Nondestructive Identification of Litchi Downy Blight at Different Stages Based on Spectroscopy Analysis

Jun Li, Junpeng Wu, Jiaquan Lin, Can Li, Huazhong Lu and Caixia Lin
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Jun Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Junpeng Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jiaquan Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Can Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Huazhong Lu: Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Caixia Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2022, vol. 12, issue 3, 1-17

Abstract: Litchi downy blight caused by Peronophythora litchii is the most serious disease in litchi production, storage and transportation. Existing disease identification technology has difficulty identifying litchi downy blight sufficiently early, resulting in economic losses. Thus, the use of diffuse reflectance spectroscopy to identify litchi downy blight at different stages of disease, particularly to achieve the early identification of downy blight, is very important. The diffuse reflectance spectral data of litchi fruits inoculated with P. litchii were collected in the wavelength range of 350–1350 nm. According to the duration of inoculation and expert evaluation, they were divided into four categories: healthy, latent, mild and severe. First, the SG smoothing method and derivation method were used to denoise the spectral curves. Then, the wavelength screening methods competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were compared to verify that the SPA method was more effective. Eleven characteristic wavelengths were selected, accounting for only 1.1% of the original data. Finally, the characteristic wavelengths were tested by six different classification models, and their accuracy was calculated. Among them, the ANN model performed best, with an accuracy of 90.7%. The results showed that diffuse reflectance spectroscopic technology has potential for identifying litchi downy blight at different stages, providing technical support for the subsequent development of related automatic detection devices.

Keywords: litchi downy blight; spectroscopy analysis; SG smoothing; CARS; SPA; classification models (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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