Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

Bingru Hou, Yaohua Hu, Peng Zhang and Lixia Hou
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Bingru Hou: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Yaohua Hu: College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Peng Zhang: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Lixia Hou: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China

Agriculture, 2022, vol. 12, issue 7, 1-17

Abstract: Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period.

Keywords: disease classification; machine learning; partial least squares; physicochemical values; potato (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: 2022
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