Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

Liyuan Zhang, Xiaoying Song, Yaxiao Niu (), Huihui Zhang, Aichen Wang, Yaohui Zhu, Xingye Zhu, Liping Chen and Qingzhen Zhu ()
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Liyuan Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaoying Song: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yaxiao Niu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Huihui Zhang: Water Management and Systems Research Unit, USDA-ARS, 2150 Centre Avenue, Bldg. D., Fort Collins, CO 80526, USA
Aichen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yaohui Zhu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xingye Zhu: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Liping Chen: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Qingzhen Zhu: High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Zhenjiang 212013, China

Agriculture, 2024, vol. 14, issue 3, 1-13

Abstract: As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R 2 ) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Keywords: plant nitrogen content; UAV RGB images; gabor filter; grey level co-occurrence matrix; winter wheat (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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