诊断对于病害管理具有重要意义。现在植物病害图像获取更加便捷,但在实际病害识别中难以统一图像来源,因此,探索不同来源图像对病害识别的影响非常重要。本研究使用三种不同图像获取设备(手机iPhone 5s、手机iPhone 11、数码照相机Nikon D700)在田间获取小麦条锈病、叶锈病和白粉病图像共计1 354张,利用Adobe Photoshop CS6中的套索工具结合切片工具获得条锈病单病斑图像,利用切片工具结合K-means聚类算法获得叶锈病和白粉病的单病斑图像,共分割获得单病斑图像9 479张。提取单病斑图像的164个颜色、形状、纹理特征后,利用ReliefF和聚类特征选择方法选择36个特征作为建模特征组合,分别基于支持向量机(Support vector machine,SVM)、K最近邻算法(K-nearest neighbor,KNN)、高斯朴素贝叶斯算法、多层感知机(Multilayer perceptron,MLP)、标签传播算法和自训练半监督算法,结合利用随机搜索方法获得的不同训练集下的最优参数/参数组合构建病害识别模型,以准确率、精确率、召回率和F1 score作为指标评价所建模型识别效果。结果表明,基于单一图像来源的训练集,所建病害识别SVM模型、MLP模型、自训练半监督模型对建模所用训练集和与建模所用训练集相同图像来源的测试集的识别准确率、精确率、召回率和F1 score均较高,除了基于由iPhone 5s获取的图像组成的训练集构建的病害识别自训练半监督模型对与建模所用训练集相同图像来源的测试集和由iPhone 11获取的图像组成的测试集的识别准确率、精确率、召回率和F1 score分别相差不大外,对另外两个不同图像来源的测试集的识别准确率、精确率、召回率和F1 score均有所降低。基于由不同来源图像组成的训练集,所建病害识别SVM模型、MLP模型和自训练半监督模型具有较好的识别效果,所建病害识别KNN模型的识别效果尚可,识别效果最优的病害识别SVM模型对不同图像来源测试集的识别准确率、精确率、召回率和F1 score最低为91.31%。结果表明,不同来源图像对病害识别具有影响,基于由不同来源图像组成的训练集构建病害识别模型可以获得对多信源图像较好的识别效果。本研究为实现小麦病害的自动化和智能化识别奠定了一定基础。
其他语种文摘	Stripe rust (yellow rust) caused by Puccinia striiformis f.sp.tritici,leaf rust caused by P.triticina,and powdery mildew caused by Blumeria graminis f.sp.tritici are important wheat leaf diseases,which seriously affect the yield and quality of wheat.Timely identification and diagnosis of the diseases are very important for their effective management.Now it is more convenient to acquire plant disease images,but it is difficult to unify image sources in actual disease identification,thus it is very important to explore the effects of images from different sources on image-based plant disease identification.In this study,a total of 1 354 images of wheat stripe rust,wheat leaf rust and wheat powdery mildew (including individual disease images and mixed-infected leaf images) were obtained in the field by using three different image acquisition devices including a smartphone iPhone 5s,a smartphone iPhone 11,and a digital camera Nikon D700.The single lesion images of wheat stripe rust were obtained by using the lasso tool in the software Adobe Photoshop CS6 in combination with the slice tool,and the single lesion images of wheat leaf rust and wheat powdery mildew were obtained by using the slice tool in combination with the K-means clustering algorithm.A total of 9 479 segmented single lesion images were obtained,and then from them,164 color,shape,and texture features were extracted.By using ReliefF and clustering feature selection methods,a feature combination consisting of 36 features was determined for building disease recognition models.Four training sets,three composed of the images from the three individual sources,respectively,and one composed of the images from the three different sources,were constructed.Based on each training set,disease recognition models were built by using six modeling methods including support vector machine (SVM),K-nearest neighbor (KNN),Gussian naive Bayesian,multilayer perceptron (MLP),label propagation algorithm,and self-training semi-supervised algorithm,with the optimal parameter/parameter combination obtained by using the random search method.Accuracy,precision,recall,and F1 score were used to evaluate the disease recognition performance of the built models.The results showed that,for the SVM,MLP,and self-training semi-supervised models built based on each training set consisting of the individual-source images,high accuracies,precisions,recalls,and F1 scores were achieved on both the training set used for modeling and the testing set with the same image source as the training set used for modeling,however,the corresponding values achieved on the two other testing sets with the different image sources as the training set used for modeling reduced,except that the accuracy,precision,recall,and F1 score of the testing set with the same image source as the training set used for modeling and the corresponding values of the testing set consisting of the images acquired by using the smartphone iPhone 11 were not much different for the self-training semi-supervised model built based on the training set consisting of the images acquired by using the smartphone iPhone 5s.Based on the training set consisting of the images from the three different sources,satisfactory recognition performances were achieved by using the built SVM,MLP,and self-training semi-supervised models;acceptable recognition performance was achieved by using the built KNN model;and the built SVM model was optimal,with the accuracies,precisions,recalls and F1 scores not less than 91.31% on all the testing sets.The results indicated that different image sources can affect image-based disease recognition and demonstrated that satisfactory recognition performance on multi-source images can be achieved by using the model built based on the training set consisting of images from different sources.In this study,some basis was provided for further studies on the automatic and intelligent recognition of wheat diseases.
来源	植物病理学报 ,2023,53(5):905-921 【核心库】
DOI	10.13926/j.cnki.apps.000891
关键词	小麦病害 ; 图像识别 ; 机器学习 ; 多信源图像 ; 病斑
地址	中国农业大学植物保护学院, 北京, 100193
语种	中文
文献类型	研究性论文
ISSN	0412-0914
学科	植物保护
基金	国家自然科学基金项目 ; 国家重点研发计划项目
文献收藏号	CSCD:7616496

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