Preliminary development of a new multispectral vision-based, automated apple grading system towards in-field fruit presorting

Abstract

While Machine vision technology has been widely implemented for fruit quality inspection at packing line facilities, but appropriate in-orchard pre-sorting technology has yet to be developed. This study represents a novel effort to leverage advanced real-time multispectral vision coupled with artificial intelligence to develop a new, automated apple grading system that inspects size, color, and surface defects simultaneously, towards in-orchard application. The system consists of a multispectral imaging chamber on a compact screw conveyor, which acquires five-band images from singulated apples traveling and rotating on the conveyor. Online experiments are conducted on different varieties of apples in diverse quality conditions at different conveyor speeds. A deep learning-based computer vision algorithm pipeline is developed to segment and track each apple on the conveyor while assessing its quality attributes (size, color, and surface defects) from different, multiple views, and grade the fruit based on full-surface quality information into three quality categories. The system will evolve into a fully integrated machine prototype for automated, in-orchard sorting.

Plant phenotyping relevance

リンゴ果実のサイズ・色・表面欠陥という器官形質を、マルチスペクトル画像と深層学習で取得・評価する自動システムの開発が中心であり、単なる農業実験の routine 測定ではない。

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