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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #412284
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: Estimating winter wheat plant nitrogen content using spectral and texture features based on a low-cost UAV RGB system throughout the growing season

Author
item ZHANG, LIYUAN - Jiangsu University
item SONG, XIAOYING - Jiangsu University
item ZHU, QINGZHEN - Jiangsu University
item Zhang, Huihui
item WANG, AICHEN - Jiangsu University
item NIU, YAXIAO - Jiangsu University

Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2024
Publication Date: 3/11/2024
Citation: Zhang, L., Song, X., Zhu, Q., Zhang, H., Wang, A., Niu, Y. 2024. Estimating winter wheat plant nitrogen content using spectral and texture features based on a low-cost UAV RGB system throughout the growing season. Agriculture. 14(3). Article 456.. https://doi.org/10.3390/agriculture14030456.
DOI: https://doi.org/10.3390/agriculture14030456

Interpretive Summary: This research investigates the use of low-cost UAV RGB systems in estimating winter wheat plant nitrogen content throughout the growing season. By integrating spectral and texture features with machine learning algorithms, such as support vector machine (SVM), the study increased the accuracy of plant nitrogen content estimation. Improvements in nitrogen management contributes to national grain security and supports sustainable farming. The findings show substantial impact of utilizing affordable technology for precise nitrogen fertilization management.

Technical Abstract: As the prior information for precise nitrogen fertilization management, plant nitrogen content (PNC) which is obtained timely and accurately through a low-cost way is of great significance for the national grain security and sustainable social development. In this study, the potential of the low-cost UAV RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were taken as the complement to the commonly used spectral information, and five machine learning regression algorithms, namely classification and regression tree (CART), K-Nearest Neighbor (KNN), artificial neural network (ANN), support vector machines (SVM) and random forest (RF), were adopted to establish the bridge between UAV RGB images derived features and ground-truth PNC, taking the multivariate linear regression (MLR) algorithm as reference. The results show that both UAV RGB images derived 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 value 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) decreased from 2.56 to 2.24 g/kg, taking the SVM regression algorithm as an example. The SVM regression algorithm with validation achieved the highest estimation accuracy R2 of 0.62 and 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.