Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion

Abstract

Efficient and fast segmentation of fruit symptoms is one of the major businesses nowadays in the agro-economy. Manual segmentation and recognition of fruit symptoms is a hard job because of many aspects such like time-consuming and waste of money. Lately, several scholars came up with image processing and pattern recognition based methods for segmentation and recognition of fruit symptoms based on their features such as color, texture, and shape. In this article, we proposed an automated system for segmentation and recognition of grape leaf diseases. The proposed system comprises of four main steps. In first step, a local contrast haze reduction (LCHR) enhancement technique is proposed for increasing the local contrast of symptoms. Thereafter, LAB color transformation is held in the second step and the best channel is selected based on the pixels information that is later utilized into thresholding function. Color, texture, and geometric features are extracted and fused by canonical correlation analysis (CCA) approach. At the time of features fusion, a noise is added in the form of irrelevant and redundant features that are removed by Neighborhood Component Analysis (NCA). The classification of final reduced features is then performed by M-class SVM. The introduced system is assessed on Plant Village dataset of three types of grape leaf diseases such as black measles, black rot, and leaf blight including healthy. The proposed method acquired an average segmentation accuracy rate of 90% and classification accuracy is above 92% which is superior in contrast of existing techniques.