Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage.

Journal: Frontiers in plant science
Published Date: Jan 3, 2024

Abstract

INTRODUCTION: Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.

Authors

  • Mengxi Zou
    College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Maodong Fu
    Hebei Maodong Xingteng Agricultural Technology Service Co., Ltd, Cangzhou, China.
  • Cunjun Li
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Zixiang Zhou
    College of Geomatics, Xi'an University of Science and Technology, Xi'an, China.
  • Haoran Meng
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Enguang Xing
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Yanmin Ren
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.

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