Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation

Lin Wang, Jiating Li, Lin Zhao, Biquan Zhao, Geng Bai, Yufeng Ge, Yeyin Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Leaf area index (LAI) is an important phenotypic trait closely related to plant vigor and biomass. It is also a key parameter used in crop growth modeling. However, manually measuring LAI in the field can be slow and labor intensive. High resolution remote sensing, such as unmanned aircraft systems (UAS), has been explored for LAI estimation but with limited data sources, usually RGB and multispectral imagery. As UAS-based thermal infrared (TIR) imaging becoming readily available in agriculture, it is worth investigating the potential of its role in improving LAI estimation. In this study we evaluated the importance of canopy temperature measured by UAS-based TIR and multispectral imagery on maize LAI quantification within a breeding context (23 genotypes). Five plot-level features (canopy temperature, structure and two common vegetation indices) were extracted from the images, and used as inputs of machine learning models for the LAI estimation. The performance of the estimation was evaluated with a 5-fold cross validation with 30 random repeats for 162 samples. Results showed that, canopy temperature, together with canopy structure as model predictors, slightly improved LAI estimation (root mean square error, RMSE of 0.853 m2/m2 and coefficient of determination, R2 of 0.740) than those models without temperature difference (RMSE of 0.917 m2/m2 and R2of 0.706) for the various genotypes included in this study. In addition, canopy temperature showed moderate and more stable significance in estimating LAI than plant height and image uniformity. Its contribution to the estimation was comparable or even higher than those from vegetation indices when being modeled with random forest in this study. These relationships may be changed with a single or less genotypes which can be explored in future studies.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI
EditorsJ. Alex Thomasson, Alfonso F. Torres-Rua
PublisherSPIE
ISBN (Electronic)9781510643314
DOIs
StatePublished - 2021
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI 2021 - Virtual, Online, United States
Duration: Apr 12 2021Apr 16 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11747
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period4/12/214/16/21

Keywords

  • Canopy temperature
  • LAI estimation
  • Machine learning model
  • Multispectral image
  • Remote sensing
  • Thermal infrared image
  • Unmanned aircraft vehicle (UAV)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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