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Supply chain from plantation up to the consumption stage as explored in the reviewed literature, with corresponding numbers of studies according to the system boundaries. Credits: Scooter by Draftphic; Tractor and trailer by Azam Ishaq; Ship by Jordan Ivey; Truck by Jonathan Li; Trolley by Saifurrijal -Noun Project CCBY3.0.
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... of coffee as an agricultural commodity, more or less processed, or the impacts of coffee drinks. All reviewed studies applied the attributional LCA approach. Details on the overarching methodologies applied are listed in Table S2. The supply chain from plantation up to the consumption of coffee as considered in the literature is summarised in Fig. 2, together with key information on details for the main stages and inputs. Across the reviewed studies, various plantations and processing routes were covered, except for semi-wet (also called honey) coffee, hence not displayed in the figure. As for the farming stage, about one third of the studies did not use primary data for the ...
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... green coffee beans. At the plantation level, despite some mention of quite complex coffee systems, such as agroforestry plots, little attention was paid to this complexity and all potential flows. As indicated in Fig. S2 in Supplementary information, there was no clear difference in the GWP between different cropping system type, with large variabilities within each type. This may be partly due to the fact that the defined cropping farming types were not consistently discriminated against fertiliser inputs that were highly variable across all systems ...
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... Coffee beans are one of the most widely traded agricultural commodities in the world [1,2]. Moreover, coffee beans represent a commodity with significant economic value [3]. ...
... The ReLU function functions by eliminating all negative values produced during the convolution process, permitting only positive values to proceed to the subsequent layer. The mathematical representation of the ReLU function is illustrated in Eq. (2). ...
... Coffee, recognized as one of the three major beverage crops globally [1], is cultivated and consumed extensively around the world [2]. In recent years, China has experienced rapid economic growth and rising living standards, leading to a significant shift in coffee consumption from a niche market predominantly enjoyed by the elite to a more widespread demographic. ...
... where TP represents true positives, and FN represents false negatives. Precision refers to the proportion of true positive samples out of all samples predicted as positive by the model, as shown in Formula (2). It measures the accuracy of the model's positive predictions. ...
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.
The rapidly growing global demand for coffee has fueled proportionate production. However, a comprehensive and consistent understanding of the associated environmental assessment (EIA) across the production-to-consumption value chain is lacking. Despite using EIA tools like life cycle assessment (LCA) and environmental footprint (EF), achieving a unified understanding of coffee's environmental impact throughout this chain remains challenging. We aimed to survey the literature on coffee LCA at length and EF briefly to map coverage areas and analyze thematic trends. We analyzed thematic trends in coffee LCA research. Moreover, we identified five critical groups listed in order of significance: by-products, LCA and broad coffee sustainability matters, coffee and its environmental impacts, waste management, and biochar. Our analysis revealed a shift from traditional environmental assessment of cultivation processes and waste management toward using coffee by-products in refinery research, developing new materials, and biomedical and pharmaceutical functions. We also found that the extant literature has not converged on a standard in both method and scope, resulting in inconsistencies in reporting coffee LCA application settings. The review results contribute by describing and organizing the LCA research status, identifying literature gaps, and guiding progress toward transparent modeling at a global level. This study highlights the need for standardized methodologies and reporting practices in coffee LCA research to improve consistency and comparability.
