Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model

Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model

Unmanned aerial vehicles (UAVs) allow field monitoring of crop water status using multimodal UAV data. However, the accuracy and evaluation of predictive models in large-scale applications remain challenging issues. We conducted a two-year field experiment on spring maize in the Hetao Irrigation Dis...

Full description

Saved in:
Bibliographic Details
Main Authors: Qi Liu, Xiaolong Hu, Yiqiang Zhang, Liangsheng Shi, Wei Yang, Yixuan Yang, Ruxin Zhang, Dongliang Zhang, Ze Miao, Yifan Wang, Zhongyi Qu
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Agricultural Water Management
Subjects:
Unmanned aerial vehicle
Multimodal
Water stress
Plant moisture content
Large-scale application
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S0378377425001210
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S0378377425001210

Similar Items