Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning.

Journal: Plant methods
Published Date: May 30, 2025

Abstract

BACKGROUND: Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and yield potential of rice, and the traditional methods for obtaining LAI have problems such as low efficiency and large error. With the development of remote sensing technology, unmanned aerial multispectral remote sensing combined with deep learning technology provides a new way for efficient and accurate estimation of LAI in rice.

Authors

  • Haixia Li
    State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China.
  • Qian Li
    Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Chunlai Yu
    Huanghe University of Science and Technology, Zhengzhou, 450006, China.
  • Shanjun Luo
    Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou, 450046, China. luoshanjun@hnas.ac.cn.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals