Estimating crop stomatal conductance from RGB, NIR, and thermal infrared images

Junxiao Zhang, Kantilata Thapa, Nipuna Chamara, Geng Bai, Yufeng Ge

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

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII
EditorsJ. Alex Thomasson, Christoph Bauer
PublisherSPIE
ISBN (Electronic)9781510661943
DOIs
StatePublished - 2023
EventAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023 - Orlando, United States
Duration: May 1 2023May 2 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12539
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAutonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII 2023
Country/TerritoryUnited States
CityOrlando
Period5/1/235/2/23

Keywords

  • Convolutional neural network
  • NIR image
  • RGB image
  • Remote sensing
  • Soil moisture
  • Stomatal conductance
  • Thermal infrared image

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Estimating crop stomatal conductance from RGB, NIR, and thermal infrared images'. Together they form a unique fingerprint.

Cite this