Next Article in Journal
Channel Allocation for Connected Vehicles in Internet of Things Services
Next Article in Special Issue
Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester
Previous Article in Journal
The Concurrent Validity, Test–Retest Reliability and Usability of a New Foot Temperature Monitoring System for Persons with Diabetes at High Risk of Foot Ulceration
Previous Article in Special Issue
Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions

Review of Weed Detection Methods Based on Computer Vision

Department of Information Science, Xi’an University of Technology, Xi’an 710048, China
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Author to whom correspondence should be addressed.
Academic Editor: Thomas Udelhoven
Sensors 2021, 21(11), 3647;
Received: 14 April 2021 / Revised: 15 May 2021 / Accepted: 21 May 2021 / Published: 24 May 2021
(This article belongs to the Special Issue Smart Agriculture Sensors)
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. View Full-Text
Keywords: weed detection; computer vision; image processing; deep learning; machine learning weed detection; computer vision; image processing; deep learning; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Wu, Z.; Chen, Y.; Zhao, B.; Kang, X.; Ding, Y. Review of Weed Detection Methods Based on Computer Vision. Sensors 2021, 21, 3647.

AMA Style

Wu Z, Chen Y, Zhao B, Kang X, Ding Y. Review of Weed Detection Methods Based on Computer Vision. Sensors. 2021; 21(11):3647.

Chicago/Turabian Style

Wu, Zhangnan, Yajun Chen, Bo Zhao, Xiaobing Kang, and Yuanyuan Ding. 2021. "Review of Weed Detection Methods Based on Computer Vision" Sensors 21, no. 11: 3647.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop