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Review

Review of Weed Detection Methods Based on Computer Vision

1
Department of Information Science, Xi’an University of Technology, Xi’an 710048, China
2
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; https://doi.org/10.3390/s21113647
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
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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. https://doi.org/10.3390/s21113647

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. https://doi.org/10.3390/s21113647

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. https://doi.org/10.3390/s21113647

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