A Machine Vision-Based Online Apple Grading System Toward In-Field Sorting

Abstract

Highlights A screw-conveyor-based machine vision system was evaluated for online apple grading. The developed vision pipeline enabled multi-view-based size estimation and comprehensive surface defect inspection. Apple sizing accuracy exceeded 95.9% across conveyor speeds of 1–2 apples s-1 per conveyor lane. The system achieved sample-level grading accuracies of up to 95.4%, demonstrating its potential for further integration and in-field validation. ABSTRACT. Apple quality grading is a critical operation in postharvest handling; however, most existing grading systems are designed for controlled packinghouse environments rather than in-field operation at harvest, which can achieve substantial cost savings for both growers and packers and improve postharvest inventory management. To address the need for in-field apple grading technology, building on our prior work, this study developed a machine vision-based apple grading system toward in-field sorting by integrating a screw-conveyor-based fruit handling mechanism with automated defect inspection and size estimation. The system enables continuous fruit transportation and rotation, allowing multi-view image acquisition for comprehensive surface assessment. The vision module comprises an enclosed image chamber equipped with uniform LED illumination and a top-mounted RGB-D (red-green-blue-depth) camera, ensuring stable and consistent quality of acquired imagery. A computer vision-based pipeline was developed to detect, track, and segment individual apples for surface defect evaluation and sizing. Multi-view images acquired during fruit rotation were fused to achieve full surface coverage. In addition, a geometry-based diameter estimation method integrating stem/calyx-aware boundary localization was introduced to improve fruit sizing robustness and accuracy. Experimental results demonstrate diameter estimation accuracy exceeding 95.9% and maintained sample-level grading accuracies of 95.4%, 94.2%, and 92.3% at conveyor speeds of 1, 1.5, and 2 apples s -1 per lane, respectively. These results demonstrate that the proposed system can support rapid apple grading and provide a practical step toward in-field fruit sorting. Both the dataset and software programs of this study has been made publicly available. Keywords: Apple, In-field grading, Machine vision, Multi-view imaging, Online inspection.

Plant phenotyping relevance

リンゴのサイズ推定と表面欠陥評価を行う画像ベースのオンライン表現型取得・選別システムを開発し、精度検証しているため、植物フェノタイピング手法が中心である。

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