TY - GEN
T1 - Estimating crop stomatal conductance from RGB, NIR, and thermal infrared images
AU - Zhang, Junxiao
AU - Thapa, Kantilata
AU - Chamara, Nipuna
AU - Bai, Geng
AU - Ge, Yufeng
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - As the global population continues to increase, the demand for food production rises accordingly. The water availability of crops has a significant impact on their yield during the processes of photosynthesis and transpiration. Crops exchange carbon dioxide and water with the atmosphere through stomata. When crops undergo water stress, they tend to close their stomata to reduce water loss. However, this can also negatively affect the crop's photosynthetic rate and carbon assimilation, leading to low yields. Stomatal conductance (SC) quantifies the rate of gas exchange between crops and the atmosphere and can inform the crop's water status. SC measurements require the use of contact-type instruments, which is time-consuming and labor-intensive. This study examined the accuracy of multiple linear regression (MLR), support vector regression (SVR), and convolutional neural network (CNN) models for SC estimation in corn and soybean using RGB, near-infrared, and thermal-infrared images from a field phenotyping platform. The results show that the CNN model outperformed other two models, with R2 value of 0.52. Furthermore, adding soil moisture as a variable to the model improved its accuracy, decreasing model RMSE from 0.147 to 0.137 mol/(m2∗s). This study highlights the potential of estimating SC from remote sensing platforms to help growers obtain information about their crop water status and plan irrigation more effectively.
AB - As the global population continues to increase, the demand for food production rises accordingly. The water availability of crops has a significant impact on their yield during the processes of photosynthesis and transpiration. Crops exchange carbon dioxide and water with the atmosphere through stomata. When crops undergo water stress, they tend to close their stomata to reduce water loss. However, this can also negatively affect the crop's photosynthetic rate and carbon assimilation, leading to low yields. Stomatal conductance (SC) quantifies the rate of gas exchange between crops and the atmosphere and can inform the crop's water status. SC measurements require the use of contact-type instruments, which is time-consuming and labor-intensive. This study examined the accuracy of multiple linear regression (MLR), support vector regression (SVR), and convolutional neural network (CNN) models for SC estimation in corn and soybean using RGB, near-infrared, and thermal-infrared images from a field phenotyping platform. The results show that the CNN model outperformed other two models, with R2 value of 0.52. Furthermore, adding soil moisture as a variable to the model improved its accuracy, decreasing model RMSE from 0.147 to 0.137 mol/(m2∗s). This study highlights the potential of estimating SC from remote sensing platforms to help growers obtain information about their crop water status and plan irrigation more effectively.
KW - Convolutional neural network
KW - NIR image
KW - RGB image
KW - Remote sensing
KW - Soil moisture
KW - Stomatal conductance
KW - Thermal infrared image
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U2 - 10.1117/12.2663888
DO - 10.1117/12.2663888
M3 - Conference contribution
AN - SCOPUS:85171193910
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII
A2 - Thomasson, J. Alex
A2 - Bauer, Christoph
PB - SPIE
T2 - Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023
Y2 - 1 May 2023 through 2 May 2023
ER -