Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning

Wei Guo, Heguang Sun, Hongbo Qiao, Hui Zhang, Lin Zhou, Ping Dong () and Xiaoyu Song ()
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Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Heguang Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Hongbo Qiao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Hui Zhang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Lin Zhou: College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China
Ping Dong: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Xiaoyu Song: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China

Agriculture, 2023, vol. 13, issue 8, 1-15

Abstract: Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF 770~780 , 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight.

Keywords: peanut southern blight; reflection spectrum; spectral index; continuous wavelet transform; machine learning (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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