Review of weed recognition: A global agriculture perspective

Abstract

Recent years have seen the emergence of various precision weed management technologies in both research and commercial contexts. These technologies better target weed management interventions to provide weed control that is more efficient and environmentally friendly. To support this effort, a significant amount of research has focused on machine vision to recognize weeds in a variety of crops. In this work, we systematically survey recent literature on weed recognition in crops and evaluate its relevance based on the status of global agriculture as presented in FAO statistics. Our findings indicate a notable emphasis on crops like sugar beet, carrot, and maize, while wheat and rice, despite their substantial contribution to global cropland and food supply, are relatively understudied. We conduct an in-depth analysis of the 12 most researched crop categories to discern trends in weed recognition research, and to understand why some crops are studied more intensively than others. This analysis reveals that the trajectory of research varies significantly between crops. We find that weed recognition in some globally critical crops is at an early stage of development, and lacks implementation and testing in real-world environments. Additionally, we find the differences in approach to weed recognition are not explained solely by the requirements of precision weed management for a given crop. Instead, the approaches taken, like with the choice of crop, often appear expedient, influenced by factors such as readily available annotated data, rather than by the crop-specific requirements of a precision weed management system.