2024/05/29 by Heesup Yun, Yun, Heesup, Sassoum Lô +7
Agricultural and Biological Sciences · Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Calibration and Measurement Techniques #Computer Vision and Pattern Recognition (cs.CV) #Effects of Environmental Stressors on Livestock #FOS: Computer and information sciences #Urban Heat Island Mitigation
paper · pdf · doi:10.48550/arxiv.2405.19413
openalex publication_date 2024/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 VisTA-SR (Visual & Thermal Alignment and Super-Resolution 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.