关键词: 番茄, 复杂环境, 滑动窗口, 病害识别, CCL-SVM

Abstract: As affected by the complex background of images from the complex environment, it is difficult to recognize disease from images of tomato leaves. This research intended to classify the most common tomato diseases of powdery mildew, early blight, and Liriomyza sativae using leaf images captured by mobile phonesina greenhouse environment. A new method of CCLSVM was designed by fusion color and texture feature and SVM method. A sliding window was applied to cut an original image to small region images to support small region image data set and high recognize performance under complex environments. Small region images of tomato leaves were selected as training and validation samples. After extract color and texture features from subimages, the SVM method was used to classify early blight, powdery mildew, Liriomyza sativae, and healthy leaf four classes. Experiment results showed that recognition accuracy was significantly improved from 60.63% to 97.5% after introducingcolor and texture features. Compared with deep learning methods, the recognition accuracy of CCLSVM wasalso higher than VGG16 and Alexnet, and the test time of a single frame was much lower than that of a deep learning network. It was considered that the designed CCLSVM method could greatly reduce the effect from the background environment, and also with low computation cost and system requirement.

Key words: tomato, complex environment, sliding window, disease recognition, CCLSVM