# Review of Weed Detection Methods Based on Computer Vision
###### Abstract  
Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.  
weed detection; computer vision; image processing; deep learning; machine learning +
Footnote †: journal: Sensors  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-000000-00000-00000-00000-000  
deep learning in fruit detection and yield estimation, including the problem and solution to fruit being occluded in imaging. However, they focused only on detection and yield estimation and disregarded other agricultural tasks that contain a large number of objects, such as weed detection. [PERSON] et al. [7] reviewed the application of deep learning in agriculture, involving many studies in the fields of weed identification, land cover classification, plant identification, fruit counting, and crop type classification. Nevertheless, it was only a summary of the current situation in weed detection. [PERSON] et al. [11] elucidated the research progress in field weed identification at home and abroad and the advantages and disadvantages of various segmentation, extraction, and identification methods. Nonetheless, few discussions were presented about the use of deep learning methods to solve the problem of weed identification. [PERSON] et al. [12] provided a comprehensive review of weed detection and classification research but focused on methods based on deep learning.  
Traditional and deep learning-based weed detection methods have their own advantages. Traditional weed detection methods require small sample sizes, have low requirements on graphics processing units, and can be used in agricultural machinery and equipment at a low cost. This paper mainly reviews the related methods for weed detection in recent years from the perspectives of traditional machine learning (ML) methods and deep learning and briefly discusses the pros and cons of the methods. The datasets of weed identification and detection and leaf classification are summarized, and the problems faced in field weed detection under different conditions are analyzed. This paper provides a certain reference to other scholars to further their research on weed detection algorithms based on computer vision and achieve intelligent weed control and related areas of research and application.