{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:13:54Z","timestamp":1778285634699,"version":"3.51.4"},"reference-count":61,"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\/501100002365","name":"China Agricultural University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32572218"],"award-info":[{"award-number":["32572218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114733","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:45:00Z","timestamp":1775522700000},"page":"114733","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["An enhanced you only look once model for multi-class apple detection in natural orchard environments"],"prefix":"10.1016","volume":"176","author":[{"given":"Xiaohang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8953-8530","authenticated-orcid":false,"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangfan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanjia","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chayan Kumer","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.engappai.2026.114733_bib1","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.3390\/s23083810","article-title":"Recognition and counting of apples in a dynamic state using a 3D camera and deep learning algorithms for robotic harvesting systems","volume":"23","author":"Abeyrathna","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114733_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108164","article-title":"The Monash Apple Retrieving System: a review on system intelligence and apple harvesting performance","volume":"213","author":"Au","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"6","key":"10.1016\/j.engappai.2026.114733_bib3","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1002\/rob.21937","article-title":"Development of a sweet pepper harvesting robot","volume":"37","author":"Arad","year":"2020","journal-title":"J. Field Robot."},{"key":"10.1016\/j.engappai.2026.114733_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2022.103635","article-title":"A weakly-supervised approach for flower\/fruit counting in apple orchards","volume":"138","author":"Bhattarai","year":"2022","journal-title":"Comput. Ind."},{"issue":"6","key":"10.1016\/j.engappai.2026.114733_bib5","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.patrec.2021.04.022","article-title":"Deep learning-based apple detection using a suppression mask R-CNN","volume":"147","author":"Chu","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.engappai.2026.114733_bib6","article-title":"O2RNet: Occluder-occludee relational network for robust apple detection in clustered orchard environments. Smart","volume":"5","author":"Chu","year":"2023","journal-title":"Agr. Technol."},{"issue":"6","key":"10.1016\/j.engappai.2026.114733_bib7","first-page":"258","article-title":"Apple growth status and posture recognition using improved YOLOv7","volume":"40","author":"Chen","year":"2024","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"10.1016\/j.engappai.2026.114733_bib8","article-title":"Estimating depth from RGB images using deep-learning for robotic applications in apple orchards. Smart","volume":"6","author":"Divyanth","year":"2023","journal-title":"Agr. Technol."},{"key":"10.1016\/j.engappai.2026.114733_bib9","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"764","article-title":"Deformable convolutional networks","author":"Dai","year":"2017"},{"key":"10.1016\/j.engappai.2026.114733_bib10","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.biosystemseng.2020.07.007","article-title":"Faster R-CNN based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting","volume":"197","author":"Fu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.engappai.2026.114733_bib11","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.biosystemseng.2019.08.017","article-title":"Fruit detection in an apple orchard using a mobile terrestrial laser scanner","volume":"187","author":"Gen\u00e9-Mola","year":"2019","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.engappai.2026.114733_bib12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.compag.2015.10.022","article-title":"Apple crop-load estimation with over-the-row machine vision system","volume":"120","author":"Gongal","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","article-title":"Sensors and systems for fruit detection and localization: a review","volume":"116","author":"Gongal","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105634","article-title":"Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN","volume":"176","author":"Gao","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.engappai.2026.114733_bib15","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/LRA.2020.2965061","article-title":"MinneApple: a benchmark dataset for apple detection and segmentation","volume":"5","author":"H\u00e4ni","year":"2020","journal-title":"IEEE Rob. Autom. Lett."},{"issue":"19","key":"10.1016\/j.engappai.2026.114733_bib16","first-page":"131","article-title":"Fusion of the lightweight network and visual attention mechanism to detect apples in orchard environment","volume":"38","author":"Hu","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"issue":"2","key":"10.1016\/j.engappai.2026.114733_bib17","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1002\/rob.21902","article-title":"A comparative study of fruit detection and counting methods for yield mapping in apple orchards","volume":"37","author":"H\u00e4ni","year":"2020","journal-title":"J. Field Robot."},{"key":"10.1016\/j.engappai.2026.114733_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105380","article-title":"Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot","volume":"172","author":"Jia","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"20","key":"10.1016\/j.engappai.2026.114733_bib19","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.3390\/s19204599","article-title":"Fruit detection and segmentation for apple harvesting using visual sensor in orchards","volume":"19","author":"Kang","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114733_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108781","article-title":"Occluded apples orientation estimator based on deep learning model for robotic harvesting","volume":"219","author":"Kok","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"7","key":"10.1016\/j.engappai.2026.114733_bib21","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.3390\/agronomy10071016","article-title":"Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot","volume":"10","author":"Kuznetsova","year":"2020","journal-title":"Agronomy"},{"key":"10.1016\/j.engappai.2026.114733_bib22","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."},{"key":"10.1016\/j.engappai.2026.114733_bib23","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning \u2013 method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.105108","article-title":"Fast implementation of real-time fruit detection in apple orchards using deep learning","volume":"168","author":"Kang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109118","article-title":"Faster-YOLO-AP: a lightweight apple detection algorithm based on improved YOLOv8 with a new efficient PDWConv in orchard","volume":"223","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib26","doi-asserted-by":"crossref","first-page":"15221","DOI":"10.1109\/ACCESS.2021.3053167","article-title":"YOLOMuskmelon: quest for fruit detection speed and accuracy using deep learning","volume":"9","author":"Lawal","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114733_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107834","article-title":"ORB-Livox: a real-time dynamic system for fruit detection and localization","volume":"209","author":"Liu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106954","article-title":"A visual identification method for the apple growth forms in the orchard","volume":"197","author":"Lv","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"10","key":"10.1016\/j.engappai.2026.114733_bib29","first-page":"2323","article-title":"DC-YOLOv8: smallsize object detection algorithm based on camera sensor","volume":"12","author":"Lou","year":"2023","journal-title":"Electronics-Switz."},{"issue":"1","key":"10.1016\/j.engappai.2026.114733_bib30","first-page":"209","article-title":"Detecting chestnuts using improved lightweight YOLOv8","volume":"40","author":"Li","year":"2024","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"10.1016\/j.engappai.2026.114733_bib31","first-page":"740","article-title":"Microsoft COCO: common objects in context","author":"Lin","year":"2014","journal-title":"ECCV"},{"key":"10.1016\/j.engappai.2026.114733_bib32","doi-asserted-by":"crossref","first-page":"9102","DOI":"10.1109\/ACCESS.2020.2964608","article-title":"Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application","volume":"8","author":"Mazzia","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114733_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.postharvbio.2023.112587","article-title":"Fruit sizing using AI: a review of methods and challenges","volume":"206","author":"Miranda","year":"2023","journal-title":"Postharvest Biol. Technol."},{"key":"10.1016\/j.engappai.2026.114733_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106562","article-title":"Vision systems for harvesting robots: produce detection and localization","volume":"192","author":"Montoya-Cavero","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108832","article-title":"Smart solutions for capsicum harvesting: unleashing the power of YOLO for detection, segmentation, growth stage classification, counting, and real-time mobile identification","volume":"219","author":"Paul","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib36","series-title":"Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation","author":"Powers","year":"2011"},{"key":"10.1016\/j.engappai.2026.114733_bib37","series-title":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","first-page":"237","article-title":"A survey on performance metrics for object-detection algorithms","author":"Padilla","year":"2020"},{"key":"10.1016\/j.engappai.2026.114733_bib38","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1002\/rob.22230","article-title":"Towards autonomous selective harvesting: a review of robot perception, robot design, motion planning and control","volume":"41","author":"Rajendran","year":"2023","journal-title":"J. Field Robot."},{"issue":"2","key":"10.1016\/j.engappai.2026.114733_bib39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s42853-023-00190-0","article-title":"A two-stage deep-learning model for detection and occlusion-based classification of Kashmiri orchard apples for robotic harvesting","volume":"48","author":"Rathore","year":"2023","journal-title":"J. Biosyst. Eng."},{"issue":"6","key":"10.1016\/j.engappai.2026.114733_bib40","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1002\/rob.21715","article-title":"Design, integration, and field evaluation of a robotic apple harvester","volume":"34","author":"Silwal","year":"2017","journal-title":"J. Field Robot."},{"issue":"15","key":"10.1016\/j.engappai.2026.114733_bib41","first-page":"314","article-title":"Apple detection in complex orchard environment based on improved RetinaNet","volume":"38","author":"Sun","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"10.1016\/j.engappai.2026.114733_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113594","article-title":"A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture","volume":"159","author":"Saedi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114733_bib43","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.isatra.2024.12.023","article-title":"End-to-end multi-scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples","volume":"157","author":"Sun","year":"2025","journal-title":"Isa T"},{"key":"10.1016\/j.engappai.2026.114733_bib44","doi-asserted-by":"crossref","first-page":"43436","DOI":"10.1109\/ACCESS.2024.3378261","article-title":"Immature green apple detection and sizing in commercial orchards using YOLOv8 and shape fitting techniques","volume":"12","author":"Sapkota","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114733_bib45","doi-asserted-by":"crossref","DOI":"10.34133\/2022\/9892464","article-title":"BFP Net: balanced feature pyramid network for small apple detection in complex orchard environment","volume":"2022","author":"Sun","year":"2022","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.engappai.2026.114733_bib46","series-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"443","article-title":"No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects","author":"Sunkara","year":"2022"},{"key":"10.1016\/j.engappai.2026.114733_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106052","article-title":"Improved multi-classes kiwifruit detection in orchard to avoid collisions during robotic picking","volume":"182","author":"Suo","year":"2021","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.engappai.2026.114733_bib48","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/adb2ad","article-title":"Efficient feature fusion network for small objects detection of traffic signs based on cross-dimensional and dual-domain information","volume":"36","author":"Tao","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114733_bib49","series-title":"Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism","author":"Tong","year":"2023"},{"issue":"4","key":"10.1016\/j.engappai.2026.114733_bib50","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1002\/rob.22000","article-title":"Automation and robotics in the cultivation of pome fruit: where do we stand today?","volume":"38","author":"Verbiest","year":"2021","journal-title":"J. Field Robot."},{"key":"10.1016\/j.engappai.2026.114733_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109164","article-title":"Assessing a multi-camera system to enhance fruit visibility for robotic harvesting in a V-trellised apple orchard","volume":"224","author":"Villacr\u00e9s","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109195","article-title":"Using Learning from Demonstration (LfD) to perform the complete apple harvesting task","volume":"224","author":"van de Ven","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107513","article-title":"Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms","volume":"204","author":"Villacr\u00e9s","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.engappai.2026.114733_bib54","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/rob.21890","article-title":"Improvements to and large\u2010scale evaluation of a robotic kiwifruit harvester","volume":"37","author":"Williams","year":"2020","journal-title":"J. Field Robot."},{"key":"10.1016\/j.engappai.2026.114733_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108833","article-title":"NVW-YOLOv8s: an improved YOLOv8s network for real-time detection and segmentation of tomato fruits at different ripeness stages","volume":"219","author":"Wang","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"9","key":"10.1016\/j.engappai.2026.114733_bib56","first-page":"28","article-title":"Real-time apple picking pattern recognition for picking robot based on improved YOLOv5m","volume":"53","author":"Yan","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"issue":"9","key":"10.1016\/j.engappai.2026.114733_bib57","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.3390\/rs13091619","article-title":"A real-time apple targets detection method for picking robot based on improved YOLOv5","volume":"13","author":"Yan","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2026.114733_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105384","article-title":"Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting","volume":"173","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"17","key":"10.1016\/j.engappai.2026.114733_bib59","doi-asserted-by":"crossref","first-page":"4150","DOI":"10.3390\/rs14174150","article-title":"An improved apple object detection method based on lightweight YOLOv4 in complex backgrounds","volume":"14","author":"Zhang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2026.114733_bib60","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105606","article-title":"Technology progress in mechanical harvest of fresh market apples","volume":"175","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114733_bib61","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and efficient IOU loss for accurate bounding box regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010158?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010158?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:26:40Z","timestamp":1778282800000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":61,"alternative-id":["S0952197626010158"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114733","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An enhanced you only look once model for multi-class apple detection in natural orchard environments","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114733","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114733"}}