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

Oscar Leonardo García-Navarrete (), Adriana Correa-Guimaraes and Luis Manuel Navas-Gracia ()
Additional contact information
Oscar Leonardo García-Navarrete: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain
Adriana Correa-Guimaraes: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain
Luis Manuel Navas-Gracia: TADRUS Research Group, Department of Agricultural and Forestry Engineering, Universidad de Valladolid, 34004 Palencia, Spain

Agriculture, 2024, vol. 14, issue 4, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/4/568/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/4/568/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:4:p:568-:d:1369080

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().