Review of deep learning-based weed identification in crop fields - Hu, Wenze; Wane, Samuel Oliver; Zhu, Junke; Li, Dongsheng; Zhang, Qing; Bie, Xiaoting; Lan, Yubin (International Journal of Agricultural and Biological Engineering, 2023)
Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. Keywords: deep learning, weed detection, weed classification, image segmentation, Convolutional Neural Network, image processing DOI: 10.25165/j.ijabe.20231604.8364 Citation: Hu W Z, Wane S O, Zhu J K, Li D S, Zhang Q, Bie X T, et al. Review of deep learning-based weed identification in crop fields. Int J Agric & Biol Eng, 2023; 16(4): 1-10.