Abstract
In the agriculture farming business, weeds, pests, and other plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes a significant diminution in both quality and capacity of crop growing. Therefore, detection and taxonomy of various plants diseases are crucial, and it demands utmost attention. However, this loss can be minimized by detecting crops diseases at their earlier stages. In this article, we are primarily focusing on a cucumber leaf diseases detection and classification method, which is comprised of five stages including image enhancement, infected spots segmentation, deep features extraction, feature selection, and finally classification. Image enhancement is performed as a pre-processing step, which efficiently improves the local contrast and makes infected regions more visible, which is later segmented with a novel Sharif saliency-based (SHSB) method. The segmentation results are further improved by fusing active contour segmentation and proposed saliency method. This step is much important for correct and useful feature extraction. In this work, pre-trained models- VGG-19 & VGG-M are utilized for features extraction and later select the most prominent features based on three selected parameters - local entropy, local standard deviation, and local interquartile range method. These refined features are finally fed to multi-class support vector machine for diseases identification. To prove the authenticity of the proposed algorithm, five cucumber leaf diseases are considered and classified to achieve classification accuracy of 98.08% in 10.52 seconds. Additionally, the proposed method is also compared with the recent techniques so as to prove its authenticity.