基于计算机视觉技术的番茄叶部病害识别

Recognition of Tomato Foliage Disease Based on Computer Vision Technology

以计算机视觉技术为手段,结合图像处理和模式识别技术,研究了番茄早疫病、晚疫病、叶霉病和棒孢叶斑病等4种叶部病害的自动识别方法。建立了一套适用于室内操作的图像采集处理系统,可进行病害样本图像的采集、预处理和病斑区域的分割。提取了每个病斑区域的9个颜色参数、5个纹理参数和4个形状参数,同时采用逐步判别与贝叶斯判别相结合和主成分分析与费歇尔判别相结合的两种方法实现特征参数的提取和判别模型的构建。逐步判别从提取的18个特征参数中选择了12个参数用于构建贝叶斯判别模型,结果对训练样本和测试样本的识别准确率分别达到100%和94.71%。主成分分析则将18个特征参数综合成2个新变量,构建的费歇尔判别函数对样本的总体识别准确率为98.32%。两种方法均获得了较好的分类效果,说明利用计算机视觉技术可以实现对番茄叶部病害的快速、准确识别,为实现番茄病害的田间实时在线检测提供了可能。

Computer vision combined with digital image processing and pattern recognition techniques were evaluated for the detection of diseased tomato leaves infected with leaf mold（Fulvia fulva）,early bligh（tAlternaria solani）,late blight（Phytophthora infestans）,and leaf spot（Corynespora cassiicola）.An image acquisition system was established to acquire leaf images.The image pre-processing techniques were applied to segment the lesion regions from the diseased leaves.And then nine color characteristics,five texture characteristics and four shape characteristics of the lesion regions were extracted.To classify the four kinds of tomato foliage diseases,stepwise discriminant analysis combined with Bayes discriminant analysis and principal component analysis combined with Fisher discriminant analysis were executed to develop the discriminant models.By the stepwise discriminant analysis,we selected 12 characteristics from the original 18 variables to develop the Bayes discriminant function, and results showed that the classification accuracies for the training and testing sets achieved 100% and 94.71% respectively.By principal component analysis,the 18 variables were reduced to two principal components（PCs）.The classification model based on the two PCs achieved classification accuracy of 98.32%.These results indicated that it is feasible to identify and classify tomato diseases using computer vision technology.This preliminary study, which was done in a closed room with restrictions to avoid interference of the field environment, showed that there is a potential to establish an online field application in tomato diseases detection based on computer vision and image processing techniques.