Field-deployable coffee yield estimation from mobile phone images using branch segmentation and occlusion correction

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dc.creatorGautron, Romain
dc.creatorSeurin, Mathieu
dc.creatorRahn, Eric
dc.creatorFaye, Emile
dc.creatorBunn, Christian
dc.date2026-03-03
dc.date2026-04-20T10:23:54Z
dc.date.accessioned2026-06-27T13:24:59Z
dc.descriptionAccurate coffee yield estimation is critical for crop management, labor and financial planning, and value-chain transparency, including compliance with the EU Deforestation Regulation. However, manual cherry counting remains labor-intensive, error-prone, and unreliable as worker fatigue sets in, highlighting the need for automated, scalable alternatives. This study introduces a novel deep-learning framework for automated coffee cherry counting using images captured with low- to mid-range smartphones across diverse smallholder farming contexts. The pipeline combines automated branch segmentation, cherry detection, and a regression-based correction module to account for occluded cherries, accommodating different data-capture modalities. We evaluated the framework on 7,025 annotated images from Colombia, Peru, Honduras, and Uganda, covering both Coffea arabica and Coffea canephora (Robusta) coffee species. Under optimal image-capture conditions (i.e., full background isolation), the model achieved high accuracy, reaching an R 2 of up to 0.96 and reducing the Mean Absolute Percentage Error (MAPE) to as low as 10% at the plot level, outperforming state-of-the-art methods. By reducing manual effort and addressing real-world constraints in smallholder settings, this approach offers a strong foundation for scalable coffee yield estimation. Future research should prioritize human-centered design validation and detailed cost-benefit analyses to support widespread adoption and long-term sustainability.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/182547
dc.identifier.urihttp://hdl.handle.net/123456789/58691
dc.languageen
dc.publisherElsevier BV
dc.rightsOpen Access
dc.sourceGautron, R.; Seurin, M.; Rahn, E.; Faye, E.; Bunn, C. (2026) Field-deployable coffee yield estimation from mobile phone images using branch segmentation and occlusion correction. Smart Agricultural Technology 13: 101901. ISSN: 2772-3755
dc.subjectagriculture
dc.subjectcoffea
dc.subjectcoffee
dc.subjectsmallholders
dc.subjectvalue chains
dc.subjectyield forecasting
dc.subjectimagery
dc.subjectimage processing
dc.titleField-deployable coffee yield estimation from mobile phone images using branch segmentation and occlusion correction
dc.typeJournal Article

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