Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.) DOI Creative Commons

Xiangtai Jiang,

Lutao Gao, Xingang Xu

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 15(1), P. 38 - 38

Published: Dec. 27, 2024

One of the most important nutrients needed for fruit tree growth is nitrogen. For orchards to get targeted, well-informed nitrogen fertilizer, accurate, large-scale, real-time monitoring, and assessment nutrition essential. This study examines Leaf Nitrogen Content (LNC) custard apple tree, a noteworthy that extensively grown in China’s Yunnan Province. uses an ensemble learning technique based on multiple machine algorithms effectively precisely monitor leaf content canopy using multispectral footage trees taken via Unmanned Aerial Vehicle (UAV) across different phases. First, shadows background noise from soil are removed UAV imagery by spectral shadow indices The noise-filtered then used extract number vegetation (VIs) textural features (TFs). Correlation analysis determine which pertinent LNC estimation. A two-layer model built quantitatively estimate stacking (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Decision Trees (GBDT), Linear Regression (LR), Extremely Randomized (ERT) among basis estimators integrated first layer. By detecting eliminating redundancy base estimators, Least Absolute Shrinkage Selection Operator regression (Lasso)model second layer improves According results, Lasso successfully finds redundant suggested approach, yields maximum estimation accuracy trees’ leaves. With root mean square error (RMSE) 0.059 absolute (MAE) 0.193, coefficient determination (R2) came 0. 661. significant potential UAV-based techniques tracking leaves highlighted this work. Additionally, approaches investigated might offer insightful information point reference remote sensing applications monitoring other crops.

Language: Английский

Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.) DOI Creative Commons

Xiangtai Jiang,

Lutao Gao, Xingang Xu

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 15(1), P. 38 - 38

Published: Dec. 27, 2024

One of the most important nutrients needed for fruit tree growth is nitrogen. For orchards to get targeted, well-informed nitrogen fertilizer, accurate, large-scale, real-time monitoring, and assessment nutrition essential. This study examines Leaf Nitrogen Content (LNC) custard apple tree, a noteworthy that extensively grown in China’s Yunnan Province. uses an ensemble learning technique based on multiple machine algorithms effectively precisely monitor leaf content canopy using multispectral footage trees taken via Unmanned Aerial Vehicle (UAV) across different phases. First, shadows background noise from soil are removed UAV imagery by spectral shadow indices The noise-filtered then used extract number vegetation (VIs) textural features (TFs). Correlation analysis determine which pertinent LNC estimation. A two-layer model built quantitatively estimate stacking (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Decision Trees (GBDT), Linear Regression (LR), Extremely Randomized (ERT) among basis estimators integrated first layer. By detecting eliminating redundancy base estimators, Least Absolute Shrinkage Selection Operator regression (Lasso)model second layer improves According results, Lasso successfully finds redundant suggested approach, yields maximum estimation accuracy trees’ leaves. With root mean square error (RMSE) 0.059 absolute (MAE) 0.193, coefficient determination (R2) came 0. 661. significant potential UAV-based techniques tracking leaves highlighted this work. Additionally, approaches investigated might offer insightful information point reference remote sensing applications monitoring other crops.

Language: Английский

Citations

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