{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:19:11Z","timestamp":1783063151494,"version":"3.54.6"},"reference-count":45,"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":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["101007702"],"award-info":[{"award-number":["101007702"]}],"id":[{"id":"10.13039\/100010665","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.2026.111833","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:24:12Z","timestamp":1777494252000},"page":"111833","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Large-scale orange fruit dataset for localization, classification and ripening assessment under varying environments"],"prefix":"10.1016","volume":"248","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-9833","authenticated-orcid":false,"given":"Alessandro","family":"Carella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7580-295X","authenticated-orcid":false,"given":"Baptiste Paul Ernest","family":"Lucas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5403-3911","authenticated-orcid":false,"given":"Safouane","family":"El Ghazouali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9881-1595","authenticated-orcid":false,"given":"Pedro Tomas","family":"Bulacio Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2537-3781","authenticated-orcid":false,"given":"Roberto","family":"Massenti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2562-9932","authenticated-orcid":false,"given":"Francesca","family":"Venturini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6060-5365","authenticated-orcid":false,"given":"Umberto","family":"Michelucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2568-2880","authenticated-orcid":false,"given":"Riccardo","family":"Lo Bianco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111833_b1","series-title":"YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain","author":"Alif","year":"2024"},{"issue":"10","key":"10.1016\/j.compag.2026.111833_b2","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11760-025-04397-w","article-title":"Implementation of YOLO-CLIP fusion algorithm for fall detection","volume":"19","author":"An","year":"2025","journal-title":"Signal Image Video Process."},{"issue":"3","key":"10.1016\/j.compag.2026.111833_b3","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1002\/agj2.21330","article-title":"Detection of on-tree chestnut fruits using deep learning and RGB unmanned aerial vehicle imagery for estimation of yield and fruit load","volume":"116","author":"Arakawa","year":"2024","journal-title":"Agron. J."},{"key":"10.1016\/j.compag.2026.111833_b4","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.biosystemseng.2021.10.009","article-title":"A convolutional neural network approach to detecting fruit physiological disorders and maturity in \u2018Abb\u00e9 F\u00e9tel\u2019 pears","volume":"212","author":"Bonora","year":"2021","journal-title":"Biosyst. Eng."},{"issue":"6","key":"10.1016\/j.compag.2026.111833_b5","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1007\/s11119-024-10139-8","article-title":"A computer vision system for apple fruit sizing by means of low-cost depth camera and neural network application","volume":"25","author":"Bortolotti","year":"2024","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2026.111833_b6","series-title":"2025 44th Chinese Control Conference","first-page":"8833","article-title":"An optimal feature selection fusion method of visual models for CLIP","author":"Cao","year":"2025"},{"key":"10.1016\/j.compag.2026.111833_b7","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1294195","article-title":"Testing effects of vapor pressure deficit on fruit growth: A comparative approach using peach, mango, olive, orange, and loquat","volume":"14","author":"Carella","year":"2023","journal-title":"Front. Plant. Sci."},{"key":"10.1016\/j.compag.2026.111833_b8","series-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.compag.2026.111833_b9","series-title":"Roboflow","author":"Dwyer","year":"2024"},{"issue":"12","key":"10.1016\/j.compag.2026.111833_b10","doi-asserted-by":"crossref","first-page":"10788","DOI":"10.1109\/LRA.2024.3474473","article-title":"CitDet: A benchmark dataset for citrus fruit detection","volume":"9","author":"James","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.compag.2026.111833_b11","series-title":"YOLO evolution: A comprehensive benchmark and architectural review of YOLOv12, YOLO11, and their previous versions","author":"Jegham","year":"2025"},{"key":"10.1016\/j.compag.2026.111833_b12","series-title":"Ultralytics YOLOv5","author":"Jocher","year":"2020"},{"key":"10.1016\/j.compag.2026.111833_b13","series-title":"Ultralytics YOLOv8","author":"Jocher","year":"2023"},{"key":"10.1016\/j.compag.2026.111833_b14","series-title":"Ultralytics YOLOv5, YOLOv8 and vision AI models","author":"Jocher","year":"2023"},{"key":"10.1016\/j.compag.2026.111833_b15","series-title":"Ultralytics YOLO11","author":"Jocher","year":"2024"},{"key":"10.1016\/j.compag.2026.111833_b16","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":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.111833_b17","doi-asserted-by":"crossref","DOI":"10.1088\/1755-1315\/922\/1\/012001","article-title":"Ablation studies on yolofruit detection algorithm for fruit harvesting robot using deep learning","volume":"922","author":"Lawal","year":"2021","journal-title":"IOP Conf. Ser Earth Env. Sci."},{"issue":"2","key":"10.1016\/j.compag.2026.111833_b18","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1007\/s13042-024-02321-1","article-title":"Single-stage zero-shot object detection network based on CLIP and pseudo-labeling","volume":"16","author":"Li","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"1","key":"10.1016\/j.compag.2026.111833_b19","doi-asserted-by":"crossref","DOI":"10.3390\/agriculture14010114","article-title":"AG-YOLO: A rapid citrus fruit detection algorithm with global context fusion","volume":"14","author":"Lin","year":"2024","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111833_b20","series-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2015"},{"key":"10.1016\/j.compag.2026.111833_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106132","article-title":"Design, development, and performance evaluation of a robot for yield estimation of kiwifruit","volume":"185","author":"Massah","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.111833_b22","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/jsfa.7061","article-title":"Huanglongbing modifies quality components and flavonoid content of \u2018Valencia\u2019 oranges","volume":"96","author":"Massenti","year":"2016","journal-title":"J. Sci. Food Agric."},{"key":"10.1016\/j.compag.2026.111833_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106533","article-title":"Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions","volume":"191","author":"Mirhaji","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.compag.2026.111833_b24","doi-asserted-by":"crossref","DOI":"10.3390\/agronomy10020164","article-title":"Fruit yield and quality of \u2018Valencia\u2019 orange trees under long-term partial rootzone drying","volume":"10","author":"Mossad","year":"2020","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111833_b25","series-title":"AgriCLIP: Adapting CLIP for agriculture and livestock via domain-specialized cross-model alignment","author":"Nawaz","year":"2024"},{"key":"10.1016\/j.compag.2026.111833_b26","first-page":"303","article-title":"Evaluation of YOLO efficiency in automatic orange detection in multi-exposure images","volume":"X-3-2024","author":"Oviedo Espinosa","year":"2024","journal-title":"ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"10.1016\/j.compag.2026.111833_b27","series-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"issue":"4","key":"10.1016\/j.compag.2026.111833_b28","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/jlpea12040051","article-title":"BIoU: An improved bounding box regression for object detection","volume":"12","author":"Ravi","year":"2022","journal-title":"J. Low Power Electron. Appl."},{"key":"10.1016\/j.compag.2026.111833_b29","series-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2016"},{"key":"10.1016\/j.compag.2026.111833_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105214","article-title":"An attribution-based pruning method for real-time mango detection with YOLO network","volume":"169","author":"Shi","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"7","key":"10.1016\/j.compag.2026.111833_b31","doi-asserted-by":"crossref","first-page":"731","DOI":"10.3390\/agriculture15070731","article-title":"Determination of optimal dataset characteristics for improving YOLO performance in agricultural object detection","volume":"15","author":"Song","year":"2025","journal-title":"Agriculture"},{"issue":"3","key":"10.1016\/j.compag.2026.111833_b32","article-title":"Improved YOLO-based real-time brinjal detection algorithm for vision modules in harvesting robots","volume":"7","author":"Tamilarasi","year":"2025","journal-title":"Eng. Res. Express"},{"key":"10.1016\/j.compag.2026.111833_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105348","article-title":"Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation","volume":"173","author":"Vasconez","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111833_b34","series-title":"Automatic data curation for self-supervised learning: A clustering-based approach","author":"Vo","year":"2024"},{"issue":"3","key":"10.1016\/j.compag.2026.111833_b35","doi-asserted-by":"crossref","first-page":"559","DOI":"10.3390\/rs14030559","article-title":"A review of deep learning in multiscale agricultural sensing","volume":"14","author":"Wang","year":"2022","journal-title":"Remote. Sens."},{"key":"10.1016\/j.compag.2026.111833_b36","series-title":"YOLOv10: Real-time end-to-end object detection","author":"Wang","year":"2024"},{"key":"10.1016\/j.compag.2026.111833_b37","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"1991","article-title":"Uni-YOLO: Vision-language model-guided YOLO for robust and fast universal detection in the open world","author":"Wang","year":"2024"},{"issue":"9","key":"10.1016\/j.compag.2026.111833_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2024.102220","article-title":"DNE-YOLO: A method for apple fruit detection in diverse natural environments","volume":"36","author":"Wu","year":"2024","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"10.1016\/j.compag.2026.111833_b39","article-title":"Improved RT-DETR and its application to fruit ripeness detection","volume":"16","author":"Wu","year":"2025","journal-title":"Front. Plant Sci."},{"issue":"4","key":"10.1016\/j.compag.2026.111833_b40","doi-asserted-by":"crossref","DOI":"10.4081\/jae.2024.1654","article-title":"AC-YOLO: Citrus detection in the natural environment of orchards","volume":"55","author":"Xiao","year":"2024","journal-title":"J. Agric. Eng."},{"issue":"11","key":"10.1016\/j.compag.2026.111833_b41","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.3390\/agriculture15111173","article-title":"E-CLIP: An enhanced CLIP-based visual language model for fruit detection and recognition","volume":"15","author":"Zhang","year":"2025","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2026.111833_b42","series-title":"Shape-IoU: More accurate metric considering bounding box shape and scale","author":"Zhang","year":"2024"},{"issue":"29","key":"10.1016\/j.compag.2026.111833_b43","doi-asserted-by":"crossref","first-page":"44697","DOI":"10.1007\/s11042-023-15548-x","article-title":"Rapid computer vision detection of apple diseases based on AMCFNet","volume":"82","author":"Zhang","year":"2023","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"10.1016\/j.compag.2026.111833_b44","doi-asserted-by":"crossref","first-page":"16848","DOI":"10.1038\/s41598-024-67526-4","article-title":"YOLO-Granada: A lightweight attentioned yolo for pomegranates fruit detection","volume":"14","author":"Zhao","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.compag.2026.111833_b45","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16965","article-title":"DETRs beat YOLOs on real-time object detection","author":"Zhao","year":"2024"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016816992600428X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S016816992600428X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:50:52Z","timestamp":1783061452000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S016816992600428X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":45,"alternative-id":["S016816992600428X"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111833","relation":{"is-supplemented-by":[{"id-type":"uri","id":"https:\/\/data.mendeley.com\/datasets\/93f32zgkxz\/1","asserted-by":"subject"}]},"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":"Large-scale orange fruit dataset for localization, classification and ripening assessment under varying environments","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111833","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111833"}}