Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat

The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Agronomy (Basel) Jg. 15; H. 7; S. 1621
Hauptverfasser:	Han, Yu, Zhang, Jiaxue, Bai, Yan, Liang, Zihao, Guo, Xinhui, Zhao, Yu, Feng, Meichen, Xiao, Lujie, Song, Xiaoyan, Zhang, Meijun, Yang, Wude, Li, Guangxin, Yang, Sha, Qiao, Xingxing, Wang, Chao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.07.2025
Schlagworte:	Accuracy Agricultural production Agriculture Algorithms Correlation analysis Crop growth Data mining Design of experiments Ensemble learning Environmental conditions Grouting Image processing Image segmentation leaf nitrogen content Learning Learning strategies Leaves Low altitude Machine learning Monitoring Monitoring methods multispectral Multispectral analysis Nitrogen Performance evaluation Plot (Narrative) Precision agriculture Remote sensing Root-mean-square errors Sensors Spectral reflectance Support vector machines Triticum aestivum Unmanned aerial vehicles Vegetation Vegetation index Wheat Winter Winter wheat China
ISSN:	2073-4395, 2073-4395
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!