Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

Bingquan Tian, Hailin Yu, Shuailing Zhang, Xiaoli Wang, Lei Yang, Jingqian Li, Wenhao Cui, Zesheng Wang, Liqun Lu, Yubin Lan and Jing Zhao ()
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Bingquan Tian: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Hailin Yu: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Shuailing Zhang: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Xiaoli Wang: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Lei Yang: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Jingqian Li: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Wenhao Cui: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Zesheng Wang: Shandong-Binzhou Cotton Technology Backyard, Binzhou 256600, China
Liqun Lu: Shandong-Binzhou Cotton Technology Backyard, Binzhou 256600, China
Yubin Lan: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Jing Zhao: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China

Agriculture, 2024, vol. 14, issue 9, 1-18

Abstract: In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R 2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R 2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Keywords: UAV multispectral images; cotton SPAD; image segmentation; mixed pixel decomposition (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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