Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data DOI Creative Commons
Jigme Thinley, Catherine Marina Pickering, Christopher E. Ndehedehe

et al.

GEOMATICA, Journal Year: 2025, Volume and Issue: unknown, P. 100047 - 100047

Published: Jan. 1, 2025

Language: Английский

Citations

1
Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning DOI Creative Commons
Yafeng Li, Xingang Xu,

Wenbiao Wu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4479 - 4479

Published: Nov. 29, 2024

Nitrogen is the main nutrient element in growth process of white radish, and accurate monitoring radish leaf nitrogen content (LNC) an important guide for precise fertilization decisions field. Using LNC as object, research on hyperspectral estimation methods was carried out based field sample data at multiple stages using feature selection integrated learning algorithm models. First, Vegetation Index (VI) constructed from data. We extracted sensitive features VI response to Pearson’s feature-selection approach. Second, a stacking-integrated approach proposed machine algorithms such Support Vector Machine (SVM), Random Forest (RF), Ridge K-Nearest Neighbor (KNN) base model first layer architecture, Lasso meta-model second realize LNC. The analysis results show following: (1) bands are mainly centered around 600–700 nm 1950 nm, VIs also concentrated this band range. (2) Stacking with spectral inputs achieved good prediction accuracy leaf, R2 = 0.7, MAE 0.16, MSE 0.05 estimated over whole stage radish. (3) variable filtering function chosen meta-model, which has redundant model-selection effect helps improve quality framework. This study demonstrates potential method stages.

Language: Английский

Citations

1