https://www.socictopen.socict.org/files/original/11c867e3d83a8085126e144ce6734494.pdf 5ce7cbf35a442cd05adedbde2842c384 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery Creator An entity primarily responsible for making the resource Francisca López-Granados, Jorge Torres-Sánchez, Francisco M. Jiménez-Brenes, Ana I. de Castro, Francisco Javier Mesas-Carrascosa Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource ANN, RPAS, dicotyledonous (broad-leaved) and monocotyledonous (grass) weeds, precision agriculture, site-specific weed management Identifier An unambiguous reference to the resource within a given context 10.3390/agronomy11040749 Source A related resource from which the described resource is derived Agronomy Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Agriculture Relation A related resource <a href="https://www.mdpi.com/2073-4395/11/4/749" target="_blank" rel="noreferrer noopener">https://www.mdpi.com/2073-4395/11/4/749</a>