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This study proposes a multi-class weed detection approach based on deep learning using unmanned aerial vehicle (UAV) images acquired in real potato fields under uncontrolled environmental conditions. A custom dataset containing five classes (potato crop and four weed categories) was created, annotated, and made publicly available to support reproducibility and future comparisons. Two modern object detection architectures, based on convolutional neural networks and Transformer models, were evaluated through multiple training variants to analyze the impact of class balancing, image resolution, and hyperparameter optimization on detection performance. The models were assessed using standard object detection metrics and complemented with statistical validation to verify the reliability of the predictions in real-field conditions. Experimental results show that both architectures achieve robust and comparable performance, demonstrating their suitability for near real-time weed detection and precision agriculture applications. The findings confirm the effectiveness of combining advanced deep learning models with UAV imagery for accurate multi-class weed identification in complex agricultural environments.<\/jats:p>","DOI":"10.1515\/comp-2025-0058","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:18:36Z","timestamp":1782901116000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-class weed detection based on\u00a0deep learning using unmanned aerial vehicle images in\u00a0potato fields"],"prefix":"10.1515","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1463-017X","authenticated-orcid":false,"given":"Luc\u00eda","family":"Sandoval-Pillajo","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia , Valencia , Spain"},{"name":"Universidad T\u00e9cnica del Norte , Ibarra , Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Perugachi-Ch\u00e1vez","sequence":"additional","affiliation":[{"name":"Universidad T\u00e9cnica del Norte , Ibarra , Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4265-999X","authenticated-orcid":false,"given":"Marco","family":"Pusd\u00e1-Chulde","sequence":"additional","affiliation":[{"name":"Universidad T\u00e9cnica del Norte , Ibarra , Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2311-0785","authenticated-orcid":false,"given":"Adriana","family":"Giret-Boggino","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia , Valencia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6404-5185","authenticated-orcid":false,"given":"Iv\u00e1n","family":"Garc\u00eda-Santill\u00e1n","sequence":"additional","affiliation":[{"name":"Universidad T\u00e9cnica del Norte , Ibarra , Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"2026070310561169259_j_comp-2025-0058_ref_001","unstructured":"United Nations, \u201cThe sustainable development goals report 2023: Special edition,\u201d [Online]. Available: https:\/\/unstats.un.org\/sdgs\/report\/2023\/ [Accessed: Dec. 20, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_002","doi-asserted-by":"crossref","unstructured":"A. Babaei-Ghaghelestany, M. T. Alebrahim, S. Farzaneh, and M. Mehrabi, \u201cThe anticancer and antibacterial properties of aqueous and methanol extracts of weeds,\u201d J. Agric. Food Res., vol.\u00a010, p.\u00a0100433, 2022, https:\/\/doi.org\/10.1016\/J.JAFR.2022.100433.","DOI":"10.1016\/j.jafr.2022.100433"},{"key":"2026070310561169259_j_comp-2025-0058_ref_003","doi-asserted-by":"crossref","unstructured":"A. Pau\u0161i\u010d, S. Tojnko, and M. Le\u0161nik, \u201cPermanent, undisturbed, in-row living mulch: A realistic option to replace glyphosate-dominated chemical weed control in intensive pear orchards,\u201d Agric. Ecosyst. Environ., vol.\u00a0318, p.\u00a0107502, 2021, https:\/\/doi.org\/10.1016\/J.AGEE.2021.107502.","DOI":"10.1016\/j.agee.2021.107502"},{"key":"2026070310561169259_j_comp-2025-0058_ref_004","doi-asserted-by":"crossref","unstructured":"M. Vasileiou et al.., \u201cTransforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning,\u201d Crop Prot., vol.\u00a0176, p.\u00a0106522, 2024, https:\/\/doi.org\/10.1016\/J.CROPRO.2023.106522.","DOI":"10.1016\/j.cropro.2023.106522"},{"key":"2026070310561169259_j_comp-2025-0058_ref_005","doi-asserted-by":"crossref","unstructured":"J. Gao et al.., \u201cCross-domain transfer learning for weed segmentation and mapping in precision farming using ground and UAV images,\u201d Expert Syst. Appl., vol.\u00a0246, p.\u00a0122980, 2024, https:\/\/doi.org\/10.1016\/J.ESWA.2023.122980.","DOI":"10.1016\/j.eswa.2023.122980"},{"key":"2026070310561169259_j_comp-2025-0058_ref_006","doi-asserted-by":"crossref","unstructured":"I. D. Garc\u00eda-Santill\u00e1n and G. Pajares, \u201cOn-line crop\/weed discrimination through the Mahalanobis distance from images in maize fields,\u201d Biosyst. Eng., vol.\u00a0166, pp.\u00a028\u201343, 2018, https:\/\/doi.org\/10.1016\/J.BIOSYSTEMSENG.2017.11.003.","DOI":"10.1016\/j.biosystemseng.2017.11.003"},{"key":"2026070310561169259_j_comp-2025-0058_ref_007","doi-asserted-by":"crossref","unstructured":"K. Osorio, A. Puerto, C. Pedraza, D. Jamaica, and L. Rodr\u00edguez, \u201cA deep learning approach for weed detection in lettuce crops using multispectral images,\u201d AgriEngineering, vol.\u00a02, no.\u00a03, pp.\u00a0471\u2013488, 2020, https:\/\/doi.org\/10.3390\/AGRIENGINEERING2030032.","DOI":"10.3390\/agriengineering2030032"},{"key":"2026070310561169259_j_comp-2025-0058_ref_008","doi-asserted-by":"crossref","unstructured":"K. Zou, X. Chen, F. Zhang, H. Zhou, and C. Zhang, \u201cA field weed density evaluation method based on UAV imaging and modified U-net,\u201d Remote Sens., vol.\u00a013, no.\u00a02, p.\u00a0310, 2021, https:\/\/doi.org\/10.3390\/RS13020310.","DOI":"10.3390\/rs13020310"},{"key":"2026070310561169259_j_comp-2025-0058_ref_009","doi-asserted-by":"crossref","unstructured":"A. Kamilaris and F. X. Prenafeta-Bold\u00fa, \u201cDeep learning in agriculture: A survey,\u201d Comput. Electron. Agric., vol.\u00a0147, pp.\u00a070\u201390, 2018, https:\/\/doi.org\/10.1016\/J.COMPAG.2018.02.016.","DOI":"10.1016\/j.compag.2018.02.016"},{"key":"2026070310561169259_j_comp-2025-0058_ref_010","doi-asserted-by":"crossref","unstructured":"C.-P. Anderson, P.-C. Marco, T.-E. Diego, C.-S. Victor, and G.-S. Iv\u00e1n, \u201cBinary classification of defects in multiple coffee beans using lightweight convolutional neural networks for embedded systems,\u201d Data Metadata, vol.\u00a04, p.\u00a0840, 2025, https:\/\/doi.org\/10.56294\/dm2025840.","DOI":"10.56294\/dm2025840"},{"key":"2026070310561169259_j_comp-2025-0058_ref_011","doi-asserted-by":"crossref","unstructured":"M. Cevallos, L. Sandoval-Pillajo, V. Caranqui-S\u00e1nchez, C. Ortega-Bustamante, M. Pusd\u00e1-Chulde, and I. Garc\u00eda-Santill\u00e1n, \u201cMorphological defects classification in coffee beans based on convolutional neural networks,\u201d Commun. Comput. Inf. Sci., vol. 2276, pp. 3\u201315, 2025. https:\/\/doi.org\/10.1007\/978-3-031-75702-0_1.","DOI":"10.1007\/978-3-031-75702-0_1"},{"key":"2026070310561169259_j_comp-2025-0058_ref_012","doi-asserted-by":"crossref","unstructured":"F. Ulloa, L. Sandoval-Pillajo, P. Landeta-L\u00f3pez, N. Granda-Pe\u00f1afiel, M. Pusd\u00e1-Chulde, and I. Garc\u00eda-Santill\u00e1n, \u201cIdentification of diabetic retinopathy from retinography images using a convolutional neural network,\u201d Commun. Comput. Inf. Sci., vol. 2276, pp. 121\u2013136, 2025. https:\/\/doi.org\/10.1007\/978-3-031-75702-0_10.","DOI":"10.1007\/978-3-031-75702-0_10"},{"key":"2026070310561169259_j_comp-2025-0058_ref_013","doi-asserted-by":"crossref","unstructured":"S. Montenegro, M. Pusd\u00e1-Chulde, V. Caranqui-S\u00e1nchez, J. Herrera-Tapia, C. Ortega-Bustamante, and I. Garc\u00eda-Santill\u00e1n, \u201cAndroid mobile application for cattle body condition score using convolutional neural networks,\u201d Commun. Comput. Inf. Sci., vol.\u00a01705, pp.\u00a091\u2013105, 2023, https:\/\/doi.org\/10.1007\/978-3-031-32213-6_7.","DOI":"10.1007\/978-3-031-32213-6_7"},{"key":"2026070310561169259_j_comp-2025-0058_ref_014","doi-asserted-by":"crossref","unstructured":"F. Salazar-Fierro, C. Cumbal, D. Trejo-Espa\u00f1a, C. Le\u00f3n-Fern\u00e1ndez, M. Pusd\u00e1-Chulde, and I. Garc\u00eda-Santill\u00e1n, \u201cDetection of scoliosis in X-ray images using a convolutional neural network,\u201d Commun. Comput. Inf. Sci., vol. 2276, pp. 167\u2013183, 2025. https:\/\/doi.org\/10.1007\/978-3-031-75702-0_13.","DOI":"10.1007\/978-3-031-75702-0_13"},{"key":"2026070310561169259_j_comp-2025-0058_ref_015","doi-asserted-by":"crossref","unstructured":"B. Chacua et al.., \u201cPeople identification through facial recognition using deep learning,\u201d in 2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019, 2019.","DOI":"10.1109\/LA-CCI47412.2019.9037043"},{"key":"2026070310561169259_j_comp-2025-0058_ref_016","doi-asserted-by":"crossref","unstructured":"B. Espejo-Garcia, N. Mylonas, L. Athanasakos, S. Fountas, and I. Vasilakoglou, \u201cTowards weeds identification assistance through transfer learning,\u201d Comput. Electron. Agric., vol.\u00a0171, p.\u00a0105306, 2020, https:\/\/doi.org\/10.1016\/J.COMPAG.2020.105306.","DOI":"10.1016\/j.compag.2020.105306"},{"key":"2026070310561169259_j_comp-2025-0058_ref_017","doi-asserted-by":"crossref","unstructured":"M. R. Pusd\u00e1-Chulde, F. A. Salazar-Fierro, L. Sandoval-Pillajo, E. P. Herrera-Granda, I. D. Garc\u00eda-Santill\u00e1n, and A. De Giusti, \u201cImage analysis based on heterogeneous architectures for precision agriculture: A systematic literature review,\u201d Adv. Intell. Syst. Comput., vol.\u00a01078, pp.\u00a051\u201370, 2020, https:\/\/doi.org\/10.1007\/978-3-030-33614-1_4.","DOI":"10.1007\/978-3-030-33614-1_4"},{"key":"2026070310561169259_j_comp-2025-0058_ref_018","doi-asserted-by":"crossref","unstructured":"K. Vinueza, L. Sandoval-Pillajo, A. Giret-Boggino, D. Trejo-Espa\u00f1a, M. Pusd\u00e1-Chulde, and I. Garc\u00eda-Santill\u00e1n, \u201cAutomatic weed quantification in potato crops based on a modified convolutional neural network using drone images,\u201d Data Metadata, vol.\u00a04, no.\u00a0194, 2025, https:\/\/doi.org\/10.56294\/dm2025194.","DOI":"10.56294\/dm2025194"},{"key":"2026070310561169259_j_comp-2025-0058_ref_019","doi-asserted-by":"crossref","unstructured":"L. Sandoval-Pillajo, I. Garc\u00eda-Santill\u00e1n, M. Pusd\u00e1-Chulde, and A. Giret, \u201cWeed detection based on deep learning from UAV imagery: A review,\u201d Smart Agric. Technol., vol. 12, 2025. https:\/\/doi.org\/10.1016\/j.atech.2025.101147.","DOI":"10.1016\/j.atech.2025.101147"},{"key":"2026070310561169259_j_comp-2025-0058_ref_020","unstructured":"R. Khanam and M. Hussain, \u201cYOLOv11: An overview of the key architectural enhancements,\u201d 2024. [Online]. Available:https:\/\/arxiv.org\/abs\/2410.17725."},{"key":"2026070310561169259_j_comp-2025-0058_ref_021","unstructured":"A. Vaswani et al.., \u201cAttention is all you need,\u201d Adv. Neural Inf. Process. Syst., vol.\u00a02017, pp.\u00a05999\u20136009, 2017. [Online]. Available: https:\/\/arxiv.org\/abs\/1706.03762v7."},{"key":"2026070310561169259_j_comp-2025-0058_ref_022","doi-asserted-by":"crossref","unstructured":"Z. Liu et al.., \u201cSwin transformer: Hierarchical vision transformer using shifted windows,\u201d 2021. [Online]. https:\/\/github [Accessed: Dec. 22, 2024].","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2026070310561169259_j_comp-2025-0058_ref_023","doi-asserted-by":"crossref","unstructured":"N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, \u201cEnd-to-end object detection with transformers,\u201d in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12346, Cham, Springer, 2020, pp. 213\u2013229.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2026070310561169259_j_comp-2025-0058_ref_024","doi-asserted-by":"crossref","unstructured":"M. Yang, R. Xu, C. Yang, H. Wu, and A. Wang, \u201cHybrid-DETR: A differentiated module-based model for object detection in remote sensing images,\u201d Electronics, vol.\u00a013, no.\u00a024, p.\u00a05014, 2024, https:\/\/doi.org\/10.3390\/ELECTRONICS13245014.","DOI":"10.3390\/electronics13245014"},{"key":"2026070310561169259_j_comp-2025-0058_ref_025","unstructured":"X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, \u201cDeformable DETR: Deformable transformers for end-to-end object detection,\u201d in ICLR 2021 \u2013 9th International Conference on Learning Representations, 2020. https:\/\/arxiv.org\/abs\/2010.04159v4 [Accessed: Dec. 22, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_026","doi-asserted-by":"crossref","unstructured":"F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, \u201cDN-DETR: Accelerate DETR training by introducing query DeNoising,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.\u00a046, no.\u00a04, pp.\u00a02239\u20132251, 2022, https:\/\/doi.org\/10.1109\/TPAMI.2023.3335410.","DOI":"10.1109\/TPAMI.2023.3335410"},{"key":"2026070310561169259_j_comp-2025-0058_ref_027","unstructured":"H. Zhang et al.., \u201cDINO: DETR with improved DeNoising anchor boxes for end-to-end object detection,\u201d in 11th International Conference on Learning Representations, ICLR 2023, 2022 [Online]. Available: https:\/\/arxiv.org\/abs\/2203.03605v4 [Accessed: Dec. 23, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_028","unstructured":"Y. Zhao et al.., \u201cDETRs beat YOLOs on real-time object detection,\u201d in 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp.\u00a016965\u201316974."},{"key":"2026070310561169259_j_comp-2025-0058_ref_029","doi-asserted-by":"crossref","unstructured":"M. Darbyshire, S. Coutts, P. Bosilj, E. Sklar, and S. Parsons, \u201cReview of weed recognition: A global agriculture perspective,\u201d Comput. Electron. Agric., vol.\u00a0227, p.\u00a0109499, 2024, https:\/\/doi.org\/10.1016\/j.compag.2024.109499.","DOI":"10.1016\/j.compag.2024.109499"},{"key":"2026070310561169259_j_comp-2025-0058_ref_030","unstructured":"U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, \u201cFrom data mining to knowledge discovery in databases,\u201d AI Mag., vol.\u00a017, no.\u00a03, p.\u00a037, 1996, https:\/\/doi.org\/10.1609\/AIMAG.V17I3.1230."},{"key":"2026070310561169259_j_comp-2025-0058_ref_031","doi-asserted-by":"crossref","unstructured":"B. Deng, Y. Lu, and J. Xu, \u201cWeed database development: An updated survey of public weed datasets and cross-season weed detection adaptation,\u201d Ecol. Inform., vol.\u00a081, p.\u00a0102546, 2024, https:\/\/doi.org\/10.1016\/J.ECOINF.2024.102546.","DOI":"10.1016\/j.ecoinf.2024.102546"},{"key":"2026070310561169259_j_comp-2025-0058_ref_032","doi-asserted-by":"crossref","unstructured":"X. Kong, T. Liu, X. Chen, X. Jin, A. Li, and J. Yu, \u201cEfficient crop segmentation net and novel weed detection method,\u201d Eur. J. Agron., vol.\u00a0161, p.\u00a0127367, 2024, https:\/\/doi.org\/10.1016\/j.eja.2024.127367.","DOI":"10.1016\/j.eja.2024.127367"},{"key":"2026070310561169259_j_comp-2025-0058_ref_033","doi-asserted-by":"crossref","unstructured":"D. A. Abuhani, M. H. Hussain, J. Khan, M. Elmohandes, and I. Zualkernan, \u201cCrop and weed detection in sunflower and sugarbeet fields using single shot detectors,\u201d in 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023, 2023.","DOI":"10.1109\/COINS57856.2023.10189257"},{"key":"2026070310561169259_j_comp-2025-0058_ref_034","doi-asserted-by":"crossref","unstructured":"N. Iqbal, C. Manss, C. Scholz, D. Konig, M. Igelbrink, and A. Ruckelshausen, \u201cAI-based maize and weeds detection on the edge with CornWeed dataset,\u201d in Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, 2023, pp.\u00a0577\u2013584.","DOI":"10.15439\/2023F2125"},{"key":"2026070310561169259_j_comp-2025-0058_ref_035","doi-asserted-by":"crossref","unstructured":"H. Li and F. Shi, \u201cA DETR-like detector-based semi-supervised object detection method for Brassica Chinensis growth monitoring,\u201d Comput. Electron. Agric., vol.\u00a0219, p.\u00a0108788, 2024, https:\/\/doi.org\/10.1016\/J.COMPAG.2024.108788.","DOI":"10.1016\/j.compag.2024.108788"},{"key":"2026070310561169259_j_comp-2025-0058_ref_036","doi-asserted-by":"crossref","unstructured":"E. Borgogno, J. M. Pe\u00f1a, A. I. de Castro, I. Borra-Serrano, and J. Dorado, \u201cCognitive computing advancements: Improving precision crop protection through UAV imagery for targeted weed monitoring,\u201d Remote Sens., vol.\u00a016, no.\u00a016, p.\u00a03026, 2024, https:\/\/doi.org\/10.3390\/RS16163026.","DOI":"10.3390\/rs16163026"},{"key":"2026070310561169259_j_comp-2025-0058_ref_037","doi-asserted-by":"crossref","unstructured":"S. K. Valicharla, R. Karimzadeh, K. Naharki, X. Li, and Y. L. Park, \u201cDetection and multi-class classification of invasive knotweeds with drones and deep learning models,\u201d Drones, vol.\u00a08, no.\u00a07, p.\u00a0293, 2024, https:\/\/doi.org\/10.3390\/DRONES8070293\/S1.","DOI":"10.3390\/drones8070293"},{"key":"2026070310561169259_j_comp-2025-0058_ref_038","doi-asserted-by":"crossref","unstructured":"Z. Guo, D. Cai, Y. Zhou, T. Xu, and F. Yu, \u201cIdentifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning,\u201d Plant Methods, vol.\u00a020, no.\u00a01, pp.\u00a01\u201317, 2024, https:\/\/doi.org\/10.1186\/S13007-024-01232-0\/TABLES\/7.","DOI":"10.1186\/s13007-024-01232-0"},{"key":"2026070310561169259_j_comp-2025-0058_ref_039","doi-asserted-by":"crossref","unstructured":"S. Arriola-Valverde, R. Rimolo-Donadio, K. Villagra-Mendoza, A. Chac\u00f3n-Rodriguez, R. Garc\u00eda-Ramirez, and E. Somarriba-Chavez, \u201cA comparative study of deep learning frameworks applied to coffee plant detection from close-range UAS-RGB imagery in Costa Rica,\u201d Remote Sens., vol.\u00a016, no.\u00a024, p.\u00a04617, 2024, https:\/\/doi.org\/10.3390\/RS16244617.","DOI":"10.3390\/rs16244617"},{"key":"2026070310561169259_j_comp-2025-0058_ref_040","doi-asserted-by":"crossref","unstructured":"O. L. Garc\u00eda-Navarrete, J. H. Camacho-Tamayo, A. B. Bregon, J. Mart\u00edn-Garc\u00eda, and L. M. Navas-Gracia, \u201cPerformance analysis of real-time detection transformer and you only look once models for weed detection in maize cultivation,\u201d Agronomy, vol.\u00a015, no.\u00a04, 2025, https:\/\/doi.org\/10.3390\/agronomy15040796.","DOI":"10.3390\/agronomy15040796"},{"key":"2026070310561169259_j_comp-2025-0058_ref_041","unstructured":"S. Pavithra, D. Kachroo, V. Kadam, H. Padala, and R. Purbey, \u201cDrone-based weed and disease detection in agricultural fields to maximize crop health using a Yolov8 approach,\u201d in 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023, 2023."},{"key":"2026070310561169259_j_comp-2025-0058_ref_042","doi-asserted-by":"crossref","unstructured":"M. U. Rehman, H. Eesaar, Z. Abbas, L. Seneviratne, I. Hussain, and K. T. Chong, \u201cAdvanced drone-based weed detection using feature-enriched deep learning approach,\u201d Knowl. Based Syst., vol.\u00a0305, 2024, https:\/\/doi.org\/10.1016\/J.KNOSYS.2024.112655.","DOI":"10.1016\/j.knosys.2024.112655"},{"key":"2026070310561169259_j_comp-2025-0058_ref_043","doi-asserted-by":"crossref","unstructured":"P. Saini and D. S. Nagesh, \u201cCottonWeeds: Empowering precision weed management through deep learning and comprehensive dataset,\u201d Crop Prot., vol.\u00a0181, p.\u00a0106675, 2024, https:\/\/doi.org\/10.1016\/J.CROPRO.2024.106675.","DOI":"10.1016\/j.cropro.2024.106675"},{"key":"2026070310561169259_j_comp-2025-0058_ref_044","doi-asserted-by":"crossref","unstructured":"J. A. O. S. Silva et al.., \u201cDeep learning for weed detection and segmentation in agricultural crops using images captured by an unmanned aerial vehicle,\u201d Remote Sens., vol.\u00a016, no.\u00a023, p.\u00a04394, 2024, https:\/\/doi.org\/10.3390\/RS16234394.","DOI":"10.3390\/rs16234394"},{"key":"2026070310561169259_j_comp-2025-0058_ref_045","doi-asserted-by":"crossref","unstructured":"R. Gao, Y. Jin, X. Tian, Z. Ma, S. Liu, and Z. Su, \u201cYOLOv5-T: A precise real-time detection method for maize tassels based on UAV low altitude remote sensing images,\u201d Comput. Electron. Agric., vol.\u00a0221, p.\u00a0108991, 2024, https:\/\/doi.org\/10.1016\/J.COMPAG.2024.108991.","DOI":"10.1016\/j.compag.2024.108991"},{"key":"2026070310561169259_j_comp-2025-0058_ref_046","doi-asserted-by":"crossref","unstructured":"M. Arsalan et al.., \u201cReal-time precision spraying application for tobacco plants,\u201d Smart Agric. Technol., vol.\u00a08, p.\u00a0100497, 2024, https:\/\/doi.org\/10.1016\/J.ATECH.2024.100497.","DOI":"10.1016\/j.atech.2024.100497"},{"key":"2026070310561169259_j_comp-2025-0058_ref_047","doi-asserted-by":"crossref","unstructured":"F. Tian, C. J. Ransom, J. Zhou, B. Wilson, and K. A. Sudduth, \u201cAssessing the impact of soil and field conditions on cotton crop emergence using UAV-based imagery,\u201d Comput. Electron. Agric., vol. 218, p. 108738, 2024. https:\/\/doi.org\/10.1016\/j.compag.2024.108738.","DOI":"10.1016\/j.compag.2024.108738"},{"key":"2026070310561169259_j_comp-2025-0058_ref_048","doi-asserted-by":"crossref","unstructured":"M. Danilevicz, R. L. Rocha, J. Batley, P. E. Bayer, M. Bennamoun, and D. Edwards, \u201cSegmentation of sandplain lupin weeds from morphologically similar narrow-leafed lupins in the field,\u201d 2023 [Online]. Available: https:\/\/figshare.com\/articles\/dataset\/Segmentation_of_sandplain_lupin_weeds_from_morphologically_similar_narrow-leafed_lupins_in_the_field\/21746669 [Accessed: Feb. 20, 2025].","DOI":"10.3390\/rs15071817"},{"key":"2026070310561169259_j_comp-2025-0058_ref_049","unstructured":"I. Moazzam, \u201cTobacco aerial dataset,\u201d Mendeley Data, vol. 2, 2023. https:\/\/doi.org\/10.17632\/5DPC5GBGPZ.2."},{"key":"2026070310561169259_j_comp-2025-0058_ref_050","doi-asserted-by":"crossref","unstructured":"I. Sa et al.., \u201cWeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming,\u201d Remote Sens., vol.\u00a010, no.\u00a09, p.\u00a01423, 2018, https:\/\/doi.org\/10.3390\/RS10091423.","DOI":"10.3390\/rs10091423"},{"key":"2026070310561169259_j_comp-2025-0058_ref_051","doi-asserted-by":"crossref","unstructured":"I. Koshelev, M. Savinov, A. Menshchikov, and A. Somov, \u201cDrone-aided detection of weeds: Transfer learning for embedded image processing,\u201d IJSTA, vol.\u00a016, pp.\u00a0102\u2013111, 2023, https:\/\/doi.org\/10.1109\/JSTARS.2022.3224657.","DOI":"10.1109\/JSTARS.2022.3224657"},{"key":"2026070310561169259_j_comp-2025-0058_ref_052","doi-asserted-by":"crossref","unstructured":"N. Genze, R. Ajekwe, Z. G\u00fcreli, F. Haselbeck, M. Grieb, and D. G. Grimm, \u201cDeep learning-based early weed segmentation using motion blurred UAV images of sorghum fields,\u201d Comput. Electron. Agric., vol.\u00a0202, p.\u00a0107388, 2022, https:\/\/doi.org\/10.1016\/J.COMPAG.2022.107388.","DOI":"10.1016\/j.compag.2022.107388"},{"key":"2026070310561169259_j_comp-2025-0058_ref_053","doi-asserted-by":"crossref","unstructured":"H. Huang, J. Deng, Y. Lan, A. Yang, X. Deng, and L. Zhang, \u201cA fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery,\u201d PLoS One, vol.\u00a013, no.\u00a04, p.\u00a0e0196302, 2018, https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0196302.","DOI":"10.1371\/journal.pone.0196302"},{"key":"2026070310561169259_j_comp-2025-0058_ref_054","unstructured":"M. Krestenitis et al.., \u201cCoFly-WeedDB: A UAV image dataset for weed detection and species identification\u201d. [Data set]. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.6697343."},{"key":"2026070310561169259_j_comp-2025-0058_ref_055","doi-asserted-by":"crossref","unstructured":"I. Sa et al.., \u201cWeedNet: Dense semantic weed classification using multispectral images and MAV for smart farming,\u201d IEEE Robot. Autom. Lett., vol.\u00a03, no.\u00a01, pp.\u00a0588\u2013595, 2018, https:\/\/doi.org\/10.1109\/LRA.2017.2774979.","DOI":"10.1109\/LRA.2017.2774979"},{"key":"2026070310561169259_j_comp-2025-0058_ref_056","unstructured":"N. Rai et al.. \u201cImageWeeds: An image dataset consisting of weeds in multiple formats to advance computer vision algorithms for real-time weed identification and spot spraying application,\u201d Mendeley Data, vol. 2, 2023. https:\/\/doi.org\/10.17632\/8KJCZTBJZ2.2."},{"key":"2026070310561169259_j_comp-2025-0058_ref_057","unstructured":"J. Valente and L. Kooistra, \u201cDataset on UAV high-resolution images from grassland with broad-leaved dock (Rumex Obtusifolius)\u201d. [Data set]. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.5119205."},{"key":"2026070310561169259_j_comp-2025-0058_ref_058","doi-asserted-by":"crossref","unstructured":"A. dos Santos Ferreira, D. Matte Freitas, G. Gon\u00e7alves da Silva, H. Pistori, and M. Theophilo Folhes, \u201cWeed detection in soybean crops using ConvNets,\u201d Comput. Electron. Agric., vol.\u00a0143, pp.\u00a0314\u2013324, 2017, https:\/\/doi.org\/10.1016\/J.COMPAG.2017.10.027.","DOI":"10.1016\/j.compag.2017.10.027"},{"key":"2026070310561169259_j_comp-2025-0058_ref_059","doi-asserted-by":"crossref","unstructured":"M.-D. Yang, H.-H. Tseng, Y.-C. Hsu, C.-Y. Yang, M.-H. Lai, and D.-H. Wu, \u201cA UAV open dataset of rice paddies for deep learning practice,\u201d Remote Sens. (Basel), vol.\u00a013, no.\u00a07, 2021, https:\/\/doi.org\/10.3390\/rs13071358.","DOI":"10.3390\/rs13071358"},{"key":"2026070310561169259_j_comp-2025-0058_ref_060","doi-asserted-by":"crossref","unstructured":"I. L. Bretas et al.., \u201cDetection and mapping of Amaranthus spinosus L. in bermudagrass pastures using drone imagery and deep learning for a site\u2010specific weed management,\u201d Agron. J., vol.\u00a0116, no.\u00a03, pp.\u00a0990\u20131002, 2024, https:\/\/doi.org\/10.1002\/agj2.21545.","DOI":"10.1002\/agj2.21545"},{"key":"2026070310561169259_j_comp-2025-0058_ref_061","doi-asserted-by":"crossref","unstructured":"J. Anderegg et al.., \u201cOn-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat,\u201d Comput. Electron. Agric., vol.\u00a0204, p.\u00a0107558, 2023, https:\/\/doi.org\/10.1016\/j.compag.2022.107558.","DOI":"10.1016\/j.compag.2022.107558"},{"key":"2026070310561169259_j_comp-2025-0058_ref_062","doi-asserted-by":"crossref","unstructured":"Q. Wang et al.., \u201cAn image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal,\u201d Comput. Electron. Agric., vol.\u00a0188, p.\u00a0106320, 2021, https:\/\/doi.org\/10.1016\/j.compag.2021.106320.","DOI":"10.1016\/j.compag.2021.106320"},{"key":"2026070310561169259_j_comp-2025-0058_ref_063","unstructured":"M. V. Ornhag, P. Persson, M. Wadenback, K. Astrom, and A. Heyden, \u201cEfficient real-time radial distortion correction for UAVs,\u201d in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp.\u00a01750\u20131759."},{"key":"2026070310561169259_j_comp-2025-0058_ref_064","unstructured":"Roboflow Docs, \u201cCreate a project | Roboflow Docs 2024,\u201d [Online]. Available: https:\/\/docs.roboflow.com\/datasets\/create-a-project [Accessed: Dec. 27, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_065","unstructured":"J. Solawetz, \u201cTrain, validation, test split for machine learning,\u201d [Online]. Available: https:\/\/blog.roboflow.com\/train-test-split\/ [Accessed: Dec. 27, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_066","unstructured":"Ultralytics, \u201cModel training with Ultralytics YOLO,\u201d [Online]. Available: https:\/\/docs.ultralytics.com\/modes\/train\/#train-settings [Accessed: Mar. 27, 2025]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_067","unstructured":"Ultralytics, \u201cRT-DETR (real-time detection transformer) \u2013 Ultralytics docs,\u201d [Online]. Available: https:\/\/docs.ultralytics.com\/models\/rtdetr\/#pre-trained-models [Accessed: Mar. 19, 2025]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_068","unstructured":"Kaggle, \u201cHow to use Kaggle,\u201d [Online]. Available: https:\/\/www.kaggle.com\/docs [Accessed: Apr. 29, 2024]."},{"key":"2026070310561169259_j_comp-2025-0058_ref_069","unstructured":"Ultralytics, \u201cUltralytics YOLOv11: Code repository,\u201d 2024. [Online]. Available at: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"2026070310561169259_j_comp-2025-0058_ref_070","doi-asserted-by":"crossref","unstructured":"R. Padilla, S. L. Netto, and E. A. B. Da Silva, \u201cA survey on performance metrics for object-detection algorithms,\u201d in International Conference on Systems, Signals, and Image Processing, IEEE, 2020, pp. 237\u2013242.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"2026070310561169259_j_comp-2025-0058_ref_071","doi-asserted-by":"crossref","unstructured":"A. Juma, J. Rodr\u00edguez, J. Caraguay, M. Naranjo, A. Qui\u00f1a-Mera, and I. Garc\u00eda-Santill\u00e1n, \u201cIntegration and evaluation of social networks in virtual learning environments: A case study,\u201d Commun. Comput. Inf. Sci., vol.\u00a0895, pp.\u00a0245\u2013258, 2019, https:\/\/doi.org\/10.1007\/978-3-030-05532-5_18.","DOI":"10.1007\/978-3-030-05532-5_18"},{"key":"2026070310561169259_j_comp-2025-0058_ref_072","unstructured":"D. Lind, W. Marchal, and S. Wathen, Basic Statistics in Business and Economics, 10th ed. Columbus, OH, McGraw Hill, 2022."},{"key":"2026070310561169259_j_comp-2025-0058_ref_073","doi-asserted-by":"crossref","unstructured":"L. Sandoval-Pillajo, M. Pusd\u00e1-Chulde, J. Pazos-Morillo, P. Granda-Gudi\u00f1o, and I. Garc\u00eda-Santill\u00e1n, \u201cMulti-class weed quantification based on U-net convolutional neural networks using UAV imagery,\u201d Appl. Sci., vol.\u00a016, no.\u00a07, p.\u00a03149, 2026, https:\/\/doi.org\/10.3390\/app16073149.","DOI":"10.3390\/app16073149"}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0058\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:00:02Z","timestamp":1783076402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0058\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,7,2]]},"published-print":{"date-parts":[[2026,1,23]]}},"alternative-id":["10.1515\/comp-2025-0058"],"URL":"https:\/\/doi.org\/10.1515\/comp-2025-0058","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"article-number":"20250058"}}