Recognition of phomopsis vexans in solanum melongena based on leaf disease spot features

Abstract: Phomopsis vexans is one of the most devastating diseases of Solanum melongena. Early detection and prevention of crop diseases is critical to control the diseases, improve crop yields, reduce the economic losses and control pesticide pollution. Therefore, the research of recognition methods for crop diseases is necessary. This paper proposed a disease recognition method of phomopsis vexans in Solanum melongena, based on leaf disease spot features. In this method, computer vision technology was used as a means of digital image processing and pattern recognition technique, focusing on analysis of the diseased leaf spots of color, shape, texture parameters. Diseased sample image was collected through an image acquisition system which composed of FitPC and server with long-distance point-to-point transmission. The collected diseased leaf images were processed using a series of image pre-processing methods, such as image transforming, smoothing, and segmentation. After removing disturbance of noise with a median filter and excluded non-blade portion with Grabcut algorithm, the preprocessed image was obtained. Since the H-values of preprocessed image in the HSI color space were concentrated within a certain range, the threshold preprocessed image was chosen as the background of the diseased leaf image. The image segmentation method, based on the result of the background and the preprocessed image multiplication, was applied to separate the disease spot images from the diseased leaf images. The twelve color characteristic parameters, eleven shape feature parameters and eight texture feature parameters for each disease spot area, i.e. the 31 disease unions classifying features were extracted by statistical analysis. The feature vectors consisted of twenty strong classification feature parameters, which were selected by the variance and principal component analysis methods. Based on the training set that composed of 35 phomopsis vexans spot feature vectors and feature vectors of 35 other disease of Solanum melongena spot, Fischer discriminant function classification which use to classify the testing set was constructed. The recognition results of the kinds of phomopsis vexans by the proposed method were 90%. Under the premise of not changing the identify objects, to improve the accuracy of recognition, it required to consider of the influence of different classification feature vectors and different samples of training set on the recognition results. The results of control experiment showed that, the identification accuracy rate decreased with the reduction of the training set, and the identification accuracy of Fisher classification discriminant function constructed by feature vectors dropped without characteristic optimization. The causes of these results were that the sample of other disease of Solanum melongena contained phomopsis vexans similar diseases and redundancy parameters were presented in the original classification feature vetor. The analysis and experimental results in this paper demonstrate that before constructing discriminant function, enough training samples should be acquired and it's nesessary to select the effective parameters that identified well from the feature parameters. The proposed method indicated that using computer vision technology could realize the rapid and accurate identification of leaf diseases of Solanum melongena, and provide supporting technology to real-time detection on solanum melongena diseases in open field. This paper only studied crop leaf disease, while diseases of the stem and fruit were not involved, which remains to be further studied.