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

Heguang Sun, Lin Zhou, Meiyan Shu, Jie Zhang, Ziheng Feng, Haikuan Feng, Xiaoyu Song, Jibo Yue () and Wei Guo ()
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Heguang Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Lin Zhou: College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China
Meiyan Shu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Jie Zhang: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China
Ziheng Feng: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China
Haikuan Feng: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China
Xiaoyu Song: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China
Jibo Yue: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

Agriculture, 2024, vol. 14, issue 3, 1-18

Abstract: 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).

Keywords: peanut southern blight; SMOTE; hyperspectral reflectance; machine learning; FOD (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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