Machine Learning and Thermal Imaging in Precision Agriculture

Thermal imaging has emerged as a pivotal technology in modern agriculture, offering farmers and domain experts advanced tools for monitoring crop health and detecting stress, pests, and diseases at early stages. Its integration with machine learning algorithms further enhances its potential, revolutionizing agricultural practices by providing real-time, data-driven insights. This paper delves into the recent advancements in the application of thermal imaging in agriculture, boosted by machine learning techniques. We explore some of the most important key areas, such as: Crop monitoring: utilizing thermal imaging for identifying and quantifying crop stress, aiding in the refinement of crop management strategies to boost yields.Irrigation management: leveraging thermal imaging data to fine-tune irrigation schedules, thereby conserving water and enhancing water use efficiency. Pest and disease detection: employing thermal imaging in conjunction with machine learning algorithms for the early detection and automated identification of pests and diseases, enabling prompt and effective interventions. This paper aims to provide a comprehensive overview of current applications and emerging trends in the use of thermal imaging and machine learning in agriculture, highlighting their benefits, challenges, and potential for future development. Moreover we conclude the paper by discussing the challenges and opportunities of using thermal imaging in agriculture applications.