Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions

Liyuan Zhang, Aichen Wang, Huiyue Zhang, Qingzhen Zhu, Huihui Zhang, Weihong Sun and Yaxiao Niu ()
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Liyuan Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Aichen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Huiyue Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Qingzhen Zhu: 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
Weihong Sun: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yaxiao Niu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2024, vol. 14, issue 7, 1-17

Abstract: The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R 2 : 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R 2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

Keywords: chlorophyll content; UAV multispectral images; machine learning regression; winter wheat; model generalization ability (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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