2026-04-28T19:30:57Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/681202025-02-06T10:18:54Zcom_10324_1166com_10324_931com_10324_894col_10324_1338

Application of convolutional neural networks in weed detection and identification: a systematic review García Navarrete, Óscar Leonardo Correa Guimaraes, Adriana Navas Gracia, Luis Manuel Precision agriculture Agricultural innovations Agricultura - Innovaciones tecnológicas Weeds Malas hierbas Weeds - Control Control de malezas Artificial intelligence -- Agricultural applications Machine learning Aprendizaje automático Computer vision Visión artificial (Robótica) Image processing Imágenes, Tratamiento de las Neural networks (Computer science) Redes neuronales (Informática) Sustainable agriculture Agricultura sostenible Producción Científica 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 2024-06-17T08:08:54Z 2024-06-17T08:08:54Z 2024 info:eu-repo/semantics/article Agriculture, 2024, Vol. 14, Nº. 4, 568 2077-0472 https://uvadoc.uva.es/handle/10324/68120 10.3390/agriculture14040568 568 4 Agriculture 14 2077-0472 eng https://www.mdpi.com/2077-0472/14/4/568 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2024 The authors Atribución 4.0 Internacional MDPI SI