Apple Disease Detection Using Deep Learning

Abstract

This paper proposes a technique for detecting and diagnosing diseases in apple fruits using image processing and deep learning methods. The proposed technique involves a series of steps, including input image acquisition, pre-processing, adaptive filtering with median filter, image enhancement, morphological analysis, fuzzy clustering, segmentation using k-means algorithm, feature extraction with Gray level co-occurrence matrix (GLCM), and classification with a deep convolutional neural network (CNN) classifier. The output of the proposed technique is the detection and classification of various diseases in apple fruits. The proposed technique is implemented using MATLAB 2018a Version and tested with several datasets. The results demonstrate that the proposed technique can effectively detect and diagnose diseases in apple fruits with high accuracy and can be used as a valuable tool in the agricultural industry.