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Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

文献类型： 外文期刊

作者： Sun, Heguang 1 ; Zhou, Lin 3 ; Shu, Meiyan 1 ; Zhang, Jie 2 ; Feng, Ziheng 2 ; Feng, Haikuan 2 ; Song, Xiaoyu 2 ; Yue, Jibo 1 ; Guo, Wei 1 ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100094, Peoples R China

3.Henan Agr Univ, Coll Plant Protect, Zhengzhou 450002, Peoples R China

关键词： peanut southern blight; SMOTE; hyperspectral reflectance; machine learning; FOD

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2024 年 14 卷 3 期

页码：

收录情况： SCI

摘要： Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant's interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75).

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