Applicability of UAV in Crop Health Monitoring Using Machine Learning Techniques

Abstract

Food demands are increasing globally. Various issues such as urbanization, climate change, and desertification increasingly favour crop pests and diseases that limit crop productivity. Elaborating and discussing the pragmatic knowledge and information on recent advances in tools and techniques for crop monitoring developed in recent decades might help agronomists make more informed decisions. This chapter discusses the progress and development of new techniques equipped with recent sensors and platforms such as drones that have revolutionized the way of understanding plant physiology and stresses. It begins with the introduction to various tools available for crop stress estimation, mainly based on optical imaging such as multispectral, thermal, and hyperspectral imaging. An overview of unmanned aerial vehicle (UAV) -based image processing pipeline is presented and shed light on the possible avenues of UAV-based remote sensing for crop health monitoring using machine learning approaches.