{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T17:06:01Z","timestamp":1779728761172,"version":"3.53.1"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100008973","name":"University of North Texas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008973","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015747","name":"University Grants Commission of Bangladesh","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015747","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compag.2026.111696","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:09:14Z","timestamp":1775869754000},"page":"111696","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A deep ensemble learning framework for precision image-level weed classification in paddy fields"],"prefix":"10.1016","volume":"248","author":[{"given":"Sadia Afrin","family":"Urmi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.H.M.","family":"Kamal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8205-4939","authenticated-orcid":false,"given":"Gahangir","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111696_b0005","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.cropro.2012.04.024","article-title":"Classification of crops and weeds from digital images: A support vector machine approach","volume":"40","author":"Ahmed","year":"2012","journal-title":"Crop Prot."},{"key":"10.1016\/j.compag.2026.111696_b0010","doi-asserted-by":"crossref","first-page":"9369","DOI":"10.1109\/ACCESS.2024.3354173","article-title":"Advancing bankruptcy forecasting with hybrid machine learning techniques: in-sights from an unbalanced polish dataset","volume":"12","author":"Ainan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.111696_b0015","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.inpa.2019.12.002","article-title":"Weed detection in canola fields using max-imum likelihood classification and deep convolutional neural network","volume":"7","author":"Asad","year":"2020","journal-title":"Information Processing in Agriculture"},{"key":"10.1016\/j.compag.2026.111696_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105590","article-title":"Weed density classification in rice crop using computer vision","volume":"175","author":"Ashraf","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0025","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.3390\/rs10111690","article-title":"Deep learning with unsupervised data labeling for weed detection in line crops in uav images","volume":"10","author":"Bah","year":"2018","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111696_b0030","series-title":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","first-page":"1","article-title":"Weed detection in rice fields using aerial images and neural networks","author":"Barrero","year":"2016"},{"key":"10.1016\/j.compag.2026.111696_b0035","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s11119-008-9083-z","article-title":"Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals","volume":"9","author":"Berge","year":"2008","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0040","series-title":"International conference on artificial intelligence and soft computing","first-page":"517","article-title":"A feature-based machine learning agent for automatic rice and weed discrimination","author":"Cheng","year":"2015"},{"key":"10.1016\/j.compag.2026.111696_b0045","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.compag.2026.111696_b0050","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0308469","article-title":"An intrusion detection model to detect zero-day attacks in unseen data using machine learning","volume":"19","author":"Dai","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.compag.2026.111696_b0055","series-title":"Inter-national workshop on multiple classifier systems","first-page":"1","article-title":"Ensemble methods in machine learning","author":"Dietterich","year":"2000"},{"key":"10.1016\/j.compag.2026.111696_b0060","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1017\/S2040470017000206","article-title":"Roboweedsupport-detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network","volume":"8","author":"Dyrmann","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"10.1016\/j.compag.2026.111696_b0065","doi-asserted-by":"crossref","first-page":"33","DOI":"10.54216\/MOR.030204","article-title":"A review of machine learning models for predicting air quality in urban areas","volume":"3","author":"El-kenawy","year":"2025","journal-title":"Metaheuristic Optimization Rev."},{"key":"10.1016\/j.compag.2026.111696_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122147","article-title":"Greylag goose optimization: Nature-inspired optimization algorithm","volume":"238","author":"El-Kenawy","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compag.2026.111696_b0075","doi-asserted-by":"crossref","first-page":"455","DOI":"10.3390\/s20020455","article-title":"High speed crop and weed identification in lettuce fields for precision weeding","volume":"20","author":"Elstone","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.111696_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105593","article-title":"Im-proving weeds identification with a repository of agricultural pre-trained deep neural networks","volume":"175","author":"Espejo-Garcia","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0085","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b9000","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision agriculture and food security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"10.1016\/j.compag.2026.111696_b0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1746-4811-7-28","article-title":"Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis","volume":"7","author":"Golzarian","year":"2011","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.111696_b0095","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimedia Inform. Retrieval"},{"key":"10.1016\/j.compag.2026.111696_b0100","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106067","article-title":"A survey of deep learning techniques for weed detection from images","volume":"184","author":"Hasan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0110","doi-asserted-by":"crossref","first-page":"49846","DOI":"10.1109\/ACCESS.2023.3276763","article-title":"Detection of basal stem rot disease using deep learning","volume":"11","author":"Haw","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compag.2026.111696_b0115","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.compag.2026.111696_b0120","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.compag.2026.111696_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105520","article-title":"Graph weeds net: a graph-based deep learning method for weed recognition","volume":"174","author":"Hu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0130","series-title":"Proceedings of the IEEE Confer-Ence on Computer Vision and Pattern Recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.compag.2026.111696_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105203","article-title":"Deep localization model for intra-row crop detection in paddy field","volume":"169","author":"Huang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0140","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6474536","article-title":"Paddy crop and weed discrimination: A multiple classifier system approach","volume":"2020","author":"Kamath","year":"2020","journal-title":"Int. J. Agronomy"},{"key":"10.1016\/j.compag.2026.111696_b0145","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comp. Elec. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0150","doi-asserted-by":"crossref","first-page":"865","DOI":"10.32604\/cmc.2022.028550","article-title":"Improved prediction of metamaterial antenna bandwidth using adaptive optimization of lstm","volume":"73","author":"Khafaga","year":"2022","journal-title":"Comp. Mater. Continua"},{"key":"10.1016\/j.compag.2026.111696_b0155","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in neural informa-tion processing systems"},{"key":"10.1016\/j.compag.2026.111696_b0160","series-title":"Combining pattern classifiers: methods and algo-rithms","author":"Kuncheva","year":"2014"},{"key":"10.1016\/j.compag.2026.111696_b0165","first-page":"16","article-title":"Deep learning-based automatic weed detection on onion field","volume":"7","author":"Lee","year":"2018","journal-title":"Smart Media J."},{"key":"10.1016\/j.compag.2026.111696_b0170","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s43154-020-00001-w","article-title":"Weed detection for selective spraying: a review","volume":"1","author":"Liu","year":"2020","journal-title":"Current Robotics Rep."},{"key":"10.1016\/j.compag.2026.111696_b0175","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.compag.2026.111696_b0180","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/LRA.2018.2846289","article-title":"Fully convolutional networks with sequential information for robust crop and weed detection in precision farming","volume":"3","author":"Lottes","year":"2018","journal-title":"IEEE Rob. Autom. Lett."},{"key":"10.1016\/j.compag.2026.111696_b0185","unstructured":"Mehta, C., Chandel, N., Rajwade, Y., 2020. Smart farm mechanization for sustainable indian agriculture."},{"key":"10.1016\/j.compag.2026.111696_b0190","doi-asserted-by":"crossref","first-page":"257","DOI":"10.3390\/agronomy10020257","article-title":"Non-chemical weed management in vegetables by using cover crops: A review","volume":"10","author":"Mennan","year":"2020","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111696_b0195","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.111696_b0200","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.111696_b0205","series-title":"2015 ASABE Annual International Meeting","first-page":"1","article-title":"A review of unmanned aircraft system (uas) applications for agriculture","author":"Myers","year":"2015"},{"key":"10.1016\/j.compag.2026.111696_b0210","doi-asserted-by":"crossref","DOI":"10.1177\/20552076241249661","article-title":"Optimizing lung cancer classification through hyperparameter tuning","volume":"10","author":"Nabeel","year":"2024","journal-title":"Digital Health"},{"key":"10.1016\/j.compag.2026.111696_b0215","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1038\/s41598-018-38343-3","article-title":"Deepweeds: A multiclass weed species image dataset for deep learning","volume":"9","author":"Olsen","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compag.2026.111696_b0220","doi-asserted-by":"crossref","first-page":"471","DOI":"10.3390\/agriengineering2030032","article-title":"A deep learning approach for weed detection in lettuce crops using multispectral images","volume":"2","author":"Osorio","year":"2020","journal-title":"AgriEngineering"},{"key":"10.1016\/j.compag.2026.111696_b0225","first-page":"42","article-title":"An aerial weed detection system for green onion crops using the you only look once (yolov3) deep learning algorithm","volume":"13","author":"Parico","year":"2020","journal-title":"Eng. Agric. Environ. Food"},{"key":"10.1016\/j.compag.2026.111696_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107179","article-title":"Weed detection in paddy field using an improved retinanet network","volume":"199","author":"Peng","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0235","doi-asserted-by":"crossref","unstructured":"Potena, C., Nardi, D., Pretto, A., 2017. Fast and accurate crop and weed identification with summarized train sets for precision agriculture, in: Intelligent Autonomous Systems 14: Proceedings of the 14th Interna-tional Conference IAS-14 14, Springer. pp. 105\u2013121.","DOI":"10.1007\/978-3-319-48036-7_9"},{"key":"10.1016\/j.compag.2026.111696_b0240","unstructured":"Rao, A.N., Ladha, J., 2013. Economic weed management approaches for rice in asia."},{"key":"10.1016\/j.compag.2026.111696_b0245","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, in: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international con-ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, Springer. pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.compag.2026.111696_b0250","series-title":"2021 IEEE International Conference on Imaging Systems and Techniques (IST)","first-page":"1","article-title":"Comparison of one-stage object detection models for weed detection in mulched onions","author":"Sanchez","year":"2021"},{"key":"10.1016\/j.compag.2026.111696_b0255","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","article-title":"Weed detection in soybean crops using convnets","volume":"143","author":"dos Santos Ferreira","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0260","first-page":"271","article-title":"Automated weed detection systems: A review","author":"Shanmugam","year":"2020","journal-title":"KnE Engineering"},{"key":"10.1016\/j.compag.2026.111696_b0265","unstructured":"Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"10.1016\/j.compag.2026.111696_b0270","first-page":"47","article-title":"Deep convolutional neural network models for weed detection in polyhouse grown bell peppers","volume":"6","author":"Subeesh","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0275","first-page":"278","article-title":"Automation and digitization of agriculture using artificial intelligence and internet of things","volume":"5","author":"Subeesh","year":"2021","journal-title":"Artif. Intell. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0280","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"Szegedy","year":"2017"},{"key":"10.1016\/j.compag.2026.111696_b0285","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolu-tions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.compag.2026.111696_b0290","series-title":"International conference on machine learning","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolu-tional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.compag.2026.111696_b0295","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","article-title":"A comparative study of fine-tuning deep learning models for plant disease identification","volume":"161","author":"Too","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0300","doi-asserted-by":"crossref","first-page":"3504","DOI":"10.3390\/math11163504","article-title":"Deep-learning-based classification of bangladeshi medicinal plants using neural ensemble models","volume":"11","author":"Uddin","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.compag.2026.111696_b0305","first-page":"463","article-title":"An unique model for weed and paddy de-tection using regional convolutional neural networks","volume":"72","author":"Vaidhehi","year":"2022","journal-title":"Acta Agriculturae Scandinavica, Section B\u2014Soil & Plant Science"},{"key":"10.1016\/j.compag.2026.111696_b0310","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","article-title":"A review on weed detection using ground-based machine vision and image processing techniques","volume":"158","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0315","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1492","article-title":"Aggregated residual transformations for deep neural networks","author":"Xie","year":"2017"},{"key":"10.1016\/j.compag.2026.111696_b9010","series-title":"Proceedings of the first workshop on evaluation and comparison of NLP systems","first-page":"79","article-title":"Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models","author":"Yacouby","year":"2020"},{"key":"10.1016\/j.compag.2026.111696_b0320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"10.1016\/j.compag.2026.111696_b0325","doi-asserted-by":"crossref","first-page":"901","DOI":"10.3390\/rs13050901","article-title":"Crop row segmentation and detection in paddy fields based on treble-classification otsu and double-dimensional clustering method","volume":"13","author":"Yu","year":"2021","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.111696_b0330","doi-asserted-by":"crossref","first-page":"34","DOI":"10.54216\/JAIM.090104","article-title":"K-nearest neighbors approach to analyze and predict air quality in delhi","volume":"9","author":"Zaki","year":"2025","journal-title":"J. Artificial Intell. Metaheuristics"},{"key":"10.1016\/j.compag.2026.111696_b0335","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014a worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111696_b0340","article-title":"Lms-yolo11n: A lightweight multi-scale weed detection model","volume":"16","author":"Zhang","year":"2025","journal-title":"Int. J. Adv. Comp. Sci. Appl."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926002917?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926002917?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T16:47:39Z","timestamp":1779727659000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926002917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":70,"alternative-id":["S0168169926002917"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111696","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A deep ensemble learning framework for precision image-level weed classification in paddy fields","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111696","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111696"}}