Abstract
•A contrast stretching technique is implemented to improves diseased contrast.•A novel correlation coefficient segmentation with statistical measures is proposed.•A deep architecture is implemented for feature extraction and later selection.•A genetic algorithm is performed for optimization of max features.
In the agriculture farming business, plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes significant diminution in both quality and capacity of growing crops. Therefore, detection and taxonomy of various plants diseases is crucial, and it demands utmost attention. In plants, fruits act a major source of nutrients worldwide, however, various range of diseases adversely affect the production as well as the quality of the fruits. Therefore, utilization of an efficient machine vision technology not only detects the diseases at their early stages but also classify them accordingly. This research is primarily focusing on the detection and classification of various fruits diseases based on correlation coefficient and deep features (CCDF). The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification. In the first step, initially contrast of input image is enhanced by utilizing a hybrid method - followed by proposed correlation coefficient-based segmentation method which separates the infected regions from the background. In the second step, two deep pre-trained models (VGG16, caffe AlexNet) are utilized for feature extraction of selected diseases (apple scab, apple rot, banana sigotka, banana cordial leaf spot, banana diamond leaf spot and deightoniella leaf and fruit spot). Parallel features fusion step is embedded to consolidate the extracted features prior to max-pooling step. Selection of most discriminant features are being performed using genetic algorithm before subjecting to the final stage of classification using mutli-class SVM. Experiments are being performed on publicly available datasets - plant village and CASC-IFW datasets to achieve the classification accuracy of 98.60%. Qualitative analysis of achieved results clearly shows that the proposed method outperforms several existing methods in terms of greater precision and improved classification accuracy.