Hyperspectral Imaging-Based Deep Learning Method for Detecting Quarantine Diseases in Apples.

Abstract

Rapid detection of quarantine diseases in apples is essential for import-export control but remains difficult because routine inspections rely on manual visual checks that limit automation at port scale. A fast, non-destructive system suitable for deployment at customs is therefore needed. In this study, three common apple quarantine pathogens were targeted using hyperspectral images acquired by a close-range hyperspectral camera and analyzed with a convolutional neural network (CNN). Symptoms of these diseases often appear similar in RGB images, making reliable differentiation difficult. Reflectance from 400 to 1000 nm was recorded to provide richer spectral detail for separating subtle disease signatures. To quantify stage-dependent differences, average reflectance curves were extracted for apples infected by each pathogen at early, middle, and late lesion stages. A CNN tailored to hyperspectral inputs, termed HSC-Resnet, was designed with an increased number of convolutional channels to accommodate the broad spectral dimension and with channel and spatial attention integrated to highlight informative bands and regions. HSC-Resnet achieved a precision of 95.51%, indicating strong potential for fast, accurate, and non-destructive detection of apple quarantine diseases in import-export management.

Plant phenotyping relevance

リンゴ病害の症状をハイパースペクトル画像とCNNで検出・識別する方法が研究の中心であり、植物の病害状態を直接推定している。

Code and data availability

The supplied article blocks describe a hyperspectral imaging dataset of 1628 apple images and an HSC-ResNet model, but contain no data availability statement, code deposit, public repository link, or author-provided URL for the dataset, images, or analysis code. The only URLs present are the ORCID of an author and the

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