TY - GEN
T1 - Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation
AU - Wang, Lin
AU - Li, Jiating
AU - Zhao, Lin
AU - Zhao, Biquan
AU - Bai, Geng
AU - Ge, Yufeng
AU - Shi, Yeyin
N1 - Funding Information:
The funding for this work was provided by USDA National Institute of Food and Agriculture under Grant No. 2020-68013-32371, Grant No. 2021-67021-34417, and the funding supported by the Nebraska Agricultural Experiment Station through the Hatch Act capacity funding program (accession number 1011130) from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture.
Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Canopy temperature
KW - LAI estimation
KW - Machine learning model
KW - Multispectral image
KW - Remote sensing
KW - Thermal infrared image
KW - Unmanned aircraft vehicle (UAV)
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U2 - 10.1117/12.2586694
DO - 10.1117/12.2586694
M3 - Conference contribution
AN - SCOPUS:85108991257
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI
A2 - Thomasson, J. Alex
A2 - Torres-Rua, Alfonso F.
PB - SPIE
T2 - Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI 2021
Y2 - 12 April 2021 through 16 April 2021
ER -