Diseased Area Segmentation Using a Novel Gray-Scale Thresholding Algorithm and Classification Using a New Deep CNN Model for Apple Fruit Sorting - Henila, M (International Journal of Computer Sciences and Engineering, 2025)
A novel Gray-Scale Thresholding Method [GSTM] for segmenting the region of interest and a Deep Convolutional Neural network model for the apple fruit sorting system has been proposed in this paper. First, the GSTM method converts the acquired colour image into a grayscale image and calculates the threshold value using the Gray pixel values. The acquired colour apple image was then segmented using the calculated threshold value to extract the diseased/defective part alone for further processing. Second, a Deep Convolutional Networking model was designed to classify the apple images as sound or diseased/defective apple images. The result obtained using the GSTM was compared with similar Grayscale thresholding methods like Otsu and Kapur. It was found that GSTM’s execution time was less and the visual segmentation was good compared to the other two methods in extracting the diseased/defective area. The Deep Convolutional Network using GSTM segmented images gave a classification/sorting accuracy rate of 91.67%.