Cross-environment stable leaf canopy skewness-kurtosis indices: Developing transferable biometric correlates for agroclimatic phenotyping models

作者：Zhengmeng Chen, Chunwei Liu, Fuzheng Wang, Hongyan Wu, Jibo Zhang, Jianfei Li, Xiaodan Xie, Yile Zhang, Rijun Lai, Yitong Zhou, Zhengyi Yao, Peng Wang, Pei Zhang · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101291 · 被引用次数：1 · 研究领域：Remote Sensing in Agriculture、Forest ecology and management、Leaf Properties and Growth Measurement

This study proposes a novel, robust and scalable approach for color phenotype modeling, providing a new research perspective for crop phenotype analysis. The color gradation skewness-distribution (CGSD) parameters, as a color characterization indicator that can be widely applied to different crops and ecological environments, possess significant theoretical value and practical application potential. Specifically, we explore the relationship between accumulated temperature—the primary heat factor driving crop development—and crop canopy leaf color, aiming to identify canopy color parameters that can consistently describe crop responses to environmental changes. In this study, we developed and tested inversion models for predicting accumulated temperature in wheat and tobacco crops grown in both laboratory and natural environments across various ecological regions. These models utilized color gradation skewness-distribution parameters derived from digital canopy images. Our results show that some inversion models can predict accumulated temperature responses with high accuracy, achieving 88.95% accuracy for wheat and 77.38% for tobacco. Statistical analysis revealed that, compared to models using parameters related to color depth, those incorporating parameters related to leaf color distribution as independent variables provided more consistent predictions across crops from different ecological regions. This can be explained from the fact that the leaf color distribution parame...