Quantitative Analysis of Vertical and Temporal Variations in the Chlorophyll Content of Winter Wheat Leaves via Proximal Multispectral Remote Sensing and Deep Transfer Learning

Changsai Zhang (), Yuan Yi, Shuxia Zhang and Pei Li
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Changsai Zhang: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yuan Yi: Jiangsu Xuhuai Regional Institute of Agricultural Science, Xuzhou 221131, China
Shuxia Zhang: School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China
Pei Li: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Agriculture, 2024, vol. 14, issue 10, 1-25

Abstract: Quantifying the vertical distribution of leaf chlorophyll content (LCC) is integral for a comprehensive understanding of the physiological status and function of winter wheat crops, having significant implications for crop management and yield optimization. In this study, we investigated the vertical LCC trait of winter wheat during two consecutive field growth seasons using proximal multispectral imaging measurements to evaluate vertical variations of LCC within winter wheat canopies. The results revealed the non-uniform vertical LCC distribution varied across the entire growth season. The effects of nitrogen fertilization rate on LCC among vertical layers increased gradually from upper to lower layers of canopy. To enhance LCC prediction accuracy, this study proposes a deep transfer learning network model for leaf trait estimation (LeafTNet). It integrates the advantages of physical radiative transfer simulations with deep neural network through transfer learning. The results demonstrate that the LeafTNet achieved remarkable predictive performance and strong robustness. Furthermore, the proposed method exhibits superior estimation accuracy compared to empirical statistical method and traditional machine learning method. This study highlights the performance of LeafTNet in accurately and efficiently quantifying LCC from proximal multispectral data, which provide technical support for the estimation of the vertical distribution of leaf traits and improve crop management.

Keywords: multispectral; chlorophyll content; vertical distribution; transfer learning; 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
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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