Discriminant Feature Extraction using Disease Segmentation for Automatic Leaf Disease Diagnosis

Abstract

Plant leaf disease is a serious issue in agriculture worldwide. Image processing-based leaf disease detection and diagnosis approaches are becoming popular. The distinguishable features of leaf image are important to improve the performance of disease detection and diagnosis model. However, proper segmentation of the diseased part of a leaf helps to extract discriminant features. From this point of view, this paper presents an efficient leaf disease diagnosis model, where discriminant features are extracted from disease segmented leaf images using optimal segmentation algorithms. In this paper, the disease and healthy parts of the leaf are separated using segmentation techniques. After that, different distinguishable features are extracted based on color and intensity value of image pixels of leaf image. Finally, the Multiclass Support vector machine (MC-SVM) with RBF Gaussian kernel is used for detecting and diagnosis the leaf disease. To validate the proposed model, an online benchmarked leaf images dataset named PlantVillage-Dataset is used. The performance evaluation of the proposed model shows a satisfactory result of disease detection performance. Moreover, three well-known image segmentation algorithms are used and an optimal segmentation method is investigated to find better accuracy of disease diagnosis. According to the experimental result, the optimal results are observed for the leaf disease diagnosis using the Otsu thresholding-based disease segmentation.