{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T06:54:49Z","timestamp":1792047289649,"version":"4.4.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":93,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100022023","name":"Brandenburgische Technische Universit\u00e4t Cottbus-Senftenberg","doi-asserted-by":"publisher","award":["BTUGRS2018_19"],"award-info":[{"award-number":["BTUGRS2018_19"]}],"id":[{"id":"10.13039\/501100022023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011099","name":"Deutsche Gesellschaft f\u00fcr Internationale Zusammenarbeit","doi-asserted-by":"publisher","award":["81275837"],"award-info":[{"award-number":["81275837"]}],"id":[{"id":"10.13039\/501100011099","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Plant Phenomics"],"published-print":{"date-parts":[[2024]]},"DOI":"10.34133\/plantphenomics.0165","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T15:31:24Z","timestamp":1710948684000},"page":"0165","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning"],"prefix":"10.1016","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1423-3396","authenticated-orcid":false,"given":"Juan Camilo","family":"Rivera-Palacio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-8745","authenticated-orcid":false,"given":"Christian","family":"Bunn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-7430","authenticated-orcid":false,"given":"Eric","family":"Rahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisy","family":"Little-Savage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8352-4608","authenticated-orcid":false,"given":"Paul G\u00fcnter","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-3446","authenticated-orcid":false,"given":"Masahiro","family":"Ryo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.34133\/plantphenomics.0165_bib1","unstructured":"Food and Agriculture Organization of the United Nations (FAO). FAOSTAT. accesse date 1 May 2023. https:\/\/www.fao.org\/faostat\/en\/#home"},{"issue":"3","key":"10.34133\/plantphenomics.0165_bib2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10584-006-9066-x","article-title":"Potential impacts of climate change on agriculture: A case of study of coffee production in Veracruz, Mexico","volume":"79","author":"Gay","year":"2006","journal-title":"Clim Chang"},{"issue":"7","key":"10.34133\/plantphenomics.0165_bib3","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s11027-009-9186-5","article-title":"Towards a climate change adaptation strategy for coffee communities and ecosystems in the Sierra Madre de Chiapas, Mexico","volume":"14","author":"Schroth","year":"2009","journal-title":"Mitig Adapt Strateg Glob Chang"},{"issue":"3","key":"10.34133\/plantphenomics.0165_bib4","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10584-011-0058-0","article-title":"Potential for growing Arabica coffee in the extreme south of Brazil in a warmer world","volume":"109","author":"Zullo","year":"2011","journal-title":"Clim Chang"},{"issue":"1","key":"10.34133\/plantphenomics.0165_bib5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0169133","article-title":"Primary and secondary yield losses caused by pests and diseases: Assessment and modeling in coffee","volume":"12","author":"Cerda","year":"2017","journal-title":"PLoS One"},{"issue":"1\u20132","key":"10.34133\/plantphenomics.0165_bib6","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10584-014-1306-x","article-title":"A bitter cup: Climate change profile of global production of Arabica and Robusta coffee","volume":"129","author":"Bunn","year":"2015","journal-title":"Clim Chang"},{"key":"10.34133\/plantphenomics.0165_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112709","article-title":"Integration of sentinel optical and radar data for mapping smallholder coffee production systems in Vietnam","volume":"266","author":"Maskell","year":"2021","journal-title":"Remote Sens Environ"},{"key":"10.34133\/plantphenomics.0165_bib8","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.agee.2012.04.013","article-title":"Analysis of management and site factors to improve the sustainability of smallholder coffee production in Tarraz\u00fa, Costa Rica","volume":"155","author":"Castro-Tanzi","year":"2012","journal-title":"Agric Ecosyst Environ"},{"key":"10.34133\/plantphenomics.0165_bib9","unstructured":"Upreti G, Bittenbender H, Ingamells JL. Rapid estimation of coffee yield. In: International Scientific Association of Coffee, editor. Proceedings of the 1991 ASIC Conference on Coffee Science. San Francisco (CA): ASIC; 1991. p. 585\u2013593."},{"key":"10.34133\/plantphenomics.0165_bib10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.scienta.2013.12.013","article-title":"Evaluation of a non-destructive sampling method and a statistical model for predicting fruit load on individual coffee (Coffea arabica) trees","volume":"167","author":"Castro-Tanzi","year":"2014","journal-title":"Sci Hortic"},{"issue":"2\u20133","key":"10.34133\/plantphenomics.0165_bib11","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S0167-8809(02)00204-9","article-title":"Coffee production, timber, and firewood in traditional and Inga-shaded plantations in southern Mexico","volume":"95","author":"Peeters","year":"2003","journal-title":"Agric Ecosyst Environ"},{"issue":"1","key":"10.34133\/plantphenomics.0165_bib12","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0167-8809(00)00134-1","article-title":"Shade effect on coffee production at the northern Tzeltal zone of the state of Chiapas, Mexico","volume":"80","author":"Soto-Pinto","year":"2000","journal-title":"Agric Ecosyst Environ"},{"issue":"6","key":"10.34133\/plantphenomics.0165_bib13","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s10681-020-02641-7","article-title":"The effect of bienniality on genomic prediction of yield in arabica coffee","volume":"216","author":"Fanelli Carvalho","year":"2020","journal-title":"Euphytica"},{"issue":"1","key":"10.34133\/plantphenomics.0165_bib14","doi-asserted-by":"crossref","first-page":"249","DOI":"10.2134\/agronj2016.03.0166","article-title":"Agrometeorological models for forecasting coffee yield","volume":"109","author":"de Oliveira Aparecido","year":"2017","journal-title":"Agron J"},{"key":"10.34133\/plantphenomics.0165_bib15","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.compag.2018.10.014","article-title":"Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties","volume":"155","author":"Kouadio","year":"2018","journal-title":"Comput Electron Agric"},{"key":"10.34133\/plantphenomics.0165_bib16","article-title":"UAV-based coffee yield prediction utilizing feature selection and deep learning","volume":"1","author":"Barbosa","year":"2021","journal-title":"Smart Agric Technol"},{"key":"10.34133\/plantphenomics.0165_bib17","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.patrec.2020.05.034","article-title":"A computer vision system for automatic cherry beans detection on coffee trees","volume":"136","author":"Rodr\u00edguez","year":"2020","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"10.34133\/plantphenomics.0165_bib18","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/sae2.12036","article-title":"Deep learning for sustainable agriculture needs ecology and human involvement","volume":"2","author":"Ryo","year":"2023","journal-title":"J Sustain Agric Environ"},{"key":"10.34133\/plantphenomics.0165_bib19","unstructured":"Servicio Nacional de Meteorolog\u00eda e Hidrolog\u00eda del Per\u00fa (SENHAMI). Monitoreo Meteorol\u00f3gico Diario. SENHAMI. accesse date 1 May 2023. https:\/\/www.senamhi.gob.pe\/"},{"key":"10.34133\/plantphenomics.0165_bib20","unstructured":"Instituto de Hidrolog\u00eda, Meteorolog\u00eda y Estudios Ambientales (IDEAM). Tiempo y el Clima, IDEAM. accesse date 1 May 2023. http:\/\/www.ideam.gov.co"},{"key":"10.34133\/plantphenomics.0165_bib21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fpls.2021.684328","article-title":"Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques\u2014A review","volume":"12","author":"Maheswari","year":"2021","journal-title":"Front Plant Sci"},{"key":"10.34133\/plantphenomics.0165_bib22","unstructured":"Tkachenko M, Malyuk M, Holmanyuk A, Liubimov (N), Label studio: Data labeling software. Label Studio accesse date 1 May 2023. https:\/\/github.com\/heartexlabs\/label-studio"},{"key":"10.34133\/plantphenomics.0165_bib23","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Comput Electron Agric"},{"key":"10.34133\/plantphenomics.0165_bib24","doi-asserted-by":"crossref","unstructured":"Zhu X, Lyu S, Wang X, Zhao Q. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. arXiv. 2021. https:\/\/doi.org\/10.48550\/arXiv.2108.11539","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"10.34133\/plantphenomics.0165_bib25","doi-asserted-by":"crossref","unstructured":"Redmon J, Santosh D, Ross G, Ali F. You Only Look Once: Unified, real-time object detection. 2016. https:\/\/doi.org\/10.48550\/arXiv.1506.02640","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.34133\/plantphenomics.0165_bib26","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A. YOLO9000: Better, faster, stronger. arXiv. 2016. https:\/\/doi.org\/10.48550\/arXiv.1612.08242","DOI":"10.1109\/CVPR.2017.690"},{"key":"10.34133\/plantphenomics.0165_bib27","unstructured":"Li C. YOLOv6 v3.0: A full-scale reloading. arXiv. 2023. https:\/\/doi.org\/10.48550\/arXiv.2301.05586"},{"key":"10.34133\/plantphenomics.0165_bib28","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: Optimal speed and accuracy of object detection. arXiv. 2020. https:\/\/doi.org\/10.48550\/arXiv.2004.10934"},{"key":"10.34133\/plantphenomics.0165_bib29","unstructured":"Ultralytics, \u201cYOLOv5,\u201d Ultralytics. Access: May 01, 2023. [Online]. Available: https:\/\/docs.ultralytics.com\/yolov5\/"},{"key":"10.34133\/plantphenomics.0165_bib30","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv 2022. https:\/\/doi.org\/10.48550\/arXiv.2207.02696","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"10.34133\/plantphenomics.0165_bib31","unstructured":"Dosovitskiy A. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv. 2020. https:\/\/doi.org\/10.48550\/arXiv.2010.11929"},{"key":"10.34133\/plantphenomics.0165_bib32","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS. CBAM: Convolutional Block Attention Module. arXiv. 2018. https:\/\/doi.org\/10.48550\/arXiv.1807.06521","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.34133\/plantphenomics.0165_bib33","doi-asserted-by":"crossref","unstructured":"Treven J, Cordova-Esparza D. A comprehensive review of yolo architectures in Computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. arXiv. 2024. https:\/\/doi.org\/10.3390\/make5040083","DOI":"10.3390\/make5040083"},{"key":"10.34133\/plantphenomics.0165_bib34","unstructured":"Paszke A. PyTorch: An imperative style, high-performance deep learning library. arXiv. 2019. https:\/\/doi.org\/10.48550\/arXiv.1912.01703"},{"key":"10.34133\/plantphenomics.0165_bib35","unstructured":"Lin T-Y. Microsoft COCO: Common Objects in Context. arXiv. 2014. https:\/\/doi.org\/10.48550\/arXiv.1405.0312"},{"key":"10.34133\/plantphenomics.0165_bib36","series-title":"Proceedings of the 26th conference on Neural Information Processing Systems","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.34133\/plantphenomics.0165_bib37","series-title":"Coffee: Growing, processing, sustainable production: A guidebook for growers, processors, traders, and researchers","year":"2004"},{"issue":"2","key":"10.34133\/plantphenomics.0165_bib38","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.acha.2015.08.007","article-title":"Unregularized online learning algorithms with general loss functions","volume":"42","author":"Ying","year":"2017","journal-title":"Appl Comput Harmon Anal"},{"issue":"1","key":"10.34133\/plantphenomics.0165_bib39","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1121\/1.5118245","article-title":"Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss","volume":"146","author":"Thakur","year":"2019","journal-title":"J Acoust Soc Am"},{"key":"10.34133\/plantphenomics.0165_bib40","doi-asserted-by":"crossref","unstructured":"Babenko A, Slesarev A, Chigorin A, Lempitsky V. Neural codes for image retrieval. arXiv. 2014. https:\/\/doi.org\/10.48550\/arXiv.1404.1777","DOI":"10.1007\/978-3-319-10590-1_38"},{"key":"10.34133\/plantphenomics.0165_bib41","doi-asserted-by":"crossref","first-page":"107827","DOI":"10.1016\/j.compag.2023.107827","article-title":"Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms","volume":"209","author":"Wu","year":"2023","journal-title":"Comput Electron Agric"},{"key":"10.34133\/plantphenomics.0165_bib42","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.compag.2017.03.010","article-title":"Automatic fruit count on coffee branches using computer vision","volume":"137","author":"Ramos","year":"2017","journal-title":"Comput Electron Agric"},{"key":"10.34133\/plantphenomics.0165_bib43","series-title":"Introduction to coffee plant and genetics","year":"2019"}],"container-title":["Plant Phenomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651524002760?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2643651524002760?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.34133\/plantphenomics.0165","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T06:04:15Z","timestamp":1738130655000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2643651524002760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":43,"alternative-id":["S2643651524002760"],"URL":"https:\/\/doi.org\/10.34133\/plantphenomics.0165","relation":{},"ISSN":["2643-6515"],"issn-type":[{"value":"2643-6515","type":"print"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning","name":"articletitle","label":"Article Title"},{"value":"Plant Phenomics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.34133\/plantphenomics.0165","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2024 Juan Camilo Rivera-Palacio et al","name":"copyright","label":"Copyright"}],"article-number":"0165"}}