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Early Detection and Dynamic Grading of Sweet Potato Scab Based on Hyperspectral Imaging

文献类型： 外文期刊

作者： Ning, Xiaosong 1 ; Xia, Qiyao 1 ; Tang, Fajiang 1 ; Ding, Ziyu 1 ; Ding, Xiawei 1 ; Zeng, Fanguo 4 ; Wang, Zhangying 1 ; Zou, Hongda 1 ; Yue, Xuejun 2 ; Huang, Lifei 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Crops Res Inst, Guangdong Prov Key Lab Crop Genet Improvement, Guangzhou 510640, Peoples R China

2.South China Agr Univ, Coll Elect Engn, Guangzhou 510642, Peoples R China

3.South China Agr Univ, Coll Agr, Guangzhou 510642, Peoples R China

4.Zhejiang Univ Water Resources & Eletr Power, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China

关键词： hyperspectral imaging; sweet potato scab; early detection; spectral analysis; dynamic grading

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 4 期

页码：

收录情况： SCI

摘要： This study investigates the early detection of sweet potato scab by using hyperspectral imaging and machine learning techniques. The research focuses on developing an accurate, economical, and non-destructive approach for disease detection and grading. Hyperspectral imaging experiments were conducted on two sweet potato varieties: Guangshu 87 (resistant) and Guicaishu 2 (susceptible). Data preprocessing included denoising, region of interest (ROI) selection, and average spectrum extraction, followed by dimensionality reduction using principal component analysis (PCA) and random forest (RF) feature selection. A novel dynamic grading method based on spectral-time data was introduced to classify the early stages of the disease, including the early latent and early mild periods. This method identified significant temporal spectral changes, enabling a refined disease staging framework. Key wavebands associated with sweet potato scab were identified in the near-infrared range, including 801.8 nm, 769.8 nm, 898.5 nm, 796.4 nm, and 780.5 nm. Classification models, including K-nearest neighbor (KNN), support vector machine (SVM), and linear discriminant analysis (LDA), were constructed to evaluate the effectiveness of spectral features. Among these classification models, the MSC-PCA-SVM model demonstrated the best performance. Specifically, the Susceptible Variety Disease Classification Model achieved an overall accuracy (OA) of 98.65%, while the Combined Variety Disease Classification Model reached an OA of 95.38%. The results highlight the potential of hyperspectral imaging for early disease detection, particularly for non-destructive monitoring of resistant and susceptible sweet potato varieties. This study provides a practical method for early disease classification of sweet potato scab, and future research could focus on real-time disease monitoring to enhance sweet potato crop management.

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