Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

Journal: Journal of the science of food and agriculture
PMID: 39221962

Abstract

BACKGROUND: Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134).

Authors

  • Yuanning Zhai
    School of Information Engineering, Huzhou University, Huzhou 313000, China.
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.
  • Lei Zhou
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xincheng Zhang
    Institute of Crop Science, Huzhou Academy of Agricultural Sciences, Huzhou, China.
  • Yun Ren
    Institute of Crop Science, Huzhou Academy of Agricultural Sciences, Huzhou, China.
  • Hengnian Qi
    School of Information Engineering, Huzhou University, Huzhou 313000, China.
  • Chu Zhang
    School of Information Engineering, Huzhou University, Huzhou 313000, China.

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