Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection - Zhu, Hongyan; Lin, Chengzhi; Liu, Gengqi; Wang, Dani; Qin, Shuai; Li, Anjie; Xu, Jun-Li; He, Yong (Frontiers in Plant Science, 2024)
Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed.Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provide a concise overview as indispensable foundational knowledge.Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture are also mentioned in this paper. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.