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.