Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

García-Navarrete, Oscar Leonardo; Correa-Guimaraes, Adriana; Navas-Gracia, Luis Manuel

doi:10.3390/agriculture14040568

Open AccessReview

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

by

Oscar Leonardo García-Navarrete

^1,2,*

,

Adriana Correa-Guimaraes

¹

and

Luis Manuel Navas-Gracia

^1,*

¹

TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain

²

Department of Civil and Agricultural Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia

^*

Authors to whom correspondence should be addressed.

Agriculture 2024, 14(4), 568; https://doi.org/10.3390/agriculture14040568

Submission received: 14 January 2024 / Revised: 26 March 2024 / Accepted: 29 March 2024 / Published: 2 April 2024

(This article belongs to the Special Issue Artificial Intelligence, UAV, and Remote Sensing Applications for Precision Agriculture)

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Abstract

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

Keywords: precision agriculture; weed classification; machine learning; machine vision; image processing; CNN

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MDPI and ACS Style

García-Navarrete, O.L.; Correa-Guimaraes, A.; Navas-Gracia, L.M. Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review. Agriculture 2024, 14, 568. https://doi.org/10.3390/agriculture14040568

AMA Style

García-Navarrete OL, Correa-Guimaraes A, Navas-Gracia LM. Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review. Agriculture. 2024; 14(4):568. https://doi.org/10.3390/agriculture14040568

Chicago/Turabian Style

García-Navarrete, Oscar Leonardo, Adriana Correa-Guimaraes, and Luis Manuel Navas-Gracia. 2024. "Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review" Agriculture 14, no. 4: 568. https://doi.org/10.3390/agriculture14040568

APA Style

García-Navarrete, O. L., Correa-Guimaraes, A., & Navas-Gracia, L. M. (2024). Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review. Agriculture, 14(4), 568. https://doi.org/10.3390/agriculture14040568

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

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