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

Accurate 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.