数字农科院2.0

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

文献类型： 外文期刊

作者： Hang Zhang;Naibo Ye;Jingru Gong;Huajie Xue;Peihao Wang;Binbin Jiao;Liping Yin;Xi Qiao

作者机构：

关键词： （2-1-2）apple disease detection;deep learning;hyperspectral imaging;import-export quarantine management

期刊名称： Foods

ISSN： 2304-8158

年卷期： 2025 年 14 卷 18 期

页码：

收录情况： SCIE（2025版）

摘要： 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.

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