Manuscript received April 25 2024; revised June 19, 2024; accepted July 5, 2024; published October 25, 2024

Abstract—This study sheds light on the evolution of the agricultural industry and highlights advances in production area. The salient recognition of fruit size and shape as critical quality parameters underscores the significance of the research. In response to this challenge, the research introduces specialized image processing techniques designed to streamline the sorting of apples in agricultural settings, specifically emphasizing accurate apple width estimation. A purpose-built machine was designed, featuring an enclosure box housing a cost-effective camera for the vision system and a chain conveyor for classifying Malus domestica Borkh kind apples. These goals were successfully achieved by implementing image preprocessing, segmentation, and measurement techniques to facilitate sorting. The proposed methodology classifies apples into three distinct classes, attaining an impressive accuracy of 94% in Class 1, 92% in Class 2, and 86% in Class 3. This represents an efficient and economical solution for apple classification and size estimation, promising substantial enhancements to sorting processes and pushing the boundaries of automation in the agricultural sector.

Keywords—agriculture, Open Source Computer Vision (OpenCV), apple, sorting, width estimation

Cite: Andrea Pilco, Viviana Moya, Angélica Quito, Juan P. Vásconez, and Matías Limaico, "Image Processing-Based System for Apple Sorting," Journal of Image and Graphics, Vol. 12, No. 4, pp. 362-371, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.