RT info:eu-repo/semantics/article
T1 Application of convolutional neural networks in weed detection and identification: a systematic review
A1 García Navarrete, Óscar Leonardo
A1 Correa Guimaraes, Adriana
A1 Navas Gracia, Luis Manuel
K1 Precision agriculture
K1 Agricultural innovations
K1 Agricultura - Innovaciones tecnológicas
K1 Weeds
K1 Malas hierbas
K1 Weeds - Control
K1 Control de malezas
K1 Artificial intelligence -- Agricultural applications
K1 Machine learning
K1 Aprendizaje automático
K1 Computer vision
K1 Visión artificial (Robótica)
K1 Image processing
K1 Imágenes, Tratamiento de las
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Sustainable agriculture
K1 Agricultura sostenible
K1 3102 Ingeniería Agrícola
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
AB 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
PB MDPI
SN 2077-0472
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/68120
UL https://uvadoc.uva.es/handle/10324/68120
LA eng
NO Agriculture, 2024, Vol. 14, Nº. 4, 568
NO Producción Científica
DS UVaDOC
RD 21-dic-2024