Adoption of Unmanned Aerial Vehicle (UAV) imagery in agricultural management: A systematic literature review

Precision agriculture and Smart farming have become the essential backbone for sustainable agricultural production by leveraging cutting edge remote sensing and communication technologies, meshed with AI driven data processing and decision making approaches. Agricultural segments, such as crop and livestock monitoring, crop/plant classification, yield prediction, weed detection, automatic harvesting, early detection, and prevention of diseases are being served for efficient, cost-effective process monitoring with increased profitability. With the remarkable development in recent decades, Unmanned Aerial Vehicles (UAV) based remote sensing technologies have gained rapid proliferation and exploitation in precision agriculture. Consequently, over the past decades, researchers have explored the capabilities of UAVs for real-time imagery data acquisition and processing through powerful Deep Learning (DL) algorithms to optimize agricultural process management. Being a prevalent research domain of high-tech field with constant advancement, there is a need for systematic review to recapitulate the contemporary literature and reveal the domain’s intellectual structure.

This systematic literature review (SLR) research has methodically scrutinized 214 peer reviewed articles on the concerned domain that are published in ranked journals and conferences over the past 14 years. Several pressing dimensions are investigated, including, the feasibility assessment of the UAVs in precision agriculture, determine the impact of imaging modalities and imagery datasets in relation to agricultural applications, categorically evaluate the UAV configuration and offer detailed scrutiny of AI methods in relation to real-time control, decision making and action performance in agricultural applications. Alongside, the taxonomy of crops across the world is documented for which UAV is utilized. Finally, the main challenges and directions of future research along the track is presented.