Development of a portable online nondestructive detection device for apple watercore based on visible/near-infrared spectroscopy.

Abstract

Visible and near-infrared (Vis/NIR) spectroscopy has been widely applied in fruit quality detection due to its advantages of rapid efficiency, non-invasiveness, and suitability for detecting opaque samples. To address the issue of whether apple watercore occurs during the growth and maturation of apples, a portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore. The device employs the AIOX2000-13 spectrometer as the detection unit, with an STM32F103VET6 ARM-based processor as the main control chip, and integrates a 4G wireless communication module to establish a stable data transmission channel between the processor and the computer. This structure ensures the efficient and stable transmission of apple spectral data and detection results, thereby meeting the need for in-field nondestructive detection of apple watercore on apple trees. The system is based on a self-designed spectral data acquisition mechanism and uses a transmission detection method to collect spectral data from 500 'Fuji' apple samples in two directions. The spectral data were preprocessed using Standard Normal Variate (SNV), and the dataset was divided using the Spectral Projection based on X-Y distances (SPXY) algorithm. Important feature wavelengths related to apple watercore were extracted by combining the Uninformative Variable Elimination method with the Successive Projections Algorithm (UVE-SPA). Subsequently, a detection model, SNV-UVE-SPA-SVM, was constructed using a Support Vector Machine (SVM) optimized by the Honey Badger Algorithm (HBA), achieving a test set accuracy of 96%. After research and analysis, Direction 1 was identified as the optimal acquisition direction, and field verification was conducted on 50 apple samples, with a detection accuracy of 94%. The results show that the detection device has the advantages of portability, high efficiency, and suitability for in-field detection, making it suitable for the rapid in-field detection of apple watercore.

Plant phenotyping relevance

リンゴの水心症という植物状態を対象に、可視・近赤外分光による携帯型非破壊検出装置と解析モデルを開発し、圃場検証まで実施しており、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe a portable Vis/NIR apple watercore detection device and its SNV-UVE-SPA-SVM model, but contain no data availability statement, no public spectral dataset, no author code/model repository, and no public URL beyond the Creative Commons license link. The 500-sample spectral data and MATLAB-emb

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