[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-49":3,"related-49":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":70,"slug":73,"view_count":20,"doi":8,"paper":8,"created_at":74},49,"云南保山\"云智农链\"数字农业平台：咖啡大模型应用场景持续拓展","https:\u002F\u002Fyndaily.yunnan.cn\u002Fhtml\u002F2026\u002F20260728\u002F20260728_001\u002F20260728_001_07.html","云南日报2026-07-28。保山市依托2025腾冲科学家论坛十大成熟学术成果——咖啡种植大模型技术，以\"云智农链\"数字农业平台为载体，将咖啡数字化种植成熟方案向茶叶、石斛、释迦果等高原特色经济作物延伸复用。在隆阳区潞江镇千亩咖啡基地，基地咖啡亩产提升约20%、精品咖啡出品率从68%提高至85%以上、每亩管护成本减少千元、水肥利用与病虫害防治效率显著提升。昌宁县建成红茶智慧大数据平台及全省首个茶叶K检云数据集成中心，搭建标准化智慧茶园四大功能模块；物流园区周转效率提升30%以上，铁路货物装卸时长压缩近60%。",null,"依托2025腾冲科学家论坛十大成熟学术成果——咖啡种植大模型技术，保山市以“云智农链”数字农业平台为载体，将咖啡数字化种植成熟方案向茶叶、石斛、释迦果等高原特色经济作物延伸复用，推动全市高原特色现代农业全方位转型升级。\n\n此前，保山市以腾冲科学家论坛产学研对接为桥梁，联动云南移动保山分公司、多所高校科研团队，在隆阳区潞江镇千亩咖啡基地落地应用咖啡种植智能大模型。该项目集成多模态数据感知、AI智能决策等前沿技术，在田间布设全域感知设备，搭建起从水肥管理到病虫害防控的全闭环数字管控体系，为咖啡种植装上了“数字大脑”。\n\n智能化改造投用后，基地咖啡亩产提升约20％，精品咖啡出品率从68％提高至85％以上，每亩管护成本减少千元，水肥利用与病虫害防治效率显著提升。这套数智化种植体系获得业界广泛认可，成功入选腾冲科学家论坛十大学术成果，形成可复制、可推广的高原经济作物智慧种植范式。\n\n立足咖啡大模型成熟底层技术，保山市推动数智种植经验跨界复用，加速茶产业数字化转型。昌宁县建成红茶智慧大数据平台及全省首个茶叶K检云数据集成中心，搭建标准化智慧茶园四大功能模块，彻底改变茶农“凭经验种茶”的传统模式，实现茶园精准管护、茶企科学布局，助力千年茶乡搭上数字种植快车。\n\n在咖啡、茶叶两大产业试点基础上，保山市持续迭代优化咖啡种植大模型，针对石斛、释迦果等小众特色作物种植痛点，定制专属数字化管护子模型，不断拓宽数字农业应用场景。同时，依托全域5G网络覆盖优势，打通田间种植与后端物流数字通道，并借助蒲缥公铁联运物流园数智管控系统，实现特色农产品智慧仓储分拣。数据显示，物流园区周转效率提升30％以上，铁路货物装卸时长压缩近60％，构建起“智慧种养+数字仓储+智能联运”的完整数字化产业链。\n\n目前，保山数字农业已实现咖啡、茶叶、石斛、释迦果等多品类覆盖，“云智农链”平台落地场景持续拓展，公铁联运智慧物流体系高效运转，科创成果已从示范试点转为常态化应用，持续为高原特色现代农业高质量发展赋能增效。","云南日报 · 2026-07-28","2026-07-27T23:00:00Z","报道",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},16,18,15,9,1,"省级官媒报道，咖啡大模型向多作物复用，有具体数据，时效性好，但属地方动态，影响范围有限。",[24],{"name":10,"url":6},[12,26,27,28,29,30],"数字农业","云南","咖啡大模型","云智农链","高原特色农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,64],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},14,true,[54],"咖啡",[56,54],"茶叶",[],{"code":59,"score":60,"supported":52,"title":61,"abstract":62,"tags":63},"I1",12,[26],[26],[26],{"code":39,"score":65,"supported":52,"title":66,"abstract":68,"tags":69},11,[67],"大模型",[67],[],[71,72],"高原特色农业 咖啡大模型 云智农链 数字农业","高原特色农业 咖啡大模型","高原特色农业咖啡大模型云智农链数字农业-49","2026-07-28T23:53:29.236753Z",{"total":76,"page":21,"page_size":76,"items":77},6,[78,176,266,321,374,432],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":8,"source_name":84,"source_url":81,"published_at":85,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":92,"tags":94,"primary_topic":100,"secondary_topics":101,"classification":103,"search_phrases":137,"slug":140,"view_count":21,"doi":141,"paper":142,"created_at":175},4081,"Aerial Phenotyping of Coffee Flowering Using Deep Learning for Early Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15193019","Early yield estimation is important for improving decision-making and management practices in coffee production systems. However, approaches that use aerial imagery to characterize flowering and relate it to subsequent yield remain limited. This study evaluated the potential of RGB imagery acquired by remotely piloted aircraft (RPA) combined with Deep Learning techniques to classify coffee plants according to three levels of a flowering proxy and to investigate its relationship with yield. A dataset comprising 1593 segmented RGB images was classified into Low, Medium, and High flowering levels using a custom convolutional neural network and three transfer learning architectures: VGG16, ResNet50V2, and MobileNetV2. ResNet50V2 was selected for final evaluation based on its validation performance and achieved 92.12% accuracy, 92.40% macro-precision, 91.81% macro-recall, and 92.06% macro-F1 on the test dataset. The flowering proxy showed a positive association with field-observed yield, with Spearman’s ρ = 0.701 (p \u003C 0.001) and R2 = 0.528 for the combined dataset of Arara and Mundo Novo. In consolidated out-of-sample predictions, the model achieved R2 = 0.489, MAE = 1.03 kg plant−1, and RMSE = 1.18 kg plant−1. Performance differed between cultivars, with consolidated R2 values of 0.567 for Arara and 0.329 for Mundo Novo. Spatial analysis also showed correspondence between the distribution of flowering levels derived from RGB imagery and the subsequent spatial distribution of yield. These findings indicate that RPA-based RGB imagery and Deep Learning can provide spatialized information related to coffee flowering and its productive performance, with potential applications in precision agriculture. Further evaluation across production cycles, cultivars, and environmental conditions is needed to assess the robustness and generalization of the approach.","早期产量估测对于改善咖啡生产系统中的决策与管理实践具有重要意义。然而，利用航空影像表征开花并将其与后续产量相关联的方法仍然有限。本研究评估了由遥控驾驶航空器（RPA）获取的RGB影像结合深度学习技术，根据开花代理指标的三个水平对咖啡植株进行分类，并探讨其与产量之间关系的潜力。利用自定义卷积神经网络和三种迁移学习架构——VGG16、ResNet50V2和MobileNetV2，将包含1593张分割RGB图像的数据集分类为低、中、高开花水平。基于验证性能，选择ResNet50V2进行最终评估，其在测试数据集上达到了92.12%的准确率、92.40%的宏精确率、91.81%的宏召回率和92.06%的宏F1值。开花代理指标与田间观测产量呈正相关，Arara和Mundo Novo合并数据集的Spearman's ρ = 0.701（p \u003C 0.001），R² = 0.528。在合并的样本外预测中，模型达到了R² = 0.489、MAE = 1.03 kg plant⁻¹和RMSE = 1.18 kg plant⁻¹。不同品种间的表现存在差异，Arara和Mundo Novo的合并R²值分别为0.567和0.329。空间分析还表明，由RGB影像得出的开花水平分布与后续产量的空间分布之间存在对应关系。这些发现表明，基于RPA的RGB影像和深度学习能够提供与咖啡开花及其生产性能相关的空间化信息，在精准农业中具有应用潜力。需要在不同生产周期、品种和环境条件下进行进一步评估，以检验该方法的稳健性和泛化能力。","Plants","2026-10-02T00:00:00Z","论文",78,{"impact":17,"substance":89,"depth":18,"authority":90,"freshness":20,"relevant":21,"comment":91},22,13,"该研究将无人机RGB影像与深度学习结合用于咖啡花期分类和早期产量预测，方法新颖、数据扎实、结论可靠，对智慧农业和精准农业有参考价值，但属于细分作物研究，影响范围有限。",[93],{"name":84,"url":81},[95,96,97,98,99],"智慧农业","农业人工智能","产量预测","遥感","咖啡种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[102],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":104},{"candidates":105,"agricultural_evidence":52},[106,111,117,124,131],{"code":32,"score":19,"supported":52,"title":107,"abstract":109,"tags":110},[108],"coffee",[54,108],[99],{"code":39,"score":19,"supported":52,"title":112,"abstract":114,"tags":116},[113],"deep learning",[115,113],"深度学习",[96],{"code":59,"score":118,"supported":52,"title":119,"abstract":120,"tags":123},7,[],[121,122],"精准农业","precision agriculture",[95],{"code":125,"score":76,"supported":52,"title":126,"abstract":127,"tags":130},"I4",[],[128,129],"空间分析","spatial analysis",[],{"code":132,"score":133,"supported":14,"title":134,"abstract":135,"tags":136},"I3",2,[],[],[97,98],[138,139],"无人机 RGB 咖啡 花期 产量预测","ResNet50V2 咖啡 深度学习","无人机RGB咖啡花期产量预测-4081","10.3390\u002Fplants15193019",{"doi":141,"openalex_id":143,"authors":144,"venue":84,"cited_by_count":167,"oa_url":81,"card":168,"direction":172,"ingested_from":174},"W7216244707",[145,147,150,153,155,158,161,164],{"name":146,"orcid":8},"Elias Horácio Zavala",{"name":148,"orcid":149},"Gabriel Araújo e Silva Ferraz","https:\u002F\u002Forcid.org\u002F0000-0001-6403-2210",{"name":151,"orcid":152},"Felipe Andrés Obando Vega","https:\u002F\u002Forcid.org\u002F0000-0001-7241-7065",{"name":154,"orcid":8},"Mirian de Lourdes Oliveira",{"name":156,"orcid":157},"Milene Alves de Figueiredo Carvalho","https:\u002F\u002Forcid.org\u002F0000-0001-6138-8182",{"name":159,"orcid":160},"Samuel Henrique Braga da Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-8783-0378",{"name":162,"orcid":163},"Andrea Pagliai","https:\u002F\u002Forcid.org\u002F0000-0002-0455-7426",{"name":165,"orcid":166},"Daniele Sarri","https:\u002F\u002Forcid.org\u002F0000-0001-6990-7573",0,{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"用无人机RGB影像和深度学习对咖啡开花分级，实现早期产量预测。","1593张分割RGB图，自定义CNN与VGG16\u002FResNet50V2\u002FMobi","ResNet50V2开花分级准确率92.12%，开花等级与产量正相关（ρ=0.701）。","农业遥感与作物表型","可跨生产周期、品种和环境验证模型泛化性，并融合多源遥感提升早期产量预测稳健性。","openalex","2026-10-03T23:30:27.930711Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":8,"source_name":182,"source_url":179,"published_at":183,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":195,"classification":197,"search_phrases":226,"slug":229,"view_count":230,"doi":231,"paper":232,"created_at":265},3165,"Explainable growth stage classification of cacao (Theobroma cacao L.) leaves and key feature visualization using vision transformer and transfer learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1885906","Accurate and objective plant phenotyping is crucial for optimizing agricultural practices, understanding plant development, and enabling rapid responses to environmental changes. Traditional methods, often relying on visual observation, can be subjective, time-consuming, and may overlook subtle but important differences. This study demonstrates the power of combining digital imaging with deep learning to classify plant material with high accuracy, even when visual differences are minimal. We focused on differentiating between stage D (light green) and stage E (dark green) leaves of cacao ( Theobroma cacao L. ), which are visually very similar in size and overall structure. Using cleared and stained leaves of the SCA 6 genotype to highlight the venation network, we trained a Vision Transformer (ViT) model, a deep learning architecture, on image patches. At the patch level, the model achieved an overall accuracy of 97.03% on an independent test set, with a recall of 96.0% for stage D and 98.3% for stage E. At the whole-leaf level, majority voting correctly classified 14 of 15 independent test leaves (93.3%). Attention maps indicated that image regions containing the midrib and primary lateral veins contributed strongly to classification. These attention maps identify discriminative image regions, but they do not by themselves determine the biological mechanism underlying the signal. The major-vein signal may reflect developmental differences in vein-associated structure, stage-associated differences in Safranin O uptake or optical density, tissue thickness, or a combination of these factors. Because vascular anatomy, lignification, hydraulic conductance, phloem loading, and source–sink status were not directly measured, these mechanisms are treated as hypotheses requiring future anatomical, histochemical, and physiological validation. Thus, this study provides a proof-of-concept for interpretable image-based classification of stage D and stage E leaves within greenhouse-grown SCA 6 cacao. Extension to other cacao genotypes, field-grown plants, independent seasons, staining batches, stress detection, species identification, genotype discrimination, or precision-agriculture deployment will require external validation.","准确、客观的植物表型分析对于优化农业实践、理解植物发育以及快速响应环境变化至关重要。传统方法通常依赖视觉观察，可能具有主观性、耗时，并且可能忽略细微但重要的差异。本研究展示了将数字成像与深度学习相结合，即使在视觉差异极小的情况下，也能以高精度对植物材料进行分类。我们聚焦于区分可可（Theobroma cacao L.）的D期（浅绿色）和E期（深绿色）叶片，这些叶片在大小和整体结构上视觉上非常相似。利用SCA 6基因型的透明染色叶片以突出脉序网络，我们在图像块上训练了Vision Transformer（ViT）模型，一种深度学习架构。在图像块水平上，该模型在独立测试集上达到了97.03%的总体准确率，D期的召回率为96.0%，E期为98.3%。在整叶水平上，多数投票正确分类了15片独立测试叶片中的14片（93.3%）。注意力图表明，包含中脉和初级侧脉的图像区域对分类贡献显著。这些注意力图识别了具有判别力的图像区域，但它们本身并不能确定信号背后的生物学机制。主脉信号可能反映了脉相关结构的发育差异、番红O摄取或光密度的阶段相关差异、组织厚度，或这些因素的组合。由于未直接测量维管解剖结构、木质化、水力导度、韧皮部装载和源–库状态，这些机制被视为假设，需要未来的解剖学、组织化学和生理学验证。因此，本研究为温室种植的SCA 6可可中D期和E期叶片的可解释图像分类提供了概念验证。扩展到其他可可基因型、田间种植植株、独立季节、染色批次、胁迫检测、物种鉴定、基因型区分或精准农业部署将需要外部验证。","Frontiers in Plant Science","2026-09-21T00:00:00Z",71,{"impact":60,"substance":186,"depth":187,"authority":90,"freshness":20,"relevant":21,"comment":188},20,17,"方法新颖、数据可靠的可解释作物表型概念验证研究，但属实验室小样本，产业影响有限。",[190],{"name":182,"url":179},[95,96,115,192,193],"可可","作物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[196],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":198},{"candidates":199,"agricultural_evidence":52},[200,205,210,217,222],{"code":32,"score":19,"supported":52,"title":201,"abstract":203,"tags":204},[202],"cacao",[192,202],[192],{"code":39,"score":206,"supported":52,"title":207,"abstract":208,"tags":209},8,[],[115,113],[96,115],{"code":211,"score":76,"supported":52,"title":212,"abstract":213,"tags":216},"B7",[],[214,215],"温室","greenhouse",[],{"code":59,"score":218,"supported":52,"title":219,"abstract":220,"tags":221},4,[],[121],[95],{"code":132,"score":21,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[193],[227,228],"可可 叶片 生长阶段 分类","Vision Transformer 作物表型","可可叶片生长阶段分类-3165",5,"10.3389\u002Ffpls.2026.1885906",{"doi":231,"openalex_id":233,"authors":234,"venue":182,"cited_by_count":167,"oa_url":179,"card":260,"direction":172,"ingested_from":174},"W7213901640",[235,237,240,242,244,246,249,252,255,257],{"name":236,"orcid":8},"Ezekiel Ahn",{"name":238,"orcid":239},"Eun-Sung Park","https:\u002F\u002Forcid.org\u002F0000-0001-6826-2865",{"name":241,"orcid":8},"Moon S. Kim",{"name":243,"orcid":8},"Hangi Kim",{"name":245,"orcid":8},"Lalit M. Kandpal",{"name":247,"orcid":248},"Sunchung Park","https:\u002F\u002Forcid.org\u002F0000-0002-7398-9476",{"name":250,"orcid":251},"Seunghyun Lim","https:\u002F\u002Forcid.org\u002F0000-0003-3023-4863",{"name":253,"orcid":254},"Lyndel W. Meinhardt","https:\u002F\u002Forcid.org\u002F0000-0001-8299-2629",{"name":256,"orcid":8},"Byoung-Kwan Cho",{"name":258,"orcid":259},"Insuck Baek","https:\u002F\u002Forcid.org\u002F0000-0003-1044-349X",{"tldr":261,"method":262,"finding":263,"direction":172,"opportunity":264},"用ViT和迁移学习对可可叶D、E期进行可解释分类，准确率达97%。","透明染色叶片图像块训练ViT，注意力图可视化关键区域。","模型准确区分D\u002FE期，中脉和主侧脉区域贡献最大。","可扩展到多基因型、田间、胁迫检测，并验证脉信号生物学机制。","2026-09-22T23:30:19.972949Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":8,"content":8,"source_name":271,"source_url":8,"published_at":272,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":276,"tags":278,"primary_topic":282,"secondary_topics":283,"classification":289,"search_phrases":308,"slug":311,"view_count":21,"doi":8,"paper":312,"created_at":320},1367,"基于无人机多源遥感特征的茶树生长与氮素状况深度学习监测","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F17\u002F2923","南京农业大学园艺学院团队提出融合光谱、纹理与谐波特征的多域融合指标，结合无人机数据与深度学习模型，用于茶树生物量与氮素积累量估算。实验表明多域融合指数显著优于传统光谱指标，可实现对茶园生物量与氮素状况的高精度反演，为近实时茶园监测提供工具。","Remote Sensing · MDPI · 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in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z",67,{"impact":60,"substance":18,"depth":17,"authority":90,"freshness":206,"relevant":21,"comment":330},"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[332],{"name":326,"url":324},[26,95,334,335],"技术采纳","农户行为",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":339},{"candidates":340,"agricultural_evidence":52},[341,345,350,355],{"code":59,"score":90,"supported":52,"title":342,"abstract":343,"tags":344},[26],[26],[26,95],{"code":39,"score":346,"supported":52,"title":347,"abstract":348,"tags":349},3,[],[67],[],{"code":351,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},"G1",[],[],[334],{"code":356,"score":21,"supported":14,"title":357,"abstract":358,"tags":359},"G2",[],[],[335],[361,362],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529","10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":364,"openalex_id":8,"authors":366,"venue":8,"cited_by_count":167,"oa_url":8,"card":367,"direction":371,"ingested_from":319},[],{"tldr":368,"method":369,"finding":370,"direction":371,"opportunity":372},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","数字乡村与农业信息化","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","2026-10-22T00:07:09.498742Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":8,"source_name":380,"source_url":377,"published_at":381,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":382,"score_detail":383,"sources":385,"tags":387,"primary_topic":392,"secondary_topics":393,"classification":394,"search_phrases":411,"slug":414,"view_count":167,"doi":415,"paper":416,"created_at":431},5479,"Comparative Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23504407","Comparative Scientific Evaluation of Five Coffee Cooperative Organizations in Oromia, Ethiopia: Digital Traceability, Value Conversion, Institutional Dependencies and Five-Year Transformation Strategies Authors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa QajelaPublication year: 2026Publication type: Comparative scientific research article \u002F Preprint Description This scientific article presents a comparative evaluation of five coffee cooperative organizations in Oromia, Ethiopia: Chercher Oda Bultum Farmers Cooperative Union, Homa Multipurpose Farmers Cooperative, Kata Muduga Multipurpose Farmers Cooperative Union, Torban Anfillo Multipurpose Farmers Cooperative Union, and Sadetan Chora Farmers Cooperative Union. The research investigates variability in biological production, institutional organization, processing efficiency, digital traceability, product quality, market participation and financial value realization. The study uses institutional questionnaires, cooperative administrative records and previously developed case assessments, supported by recent international, African and Ethiopian scientific literature. Its integrated analytical framework comprises 18 end-to-end value-chain components, nine value-creation and conversion gates, three institutional levels and the eight-stage Big Win methodology. The comparative results demonstrate significant differences in organizational scale, coffee-growing ecology, farmer registration, geospatial mapping, production measurement, processing infrastructure, quality assurance, certification, export-market participation, financial settlement and digital readiness. The findings also identify common weaknesses in upstream traceability, data consistency, coffee-lot genealogy, processing mass balance, producer-level quality differentiation and verifiable links between commercial transactions and farmer payments. The scientific discussion examines how biological productivity, environmental sustainability, quality conservation, processing efficiency, value addition, buyer acceptance, and realized commercial value depend on reliable institutional coordination and information exchange. The analysis draws on recent research concerning digitalization and sustainability (Hidalgo et al., 2023), Ethiopian coffee traceability and buyer trust (Mbakop et al., 2023), digital traceability capabilities (Tadesse et al., 2024), geographical-origin compliance (Urugo et al., 2025), Ethiopian coffee-quality differentiation (Worku, Astatkie, and Boeckx, 2023), and sustainability performance in southwestern Oromia (Botola, Werke, and Birbirsa, 2026). International institutional frameworks from the International Coffee Organization, UNIDO, FAO, GS1, and ISO inform the proposed approaches to value-chain resilience, circular resource management, digital interoperability, and product traceability. The article proposes an integrated Smart Digital Coffee Value Chain architecture linking market intelligence, genetic resources, nursery development, producer and plot identity, extension services, production, harvesting, aggregation, processing, quality assurance, storage, certification, market contracts, buyer acceptance, financial settlement and feedback. A five-year scientific transformation proposal identifies differentiated improvement priorities for each organization, together with harmonized Business Requirements (BR), Functional Requirements (FR), institutional data dependencies, Key Performance Indicators (KPIs), Key Risk Indicators (KRIs) and corrective and preventive actions. The principal scientific contribution is a comparative framework demonstrating how cooperative organizations with different ecological, institutional, and commercial characteristics can implement a common, interoperable digital value-chain system while following locally appropriate transformation pathways. The study contributes to scientific understanding of cooperative value-chain performance, digital agriculture, institutional coordination, biological and economic efficiency, environmental sustainability and commercial value realization. The findings are based principally on documentary and organizational evidence. Reported performance indicators are distinguished from calculated measures and proposed interventions. Differences in reporting periods, measurement units and data completeness limit direct statistical comparability, while the proposed five-year improvements require implementation and independent evaluation. Keywords Smart Digital Coffee Value Chain; Comparative Scientific Evaluation; Oromia; Ethiopia; Coffee Cooperative Unions; Chercher Oda Bultum; Homa Cooperative; Kata Muduga; Torban Anfillo; Sadetan Chora; Digital Traceability; Value Creation; Value Conservation; Value Addition; Biological Efficiency; Economic Efficiency; Institutional Dependency; Big Win Methodology; Sustainable Coffee Production.","埃塞俄比亚奥罗米亚地区五家咖啡合作社组织的比较科学评估：数字可追溯性、价值转化、制度依赖与五年转型战略\n\n作者：Kedir Nefo Biftu、Kebede Hail、Tadila Tadessa Qajela\n\n出版年份：2026年\n\n出版类型：比较科学研究论文\u002F预印本\n\n描述\n\n本科学论文对埃塞俄比亚奥罗米亚地区五家咖啡合作社组织进行了比较评估：Chercher Oda Bultum农民合作社联盟、Homa多用途农民合作社、Kata Muduga多用途农民合作社联盟、Torban Anfillo多用途农民合作社联盟和Sadetan Chora农民合作社联盟。该研究考察了生物生产、制度组织、加工效率、数字可追溯性、产品质量、市场参与和财务价值实现方面的变异性。研究采用机构问卷、合作社行政记录和先前开发的案例评估，并以近期国际、非洲和埃塞俄比亚科学文献为支撑。其综合分析框架由18个端到端价值链组成部分、九个价值创造与转化关口、三个制度层面和八阶段“大赢”方法组成。比较结果揭示了在组织规模、咖啡种植生态、农民登记、地理空间测绘、生产计量、加工基础设施、质量保证、认证、出口市场参与、财务结算和数字准备度方面存在显著差异。研究结果还识别了上游可追溯性、数据一致性、咖啡批次谱系、加工物料平衡、生产者层面质量分级以及商业交易与农民付款之间可验证联系方面的共同弱点。科学讨论审视了生物生产力、环境可持续性、质量保持、加工效率、价值增值、买方接受度和已实现商业价值如何依赖于可靠的制度协调和信息交换。分析借鉴了近期关于数字化与可持续性（Hidalgo等，2023）、埃塞俄比亚咖啡可追溯性与买方信任（Mbakop等，2023）、数字可追溯能力（Tadesse等，2024）、地理原产地合规（Urugo等，2025）、埃塞俄比亚咖啡质量分级（Worku","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",59,{"impact":206,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":384},"该研究以埃塞俄比亚咖啡合作社为对象，提出数字价值链框架，方法有一定新意，但属区域性案例研究，对国内三农信息化仅有间接参考价值。",[386],{"name":380,"url":377},[26,388,389,390,391],"价值链","合作社","数字溯源","咖啡产业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":395},{"candidates":396,"agricultural_evidence":52},[397,401,406],{"code":32,"score":19,"supported":52,"title":398,"abstract":399,"tags":400},[108],[54,108],[391],{"code":356,"score":218,"supported":52,"title":402,"abstract":403,"tags":405},[],[404],"农民合作社",[389],{"code":59,"score":218,"supported":52,"title":407,"abstract":408,"tags":410},[],[409],"digital agriculture",[26],[412,413],"埃塞俄比亚 咖啡合作社 数字溯源","咖啡产业 数字农业 数字溯源 价值链","埃塞俄比亚咖啡合作社数字溯源-5479","10.5281\u002Fzenodo.23504407",{"doi":415,"openalex_id":417,"authors":418,"venue":380,"cited_by_count":167,"oa_url":377,"card":425,"direction":430,"ingested_from":174},"W7223041235",[419,421,423],{"name":420,"orcid":8},"Kedir Biftu",{"name":422,"orcid":8},"Kebede Hailu",{"name":424,"orcid":8},"Tadila Tadessa Qajela",{"tldr":426,"method":427,"finding":428,"direction":371,"opportunity":429},"比较埃塞俄比亚奥罗米亚五个咖啡合作社的数字化追溯与价值转化差异。","机构问卷、合作社行政记录与案例评估，构建18环节价值链分析框架。","各社在组织规模、数字追溯与支付可验证性差异显著，上游追溯普遍薄弱。","可研究小农户合作社低成本数字追溯与批次谱系方案，打通交易与农户付款的可验证链路。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:09.167175Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":8,"source_name":380,"source_url":435,"published_at":381,"category":86,"cover_url":8,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":447,"secondary_topics":448,"classification":449,"search_phrases":470,"slug":473,"view_count":167,"doi":474,"paper":475,"created_at":486},5477,"Smart Digital Coffee Value Chain Transformation: A Scientific Implementation Manual for Kata Muduga Multipurpose Farmers Cooperatives Union, Oromia, Ethiopia Authors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa Qajela","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23488198","Smart Digital Coffee Value Chain Transformation: A Scientific Implementation Manual for Kata Muduga Multipurpose Farmers Cooperatives Union, Oromia, EthiopiaAuthors: Kedir Nefo Biftu, Kebede Hail, and Tadila Tadessa Qajela Publication year: 2026 Publication type: Scientific technical manual and five-year strategic implementation guide DescriptionThis 30-page scientific implementation manual provides a practical, evidence-based framework for transforming the coffee value chain of Kata Muduga Multipurpose Farmers Cooperatives Union in Jimma Zone, Oromia, Ethiopia. The manual translates institutional assessment findings, scientific knowledge, digital-system requirements, and value-chain management principles into operational procedures suitable for cooperative organizations and other agricultural value-chain institutions. The manual draws on the Union's institutional questionnaire completed on 7 October 2026 and relevant scientific publications on international, African, and Ethiopian coffee production, processing, traceability, sustainability, and cooperative development. Its integrated methodological structure consists of 18 end-to-end coffee value-chain components, nine value-creation and conversion points, three institutional levels, and eight stages of the Big Win delivery methodology. The institutional baseline includes 28,164 members, 61,270 hectares of coffee-growing land, 49,287 productive hectares, an annual production of 315,337 quintals, 62 washing and collection facilities, 5,127 GPS-mapped farms, and reported direct-export experience. The manual evaluates these existing institutional capacities and identifies priority gaps in farmer-level digital identification, planting-material genealogy, production information, product traceability, processing utilization, quality testing, environmental compliance, market contracts, payment verification, and coordinated performance management. It establishes differentiated responsibilities for three institutional levels: Core value-chain actors: Farmers, primary cooperatives, cooperative unions, processors, exporters, and buyers responsible for biological production, collection, processing, commercial transactions, and payment. Supporting institutions: Research centers, agricultural extension services, financial institutions, quality laboratories, logistics providers, and digital technology organizations responsible for specialized technical and operational services. Policy and enabling institutions: The Oromia Cooperative Promotion Commission, agricultural and trade bureaus, regulatory organizations, and regional and national governments responsible for governance, standards, policy coordination, and enabling infrastructure. The manual specifies practical Business Requirements (BR), Functional Requirements (FR), data ownership, information exposing and consuming relationships, interoperable digital identification, transaction records, quality assurance, institutional accountability, and measurable performance indicators. Recent international and African experiences are incorporated to identify transferable best practices in cooperative modernization, agricultural digitalization, specialty-coffee differentiation, sustainable production, traceability, and circular value creation. The proposed five-year implementation roadmap applies the Big Win methodology through Direction, Mini-Lab, Open Day, Roadmap, KPI\u002FKRI Development, Implementation, Verification, and Annual Performance Reporting. Implementation priorities include reliable farmer and farm registration, verified production data, digital collection receipts, coffee-lot genealogy, quality-preserving processing, transparent market contracts, buyer acceptance, producer-payment reconciliation, and feedback-driven corrective actions. The principal contribution of this manual is a structured and adaptable implementation model for converting agricultural production potential into traceable products, conserved quality, added commercial value, accepted market transactions, and realized producer benefits. The manual is designed for practical application by farmers, cooperatives, cooperative unions, researchers, government organizations, development partners, technology developers, exporters, and investors in Ethiopia, Africa, and other coffee-producing regions. It serves as an operational companion to the scientific case study and provides guidance for institutional capacity development, digital-system implementation, monitoring, evaluation, and replication of verified best practices. The five-year roadmap and performance targets represent proposed interventions requiring stakeholder approval, practical implementation, and independent validation. KeywordsSmart Digital Coffee Value Chain; Kata Muduga; Scientific Implementation Manual; Coffee Cooperatives; Jimma; Oromia; Ethiopia; Digital Agriculture; Value Creation; Value Conservation; Value Addition; Biological Efficiency; Economic Efficiency; Business Requirements; Functional Requirements; Institutional Dependency; Big Win Methodology; CAPA; Coffee Sustainability.","智能数字咖啡价值链转型：埃塞俄比亚奥罗米亚州Kata Muduga多用途农民合作社联盟科学实施手册\n\n作者：Kedir Nefo Biftu、Kebede Hail 和 Tadila Tadessa Qajela\n出版年份：2026年\n出版类型：科学技术手册与五年战略实施指南\n\n描述\n\n这本30页的科学实施手册为埃塞俄比亚奥罗米亚州吉马地区的Kata Muduga多用途农民合作社联盟的咖啡价值链转型提供了一个实用的、基于证据的框架。该手册将机构评估结果、科学知识、数字系统要求和价值链管理原则转化为适用于合作社组织及其他农业价值链机构的操作程序。该手册依据该联盟于2026年10月7日完成的机构问卷，以及有关国际、非洲和埃塞俄比亚咖啡生产、加工、可追溯性、可持续性和合作社发展的相关科学出版物。其综合方法结构由18个端到端咖啡价值链组成部分、九个价值创造与转化点、三个机构层级以及“大赢”（Big Win）交付方法的八个阶段组成。机构基线包括28，164名成员、61，270公顷咖啡种植土地、49，287公顷生产性土地、年产量315，337公担、62个水洗和收集设施、5，127个经GPS测绘的农场，以及已报告的直接出口经验。该手册评估了这些现有机构能力，并识别出农民层面数字身份识别、种植材料谱系、生产信息、产品可追溯性、加工利用率、质量检测、环境合规、市场合同、付款验证和协调绩效管理等方面的优先差距。该手册为三个机构层级确立了差异化责任：核心价值链参与者：负责生物生产、收集、加工、商业交易和付款的农民、初级合作社、合作社联盟、加工商、出口商和买家。支持性机构：负责专业技术和运营服务的研究中心、农业推广服务机构、金融机构、质量实验室、物流服务提供商和数字技术组织。政策与赋能机构：奥罗",79,{"impact":18,"substance":89,"depth":18,"authority":60,"freshness":20,"relevant":21,"comment":440},"该手册面向埃塞俄比亚咖啡合作社，提供数字化价值链实施框架，数据详实、方法结构清晰，对农业信息化有参考价值，但属区域性案例，影响范围有限。",[442],{"name":380,"url":435},[26,95,444,445,446],"咖啡产业链","农业合作社","产品溯源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":450},{"candidates":451,"agricultural_evidence":52},[452,456,462,466],{"code":32,"score":51,"supported":52,"title":453,"abstract":454,"tags":455},[108],[54,108],[],{"code":351,"score":76,"supported":52,"title":457,"abstract":458,"tags":461},[],[459,460],"农业推广","agricultural extension",[],{"code":59,"score":230,"supported":52,"title":463,"abstract":464,"tags":465},[],[409],[26,95],{"code":356,"score":346,"supported":52,"title":467,"abstract":468,"tags":469},[],[404],[],[471,472],"Kata Muduga 咖啡 合作社","埃塞俄比亚 咖啡 数字价值链","KataMuduga咖啡合作社-5477","10.5281\u002Fzenodo.23488198",{"doi":474,"openalex_id":476,"authors":477,"venue":380,"cited_by_count":167,"oa_url":435,"card":481,"direction":430,"ingested_from":174},"W7223028195",[478,479,480],{"name":420,"orcid":8},{"name":422,"orcid":8},{"name":424,"orcid":8},{"tldr":482,"method":483,"finding":484,"direction":371,"opportunity":485},"为埃塞俄比亚咖啡合作社联盟制定数字化价值链转型的科学实施手册。","基于机构问卷与文献，构建18个价值链环节、三级机构与八阶段交付方法。","识别出农户数字身份、溯源、质检与支付核验等优先数字化缺口。","可研究小农户合作社场景下低成本溯源与数字身份落地的效果评估。","2026-10-21T23:30:08.879737Z"]