{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:40:36Z","timestamp":1776922836674,"version":"3.51.2"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.compag.2026.111693","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:08:43Z","timestamp":1774490923000},"page":"111693","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A drone- and deep-learning-based harvest planning model for determining the optimal harvesting sequence of broccoli fields"],"prefix":"10.1016","volume":"247","author":[{"given":"Xiaofei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Lei","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Ao","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Zhiheng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.111693_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108871","article-title":"Yield estimation and health assessment of temperate fruits: a modular framework","volume":"136","author":"Ahmad","year":"2024","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.compag.2026.111693_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2022.103624","article-title":"A survey on smart farming data, applications and techniques","volume":"138","author":"De Alwis","year":"2022","journal-title":"Comput. Ind."},{"key":"10.1016\/j.compag.2026.111693_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural object detection with you only look once (YOLO) algorithm: a bibliometric and systematic literature review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2020.126055","article-title":"Predicting dates of head initiation and yields of broccoli crops grown throughout Scotland","volume":"116","author":"Cammarano","year":"2020","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2026.111693_b0025","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11119-023-10057-1","article-title":"Sweet corn yield prediction using machine learning models and field-level data","volume":"25","author":"Dhaliwal","year":"2024","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0030","unstructured":"FAO, 2023. Food and Agricultural Organization of the United Nations. https\/\/www.fao.693org\/faostat\/en\/#data\/QCL."},{"key":"10.1016\/j.compag.2026.111693_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106812","article-title":"Fruit yield prediction and estimation in orchards: a state-of-the-art comprehensive review for both direct and indirect methods","volume":"195","author":"He","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109875","article-title":"How to assess the digitization and digital effort: a framework for Digitization Footprint (Part 1)","volume":"230","author":"Huang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0045","doi-asserted-by":"crossref","first-page":"10574","DOI":"10.1016\/j.compag.2020.105748","article-title":"A deep learning system for single and overall weight estimation of melons using unmanned aerial vehicle images","volume":"178","author":"Kalantar","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108633","article-title":"Maturity identification and category determination method of broccoli based on semantic segmentation models","volume":"217","author":"Kang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109654","article-title":"Design, integration, and field evaluation of a selective harvesting robot for broccoli","volume":"227","author":"Kang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0060","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s13580-020-00317-8","article-title":"A hybrid decision tool for optimizing broccoli production in a changing climate","volume":"62","author":"Kim","year":"2021","journal-title":"Hortic. Environ. Biotechnol."},{"key":"10.1016\/j.compag.2026.111693_b0065","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-024-10775-6","article-title":"Deep learning implementation of image segmentation in agricultural applications: a comprehensive review","volume":"57","author":"Lei","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compag.2026.111693_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108412","article-title":"Label-efficient learning in agriculture: a comprehensive review","volume":"215","author":"Li","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0075","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11119-025-10220-w","article-title":"Enhanced visual detection of litchi fruit in complex natural environments based on unmanned aerial vehicle (UAV) remote sensing","volume":"26","author":"Liang","year":"2025","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109344","article-title":"Dynamic monitoring and counting for lotus flowers and seedpods with UAV based on improved YOLOv7-tiny","volume":"225","author":"Lyu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0085","doi-asserted-by":"crossref","first-page":"731","DOI":"10.3390\/rs14030731","article-title":"Assessment of different object detectors for the maturity level classification of broccoli crops using UAV imagery","volume":"14","author":"Psiroukis","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2026.111693_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108543","article-title":"A fast and efficient approach to estimate wild blueberry yield using machine learning with drone photography: Flight altitude, sampling method and model effects","volume":"216","author":"Qu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0095","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K.V., Carion, N., Wu, C.-Y., Girshick, R., Doll\u00e1r, P., Feichtenhofer, C., Fair, M., 2024. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. https:\/\/github.com\/facebookresearch\/sam2."},{"key":"10.1016\/j.compag.2026.111693_b0100","doi-asserted-by":"crossref","first-page":"0086","DOI":"10.34133\/plantphenomics.0086","article-title":"Drone-based harvest data prediction can reduce on-farm food loss and improve farmer income","volume":"5","author":"Wang","year":"2023","journal-title":"Plant Phenomics"},{"key":"10.1016\/j.compag.2026.111693_b0105","doi-asserted-by":"crossref","first-page":"4101","DOI":"10.1002\/rob.70008","article-title":"In situ detection and measurement of broccoli heads under different lighting conditions using proximal remote sensing","volume":"42","author":"Wang","year":"2025","journal-title":"J. Field Robot."},{"key":"10.1016\/j.compag.2026.111693_b0110","first-page":"1","article-title":"UAV-based RGB imagery for hokkaido pumpkin (Cucurbita max.) detection and yield estimation","volume":"21","author":"Wittstruck","year":"2021","journal-title":"Sensors (Switzerland)."},{"key":"10.1016\/j.compag.2026.111693_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107822","article-title":"A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images","volume":"209","author":"Ye","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0120","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.3390\/agronomy14112496","article-title":"Monitoring of broccoli flower head development in fields using drone imagery and deep learning methods","volume":"14","author":"Zhang","year":"2024","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2026.111693_b0125","first-page":"145","article-title":"Design and experiment of the negative pressure adsorption Cartesian robot system for apple harvesting","volume":"18","author":"Zhang","year":"2025","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"10.1016\/j.compag.2026.111693_b0130","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zheng, Y., Xun, Y., Yang, Q., Wang, Z., 2025. Drone-based remote sensing for yield estimation of xisha watermelon using global scanning, in: Park, D., Liu, C., Lee, DY., Kim, M.J. (Eds.), Robot Intelligence Technology and Applications 9. RiTA 2024. Lecture Notes in Networks and Systems, vol 1419. Springer, Berlin, pp. 17\u201327. https:\/\/doi.org\/10.1007\/978-3-031-92011-0_2.","DOI":"10.1007\/978-3-031-92011-0_2"},{"key":"10.1016\/j.compag.2026.111693_b0135","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s11119-025-10252-2","article-title":"Smart UAV-assisted blueberry maturity monitoring with Mamba-based computer vision","volume":"26","author":"Zhao","year":"2025","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2026.111693_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodchem.2024.138517","article-title":"A comparative metabolomics analysis of phytochemcials and antioxidant activity between broccoli floret and by-products (leaves and stalks)","volume":"443","author":"Zhao","year":"2024","journal-title":"Food Chem."},{"key":"10.1016\/j.compag.2026.111693_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2022.103055","article-title":"An automated, high-performance approach for detecting and characterizing broccoli based on UAV remote-sensing and transformers: a case study from Haining, China","volume":"114","author":"Zhou","year":"2022","journal-title":"Int. J. Appl. Earth Observ. Geoinform."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926002887?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926002887?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:52:05Z","timestamp":1776919925000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926002887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":29,"alternative-id":["S0168169926002887"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111693","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A drone- and deep-learning-based harvest planning model for determining the optimal harvesting sequence of broccoli 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.2026.111693","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":"111693"}}