Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data DOI Creative Commons

Hao Ma,

Yarui Liu, Shijie Jiang

и другие.

Agronomy, Год журнала: 2025, Номер 15(5), С. 1094 - 1094

Опубликована: Апрель 29, 2025

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat can improve field management efficiency and optimize fertilization irrigation. Changes in characteristics of at different stages affect structure, leading to changes quality LiDAR point cloud (e.g., lower density, more noise points). Multispectral data capture these crop provide information about status wheat. Therefore, a method proposed that fuses features multispectral feature parameters estimate winter Low-altitude unmanned aerial systems (UASs) equipped with cameras were used collect from experimental fields during three key stages: green-up (GUS), jointing (JS), booting (BS). Analysis variance, variance inflation factor, Pearson correlation analysis employed extract significantly correlated height. Four estimation models constructed based on Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, Support Vector Regression models. The model training results showed OP-RF provided best performance across all coefficient determination values 0.921, 0.936, 0.842 GUS, JS, BS, respectively. root mean square error 0.009 m, 0.016 0.015 m. absolute 0.006 0.011 At same time, it was obtained fusing better than single type parameters. meet requirements prediction. These demonstrate fusion accuracy monitoring. provides valuable remote sensing phenotypic low densely planted crops also support assessment management.

Язык: Английский

Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data DOI Creative Commons

Hao Ma,

Yarui Liu, Shijie Jiang

и другие.

Agronomy, Год журнала: 2025, Номер 15(5), С. 1094 - 1094

Опубликована: Апрель 29, 2025

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat can improve field management efficiency and optimize fertilization irrigation. Changes in characteristics of at different stages affect structure, leading to changes quality LiDAR point cloud (e.g., lower density, more noise points). Multispectral data capture these crop provide information about status wheat. Therefore, a method proposed that fuses features multispectral feature parameters estimate winter Low-altitude unmanned aerial systems (UASs) equipped with cameras were used collect from experimental fields during three key stages: green-up (GUS), jointing (JS), booting (BS). Analysis variance, variance inflation factor, Pearson correlation analysis employed extract significantly correlated height. Four estimation models constructed based on Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, Support Vector Regression models. The model training results showed OP-RF provided best performance across all coefficient determination values 0.921, 0.936, 0.842 GUS, JS, BS, respectively. root mean square error 0.009 m, 0.016 0.015 m. absolute 0.006 0.011 At same time, it was obtained fusing better than single type parameters. meet requirements prediction. These demonstrate fusion accuracy monitoring. provides valuable remote sensing phenotypic low densely planted crops also support assessment management.

Язык: Английский

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