Online multi-view multispectral detection for early bruised apple

Abstract

Online multispectral dynamic inspection is crucial for smart agriculture, particularly in acquiring multispectral image data across the entire surface of fruits during the inspection process. This study focuses on early bruises in apples, presenting an online multispectral multi-surface imaging strategy. The proposed strategy is based on an imaging model using two side mirrors, combined with an imaging sensor with a lens-filter array. This configuration enables the rapid capture of spatial texture and multispectral information from the multiple viewing directions for a sample in a single imaging process of one CCD. During the design process, a monochromatic LED-based integrating sphere optical system is introduced to uniformly illuminate the entire surface of the apple samples. Based on this, a mathematical model is established for the side mirror layout and system geometric parameters to determine the system configuration that scans the sample surface. In practical applications, the proposed method achieved an effective classification rate of 91 % for three quality categories of apples—sound, slightly bruised, and severely bruised—at a detection speed of about 3 per second. These results suggest that this study provides potential technical support for apple quality monitoring in smart agriculture.

Plant phenotyping relevance

リンゴ果実の打撲状態を対象に、多視点マルチスペクトル撮像システムと画像取得戦略を設計・評価しており、植物状態の取得方法が研究の中心である。

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