Development of a Compact Automatic Sorting Machine for Kazakhstani Apple Varieties Based on Computer Vision

Abstract

This article presents the development and experimental study of a compact, automated apple sorting machine based on computer vision, indirect fruit weight estimation, and color grading. It is designed for use in small and medium-sized farms, where the use of industrial lines is limited by high cost and complex maintenance. The proposed machine enables non-destructive assessing and sorting of apples in a continuous flow mode without the use of mechanical weighing, includes a fruit feeding and positioning module, a computer vision system, an image processing unit, and a sorting actuator synchronized with the conveyor movement. Fruit weight and color assessment is based on visual geometric parameters (diameter, fruit height, projected area, and the proportion of surface color), extracted from digital images, followed by classification by product category using regression models. As part of the experimental study, a correlation analysis was conducted between the actual weight of apples and their geometric parameters for five varieties typical of Kazakhstan. It was shown that the projected fruit area exhibits the most stable correlation with weight, justifying its use as the primary predictor in constructing a regression model for indirect weight estimation. An assessment of the accuracy of apple classification by product categories was carried out and the influence of conveyor speed on the stability and correctness of sorting was observed. The experimental results with a total 1250 apples of five varieties - Aport Alexander, Sinap Almaty, Kazakhski Yubileinyi, Ainur, and Nursat indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s. In this mode, sorting throughput is approximately 400 kg/hour, with an average accuracy of 92% for the automatic classification in accordance with GOST requirements. These results confirm that the proposed approach provides sufficient real-time sorting accuracy with a simple machine design. The machine can be used as a standalone sorting solution, as well as a base platform for further expansion of functionality by integrating surface defect assessment and grade identification modules.

Plant phenotyping relevance

果実の形態・色・重量を画像から推定し分類するコンピュータビジョン方式と装置の開発が中心であり、単なる品質測定ではなく、再利用可能な植物器官形質の取得・推定法を提示している。

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