Cotton aphid infestation monitoring using Sentinel-2 MSI imagery coupled with derivative of ratio spectroscopy and random forest algorithm

Abstract

Aphids are one of the main pests of cotton and have been an important disaster limiting cotton yield. It is important to use satellite multispectral data to monitor the severity of cotton aphids in a timely and accurate manner on regional scale. Based on the combination of derivative of ratio spectra (DRS) and random forest (RF) algorithm, this study researched the quantitative monitoring model of cotton aphid severity based on Sentinel-2 data. First, the cotton area was extracted by using a supervised classification algorithm and the vegetation index threshold method. Then, the DRS algorithm was used to analyze the spectral characteristics of cotton aphids from three scales, and the Pearson correlation analysis algorithm was used to extract the bands significantly related to aphid infestation. Finally, the RF model was trained by ground sampling points and its accuracy was evaluated. The optimal model results were selected by the cross-validation method, and the accuracy was compared with the four classical classification algorithms. The results showed that (1) the canopy spectral reflectance curves at different grades of cotton aphid infestation were significantly different, with a significant positive correlation between cotton aphid grade and spectral reflectance in the visible band range and a negative correlation in the near-infrared band range; (2) The DRS algorithm could effectively remove the interference of the background endmember of satellite multispectral image pixels and enhance the aphid spectral features. The analysis results from three different scales and the evaluation results demonstrate the effectiveness of the algorithm in processing satellite multispectral data; (3) After the DRS processing, Sentinel-2 multispectral images could effectively classify the severity of cotton aphid infestation by the RF model with an overall classification accuracy of 80% and a kappa coefficient of 0.73. Compared with the results of four classical classification algorithms, the proposed algorithm has the best accuracy, which proves the superiority of RF. Based on satellite multispectral data, the DRS and RF can be combined to monitor the severity of cotton aphids on a regional scale, and the accuracy can meet the actual need.

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

“…Some studies focus on developing crop-specific spectral indices [27,28]. In addition, some studies have analyzed different spectral transformation forms, such as logarithms, derivatives, and continuous wavelet transforms, to enhance the separability of spectra under different severity levels [29,30]. In order to ascertain the spectral response mechanism of plants with peanut southern blight, we have employed the Continuous Wavelet Transform (CWT) technique.…”

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

“…For A. gossypii infestation class identification, other researchers have more often studied multispectral ( Zeng et al., 2021 ; Fu et al., 2022 ) and hyperspectral ( Feng and Liu, 2020 ) cotton images collected by unmanned aerial vehicle (UAV), while our study was based on cotton images collected by smartphones. The methods of the two approaches are different; the cotton images collected by UAV can qualitatively determine the degree of A. gossypii infestation at a macro level, with the degree of A. gossypii infestation reflected by the spectral curve characteristics of the cotton canopy, while the cotton images taken by smartphones in this paper can quantitatively determine the degree of A. gossypii infestation of a single cotton plant, with more accurate results.…”

Cotton yield estimation using several vegetation indices

“…Based on the controlled classification algorithm of satellite images, cotton fields were identified in the area [33][34]. Hancong [35] calculated the percentage of cotton area using satellite imagery. By dividing the number of cotton pixels by the total area of the field, they obtained the percentage of cotton area that was strongly correlated with yield.…”