{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:30:58Z","timestamp":1780356658200,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development (CNPq)","award":["407465\/2021-9"],"award-info":[{"award-number":["407465\/2021-9"]}]},{"name":"National Council for Scientific and Technological Development (CNPq)","award":["420296\/2023-9"],"award-info":[{"award-number":["420296\/2023-9"]}]},{"name":"Foundation for Research Support of the State of Goi\u00e1s (FAPEG); Embrapa Rice and Beans","award":["407465\/2021-9"],"award-info":[{"award-number":["407465\/2021-9"]}]},{"name":"Foundation for Research Support of the State of Goi\u00e1s (FAPEG); Embrapa Rice and Beans","award":["420296\/2023-9"],"award-info":[{"award-number":["420296\/2023-9"]}]},{"name":"University of Georgia; ACIV\u2014Associa\u00e7\u00e3o para o Desenvolvimento da Engenharia Civil\u2014Portugal; and internal funding from the Goiano Federal Institute\u2014Campus Ceres","award":["407465\/2021-9"],"award-info":[{"award-number":["407465\/2021-9"]}]},{"name":"University of Georgia; ACIV\u2014Associa\u00e7\u00e3o para o Desenvolvimento da Engenharia Civil\u2014Portugal; and internal funding from the Goiano Federal Institute\u2014Campus Ceres","award":["420296\/2023-9"],"award-info":[{"award-number":["420296\/2023-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Artificial Intelligence (AI) has changed how processes are developed, and decisions are made in the agricultural area replacing manual and repetitive processes with automated and more efficient ones. This study presents the application of deep learning techniques to detect and segment weeds in agricultural crops by applying models with different architectures in the analysis of images captured by an Unmanned Aerial Vehicle (UAV). This study contributes to the computer vision field by comparing the performance of the You Only Look Once (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), Mask R-CNN (with framework Detectron2), and U-Net models, making public the dataset with aerial images of soybeans and beans. The models were trained using a dataset consisting of 3021 images, randomly divided into test, validation, and training sets, which were annotated, resized, and increased using the Roboflow application interface. Evaluation metrics were used, which included training efficiency (mAP50 and mAP50-90), precision, accuracy, and recall in the model\u2019s evaluation and comparison. The YOLOv8s variant achieved higher performance with an mAP50 of 97%, precision of 99.7%, and recall of 99% when compared to the other models. The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. Furthermore, this study demonstrated the great potential of using advanced object segmentation algorithms in detecting weeds in soybean and bean crops.<\/jats:p>","DOI":"10.3390\/rs16234394","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T08:38:24Z","timestamp":1732523904000},"page":"4394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep Learning for Weed Detection and Segmentation in Agricultural Crops Using Images Captured by an Unmanned Aerial Vehicle"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1722-5145","authenticated-orcid":false,"given":"Josef Augusto Oberdan Souza","family":"Silva","sequence":"first","affiliation":[{"name":"Cerrado Irrigation Graduate Program, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vilson Soares de","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Faculty of Information Systems, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9399-4478","authenticated-orcid":false,"given":"Marcio","family":"Mesquita","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy, Federal University of Goi\u00e1s (UFG), Nova Veneza, Km 0, Campus Samambaia\u2014UFG, Goi\u00e2nia 74690-900, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6303-9063","authenticated-orcid":false,"given":"Lu\u00eds S\u00e9rgio Rodrigues","family":"Vale","sequence":"additional","affiliation":[{"name":"Cerrado Irrigation Graduate Program, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"},{"name":"Faculty of Agronomy, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thiago do Nascimento Borges","family":"Marques","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2611-4036","authenticated-orcid":false,"given":"Jhon Lennon Bezerra da","family":"Silva","sequence":"additional","affiliation":[{"name":"Cerrado Irrigation Graduate Program, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1318-2320","authenticated-orcid":false,"given":"Marcos Vin\u00edcius da","family":"Silva","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Forestry Sciences, Federal University of Campina Grande (UFCG), Av. Universit\u00e1ria, s\/n, Santa Cec\u00edlia, Patos 58708-110, Para\u00edba, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7426-942X","authenticated-orcid":false,"given":"Lorena Nunes","family":"Lacerda","sequence":"additional","affiliation":[{"name":"Crop and Soil Sciences Department, University of Georgia, Athens, GA 30602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6131-7605","authenticated-orcid":false,"given":"Jos\u00e9 Francisco de","family":"Oliveira-J\u00fanior","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), Macei\u00f3 57072-260, Alagoas, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0135-2249","authenticated-orcid":false,"given":"Jo\u00e3o Lu\u00eds Mendes Pedroso de","family":"Lima","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Sciences and Technology, University of Coimbra, 3030-788 Coimbra, Portugal"},{"name":"MARE\u2014Marine and Environmental Sciences Centre, University of Coimbra, 3000-456 Coimbra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8698-292X","authenticated-orcid":false,"given":"Henrique Fonseca Elias de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Cerrado Irrigation Graduate Program, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"},{"name":"Faculty of Agronomy, Goiano Federal Institute\u2014Campus Ceres, GO-154, km 218\u2014Zona Rural, Ceres 76300-000, Goi\u00e1s, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107723","DOI":"10.1016\/j.compag.2023.107723","article-title":"Leaf area index estimation of pergola-trained vineyards in arid regions using classical and deep learning methods based on UAV-based RGB images","volume":"207","author":"Ilniyaz","year":"2023","journal-title":"Comput. 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