Next Article in Journal
Winter Wheat Resistant to Increases in Rain and Snow Intensity in a Semi-Arid System
Next Article in Special Issue
A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario
Previous Article in Journal
The Protective Biochemical Properties of Arbuscular Mycorrhiza Extraradical Mycelium in Acidic Soils Are Maintained throughout the Mediterranean Summer Conditions
 
 
Article

Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery

1
Grupo Imaping, Instituto de Agricultura Sostenible-CSIC, Avda. Menéndez Pidal s/n, 14004 Cordoba, Spain
2
Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071 Cordoba, Spain
*
Author to whom correspondence should be addressed.
Current address: Weed Control Group, Plant Protection Department, National Agricultural and Food Research and Technology Institute-INIA, Crta. de la Coruña, km 7,5, 28040 Madrid, Spain.
Academic Editor: Craig Morley
Agronomy 2021, 11(4), 749; https://doi.org/10.3390/agronomy11040749
Received: 3 March 2021 / Revised: 8 April 2021 / Accepted: 10 April 2021 / Published: 12 April 2021
Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds. View Full-Text
Keywords: ANN; RPAS; site-specific weed management; precision agriculture; dicotyledonous (broad-leaved) and monocotyledonous (grass) weeds ANN; RPAS; site-specific weed management; precision agriculture; dicotyledonous (broad-leaved) and monocotyledonous (grass) weeds
Show Figures

Figure 1

MDPI and ACS Style

Torres-Sánchez, J.; Mesas-Carrascosa, F.J.; Jiménez-Brenes, F.M.; de Castro, A.I.; López-Granados, F. Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery. Agronomy 2021, 11, 749. https://doi.org/10.3390/agronomy11040749

AMA Style

Torres-Sánchez J, Mesas-Carrascosa FJ, Jiménez-Brenes FM, de Castro AI, López-Granados F. Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery. Agronomy. 2021; 11(4):749. https://doi.org/10.3390/agronomy11040749

Chicago/Turabian Style

Torres-Sánchez, Jorge, Francisco Javier Mesas-Carrascosa, Francisco M. Jiménez-Brenes, Ana I. de Castro, and Francisca López-Granados. 2021. "Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery" Agronomy 11, no. 4: 749. https://doi.org/10.3390/agronomy11040749

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop