  Thermal cameras are an important tool for agricultural research because they
allow for non-invasive measurement of plant temperature, which relates to
important photochemical, hydraulic, and agronomic traits. Utilizing low-cost
thermal cameras can lower the barrier to introducing thermal imaging in
agricultural research and production. This paper presents an approach to
improve the temperature accuracy and image quality of low-cost thermal imaging
cameras for agricultural applications. Leveraging advancements in computer
vision techniques, particularly deep learning networks, we propose a method,
called $\textbf{VisTA-SR}$ ($\textbf{Vis}$ual \& $\textbf{T}$hermal
$\textbf{A}$lignment and $\textbf{S}$uper-$\textbf{R}$esolution Enhancement)
that combines RGB and thermal images to enhance the capabilities of
low-resolution thermal cameras. The research includes calibration and
validation of temperature measurements, acquisition of paired image datasets,
and the development of a deep learning network tailored for agricultural
thermal imaging. Our study addresses the challenges of image enhancement in the
agricultural domain and explores the potential of low-cost thermal cameras to
replace high-resolution industrial cameras. Experimental results demonstrate
the effectiveness of our approach in enhancing temperature accuracy and image
sharpness, paving the way for more accessible and efficient thermal imaging
solutions in agriculture.
