VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture

TL;DR

VisTA-SR addresses the challenge of using affordable thermal cameras for agriculture by combining RGB-guided domain alignment with a multi-modal super-resolution method. The approach includes a calibration workflow for low-cost cameras and a two-stage VisTA-SR pipeline that uses CycleGAN for RGB-to-thermal translation, template matching for robust alignment, and a CNN–GAN SR network to enhance resolution. Field validation shows that calibrated low-cost sensors deliver more accurate temperatures, while VisTA-SR improves image sharpness and detail at organ scales, enabling more precise crop temperature analyses. The work promotes accessible, high-resolution thermal imaging for plant science and agricultural monitoring, with future directions toward robust metric development and biophysical parameter estimation.

Abstract

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.

Figures (9)

• Figure 1: Structure of the proposed VisTA-SR network. The network has two main stages: the Image Alignment and the Super-Resolution Network. The Image Alignment aligns the RGB and thermal images, while the Super-Resolution Network enhances the resolution of the thermal image.
• Figure 2: Comparison between thermocouple, factory, and calibrated temperature values in a time series
• Figure 3: Comparison between factory and calibrated temperature values in a 1:1 plot
• Figure 4: Matching and aligning process of low-resolution and high-resolution thermal images
• Figure 5: Matching RGB and thermal images using CycleGAN and template matching