Apple Spots and Defects Detection Based on Machine Vision, Fuzzy Systems, and Improved Gray Wolf Optimization Algorithm

One of the most important agricultural products that are produced in Iran and has a very high use is the apple-tree, which unfortunately sometimes suffers due to the existence of defects and surface defects. The quality control of apple trees before their export is of special importance as a strategic product in the country. Machine vision is one of the new methods in the automatic classification of apple trees, whose algorithms include three stages cutting the image of the apple from the background, extracting the image, and finally examining the presence of defects in the cut image of the apple. In the existing methods for apple-tree image segmentation, assuming that the background of the image is known in advance, apple-tree segmentation is easily done with the help of simple thresholding algorithms. Various methods in the field of image processing and machine vision have been presented for the quality control of this pure product based on image processing and machine vision, each of which has its advantages and disadvantages. Soft calculations were done to detect apple tree defects. The main goal of this chapter is to design a suitable system for detecting surface defects in apple trees. To analyze and check the accuracy of the proposed system, the above-mentioned parameters were applied to COFILAB database images with 100 images. The efficiency of the proposed method was evaluated by comparing it with two high-efficiency methods based on three measurement parameters. The results indicated that using the proposed method with an 87% correct detection rate compared to the SVM-PSO method with 80% and Features of Color with 84%, has improved the efficiency of the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.