Frost management in agriculture with advanced sensing, modeling, and artificial intelligent technologies: A review

Frost poses a major threat to agricultural economies through its adverse impact on crop yields. Smart and high-efficiency frost management including pre-frost prediction, in-process monitoring, timely protection, and post-frost evaluation can assist growers in mitigating the impact of frost damage. Due to the unpredictable characteristics of frost, traditional management strategies have limited capacity to timely and autonomously implement frost protection methods. In recent years, the development of machine learning (ML), the Internet of Things (IoT), cyber-physical systems (CPS), mathematical modeling, and cutting-edge robotics i.e., unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) allows great improvements of frost protection performance. This review presents a comprehensive survey of these state-of-the-art technologies applied to frost management in plants. Of the 253 papers reviewed, 8.7% were published before 2000, 13.4% and 45.1% were published between 2001 and 2010 and between 2011 and 2020, and 32.8% were published after 2020, suggesting that this research area is rapidly growing in recent decades. In this survey, we found that fruit trees and arable crops were the most studied frost-damaged crop types, accounting for 41.4% and 25.3% of the research papers specifying the studied crops. The machine learning-powered frost forecasting and IoT/CPS-based frost monitoring and protection are discussed. In addition, non-destructive image sensing techniques for post-frost detection and evaluation, and advanced mathematical modeling for the improvement of frost protection performance are analyzed. Finally, the current challenges and the opportunities in future research for integrated, intelligent, and autonomous frost management in large-scale fields are summarized.