[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2879":3,"related-2879":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":8,"created_at":66},2879,"中国咖啡的品质根基与创新驱动论坛在昆明举办:云南省农科院聚焦全产业链标准体系","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260918A07YT800","9月17日,2026中国热带作物产业大会分论坛在昆明举行,来自中国标准化研究院、云南省农业科学院等单位的80余名代表参会。论坛以标准筑基科技赋能为主线,中国农业大学副教授李想作AI助力种好咖啡报告,介绍AI农技大脑一田一策精准决策、AI撮合平台、田间移动服务站、智慧农场ERP等农事服务新模式,提出AI-农业服务人员-农民服务路径。",null,"中国新闻网\u002F腾讯新闻","2026-09-18T00:00:00Z","报道",10,false,68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,16,14,11,9,1,"省级科研机构主导的咖啡全产业链标准与AI农技服务论坛，行业细分进展有实质信息，但影响层级限于产业与省级范围。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","咖啡产业","热带作物","农业标准体系",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},12,true,[48],"咖啡",[48],[28],{"code":52,"score":21,"supported":13,"title":53,"abstract":54,"tags":55},"I1",[],[],[26],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[27],[62,63],"云南省农科院 咖啡 标准体系","AI农技大脑 咖啡","云南省农科院咖啡标准体系-2879",4,"2026-09-19T00:06:04.718237Z",{"total":68,"page":21,"page_size":68,"items":69},6,[70,171,218,306,377,436],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":8,"source_name":76,"source_url":73,"published_at":77,"category":78,"cover_url":8,"hotness":12,"is_selected":13,"score":79,"score_detail":80,"sources":84,"tags":86,"primary_topic":90,"secondary_topics":91,"classification":97,"search_phrases":132,"slug":135,"view_count":21,"doi":136,"paper":137,"created_at":170},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":81,"depth":16,"authority":82,"freshness":20,"relevant":21,"comment":83},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。",[85],{"name":76,"url":73},[26,27,87,88,89],"产量预测","遥感","咖啡种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[92],{"code":57,"slug":93,"name":94,"path":95,"parent_code":96},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":98},{"candidates":99,"agricultural_evidence":46},[100,106,112,119,126],{"code":32,"score":101,"supported":46,"title":102,"abstract":104,"tags":105},15,[103],"coffee",[48,103],[89],{"code":57,"score":101,"supported":46,"title":107,"abstract":109,"tags":111},[108],"deep learning",[110,108],"深度学习",[27],{"code":52,"score":113,"supported":46,"title":114,"abstract":115,"tags":118},7,[],[116,117],"精准农业","precision agriculture",[26],{"code":120,"score":68,"supported":46,"title":121,"abstract":122,"tags":125},"I4",[],[123,124],"空间分析","spatial analysis",[],{"code":127,"score":128,"supported":13,"title":129,"abstract":130,"tags":131},"I3",2,[],[],[87,88],[133,134],"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":136,"openalex_id":138,"authors":139,"venue":76,"cited_by_count":162,"oa_url":73,"card":163,"direction":167,"ingested_from":169},"W7216244707",[140,142,145,148,150,153,156,159],{"name":141,"orcid":8},"Elias Horácio Zavala",{"name":143,"orcid":144},"Gabriel Araújo e Silva Ferraz","https:\u002F\u002Forcid.org\u002F0000-0001-6403-2210",{"name":146,"orcid":147},"Felipe Andrés Obando Vega","https:\u002F\u002Forcid.org\u002F0000-0001-7241-7065",{"name":149,"orcid":8},"Mirian de Lourdes Oliveira",{"name":151,"orcid":152},"Milene Alves de Figueiredo Carvalho","https:\u002F\u002Forcid.org\u002F0000-0001-6138-8182",{"name":154,"orcid":155},"Samuel Henrique Braga da Cunha","https:\u002F\u002Forcid.org\u002F0000-0002-8783-0378",{"name":157,"orcid":158},"Andrea Pagliai","https:\u002F\u002Forcid.org\u002F0000-0002-0455-7426",{"name":160,"orcid":161},"Daniele Sarri","https:\u002F\u002Forcid.org\u002F0000-0001-6990-7573",0,{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"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":172,"title":173,"url":174,"summary":175,"summary_zh":8,"content":176,"source_name":177,"source_url":8,"published_at":178,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":179,"score_detail":180,"sources":185,"tags":187,"primary_topic":192,"secondary_topics":193,"classification":194,"search_phrases":213,"slug":216,"view_count":21,"doi":8,"paper":8,"created_at":217},3232,"2026年中国热带作物产业大会在昆明举办——云南农科院发布咖啡重大科技成果 云南咖啡种植面积占全国98%以上","https:\u002F\u002Fwww.jingjiribao.cn\u002Fstatic\u002Fdetail.jsp?id=684363","9-17 经济日报报道：2026年中国热带作物产业大会9-17在云南昆明举行。本次大会聚焦咖啡这一热带作物，以\"咖位中国·链动全球\"为主题。云南省农业科学院发布咖啡重大科技成果，包括咖啡园\"乔—灌—草\"立体复合种植技术、咖啡化肥农药减施增效技术、咖啡鲜果无水加工技术、咖啡定向发酵关键技术等，覆盖种植、植保与加工环节。云南省市场监管局宣布成立云南省咖啡技术标准化委员会并揭牌。\"十四五\"期间，云南咖啡豆精品率从8%跃升至41.7%，精深加工率从20%提升到85%。2025年云南咖啡种植面积达151.88万亩、产量达17.07万吨，均占全国98%以上，咖啡全产业链产值达995亿元。","2026年中国热带作物产业大会9月17日在云南昆明举行。本次大会聚焦咖啡这一热带作物，以“咖位中国·链动全球”为主题，200余名专家学者、企业家、业界代表等围绕咖啡提质、品牌建设与国际合作展开研讨，推动我国咖啡向标准化、精品化、品牌化方向发展。\n\n![Image 1](https:\u002F\u002Fstatic.jingjiribao.cn\u002F2026\u002F9\u002F20\u002F20260920103055_7096.JPEG)\n\n开幕式上，云南省市场监管局宣布成立云南省咖啡技术标准化委员会并揭牌。云南省农业科学院发布咖啡重大科技成果，包括咖啡园“乔—灌—草”立体复合种植技术、咖啡化肥农药减施增效技术、咖啡鲜果无水加工技术、咖啡定向发酵关键技术等，覆盖种植、植保与加工环节。云南日报报业集团发布《中式咖啡文化传播影响力报告》。\n\n据介绍，云南热区面积约8.1万平方公里，是全国热区资源富集省份之一。近年来，云南立足多样立体的气候资源禀赋，大力发展热作产业，咖啡成为最具辨识度、最有竞争力的热作产业之一。“十四五”期间，云南咖啡豆精品率从8%跃升至41.7%，精深加工率从20%提升到85%。2025年，云南咖啡种植面积达151.88万亩、产量达17.07万吨，均占全国98%以上，咖啡全产业链产值达995亿元。\n\n本次大会设“变局与新机——全球咖啡格局重塑中的中国路径与中非、GMS（大湄公河次区域）机遇”“标准筑基·科技赋能——中国咖啡的品质根基与创新驱动”“从产地到品牌——云南咖啡的精品化跃迁”“世界咖啡·中国表达——中国咖啡文化国际传播”4个平行分论坛。\n\n本次大会由农业农村部南亚热带作物中心、云南省农业农村厅、昆明市人民政府共同主办，大会期间，云南还举办咖啡加工工职业技能大赛、花咖之夜、2026云南咖啡展、滇池国际咖啡文化嘉年华·“秋韵啡扬”、滇池国际咖啡文化嘉年华·翠湖咖啡主题市集等活动，让参会嘉宾、游客与本地消费者体验云南丰富多彩的咖啡文化。","经济日报","2026-09-17T00:00:00Z",87,{"impact":181,"substance":182,"depth":183,"authority":18,"freshness":68,"relevant":21,"comment":184},27,23,17,"全国性热带作物产业大会落地昆明，云南农科院发布覆盖种植到加工的重大科技成果，并给出精品率、精深加工率与全产业链产值等关键数据，产业级信息增量充足，值得进入每日精选。",[186],{"name":177,"url":174},[188,189,28,190,29,191],"品牌建设","标准化","精深加工","农业科技成果",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":195},{"candidates":196,"agricultural_evidence":46},[197,201,208],{"code":32,"score":45,"supported":46,"title":198,"abstract":199,"tags":200},[48],[48],[28],{"code":202,"score":68,"supported":46,"title":203,"abstract":204,"tags":207},"F4",[],[205,206],"农药","植保",[],{"code":209,"score":21,"supported":13,"title":210,"abstract":211,"tags":212},"E5",[],[],[188],[214,215],"云南农科院 咖啡 科技成果","云南咖啡 种植面积 全产业链产值","云南农科院咖啡科技成果-3232","2026-09-23T00:04:30.734823Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":8,"source_name":224,"source_url":221,"published_at":225,"category":78,"cover_url":8,"hotness":12,"is_selected":13,"score":226,"score_detail":227,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":266,"slug":269,"view_count":270,"doi":271,"paper":272,"created_at":305},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":45,"substance":228,"depth":183,"authority":82,"freshness":20,"relevant":21,"comment":229},20,"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。",[231],{"name":224,"url":221},[26,27,110,233,234],"可可","作物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[237],{"code":57,"slug":93,"name":94,"path":95,"parent_code":96},{"status":39,"locked":13,"version":40,"method":41,"evidence":239},{"candidates":240,"agricultural_evidence":46},[241,246,251,258,262],{"code":32,"score":101,"supported":46,"title":242,"abstract":244,"tags":245},[243],"cacao",[233,243],[233],{"code":57,"score":247,"supported":46,"title":248,"abstract":249,"tags":250},8,[],[110,108],[27,110],{"code":252,"score":68,"supported":46,"title":253,"abstract":254,"tags":257},"B7",[],[255,256],"温室","greenhouse",[],{"code":52,"score":65,"supported":46,"title":259,"abstract":260,"tags":261},[],[116],[26],{"code":127,"score":21,"supported":13,"title":263,"abstract":264,"tags":265},[],[],[234],[267,268],"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":271,"openalex_id":273,"authors":274,"venue":224,"cited_by_count":162,"oa_url":221,"card":300,"direction":167,"ingested_from":169},"W7213901640",[275,277,280,282,284,286,289,292,295,297],{"name":276,"orcid":8},"Ezekiel Ahn",{"name":278,"orcid":279},"Eun-Sung Park","https:\u002F\u002Forcid.org\u002F0000-0001-6826-2865",{"name":281,"orcid":8},"Moon S. Kim",{"name":283,"orcid":8},"Hangi Kim",{"name":285,"orcid":8},"Lalit M. Kandpal",{"name":287,"orcid":288},"Sunchung Park","https:\u002F\u002Forcid.org\u002F0000-0002-7398-9476",{"name":290,"orcid":291},"Seunghyun Lim","https:\u002F\u002Forcid.org\u002F0000-0003-3023-4863",{"name":293,"orcid":294},"Lyndel W. Meinhardt","https:\u002F\u002Forcid.org\u002F0000-0001-8299-2629",{"name":296,"orcid":8},"Byoung-Kwan Cho",{"name":298,"orcid":299},"Insuck Baek","https:\u002F\u002Forcid.org\u002F0000-0003-1044-349X",{"tldr":301,"method":302,"finding":303,"direction":167,"opportunity":304},"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":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":312,"source_url":309,"published_at":313,"category":78,"cover_url":8,"hotness":12,"is_selected":13,"score":314,"score_detail":315,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":340,"slug":343,"view_count":344,"doi":345,"paper":346,"created_at":376},3007,"Estimation of coffee leaf physiological indicators based on VAG-ViT: A lightweight framework fusing multi-view RGB images and dual-task learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112452","Estimation of coffee leaf physiological indicators based on VAG-ViT: A lightweight framework fusing multi-view RGB images and dual-task learning。Computers and Electronics in Agriculture","基于VAG-ViT的咖啡叶片生理指标估测：一种融合多视角RGB图像与双任务学习的轻量级框架。","Computers and Electronics in Agriculture","2026-09-19T00:00:00Z",77,{"impact":17,"substance":316,"depth":183,"authority":18,"freshness":20,"relevant":21,"comment":317},21,"提出融合多视图RGB与双任务学习的轻量化ViT框架，用于咖啡叶片生理指标估算，方法新颖且面向经济作物精准管理，具备行业参考价值。",[319],{"name":312,"url":309},[26,27,321,89,322],"轻量化模型","多视图图像",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":326},{"candidates":327,"agricultural_evidence":46},[328,332,336],{"code":32,"score":101,"supported":46,"title":329,"abstract":330,"tags":331},[103],[48,103],[89],{"code":57,"score":128,"supported":13,"title":333,"abstract":334,"tags":335},[],[],[27,321],{"code":52,"score":21,"supported":13,"title":337,"abstract":338,"tags":339},[],[],[26],[341,342],"咖啡叶片 生理指标 多视图RGB","VAG-ViT 双任务学习","咖啡叶片生理指标多视图RGB-3007",3,"10.1016\u002Fj.compag.2026.112452",{"doi":345,"openalex_id":347,"authors":348,"venue":312,"cited_by_count":162,"oa_url":309,"card":371,"direction":167,"ingested_from":169},"W7213674051",[349,351,353,356,358,360,362,364,366,368],{"name":350,"orcid":8},"Zongyuan Lv",{"name":352,"orcid":8},"Jianping Yang",{"name":354,"orcid":355},"Li Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7006-4443",{"name":357,"orcid":8},"Rongbiao Ji",{"name":359,"orcid":8},"Yadong Li",{"name":361,"orcid":8},"Mengyao Wu",{"name":363,"orcid":8},"Songling Huang",{"name":365,"orcid":8},"Jingyu Yuan",{"name":367,"orcid":8},"Zihao Jiang",{"name":369,"orcid":370},"Tong Li","https:\u002F\u002Forcid.org\u002F0000-0002-3257-213X",{"tldr":372,"method":373,"finding":374,"direction":167,"opportunity":375},"提出VAG-ViT轻量框架，融合多视角RGB图像与双任务学习，估算咖啡叶片生理指标。","多视角RGB图像融合、双任务学习、轻量级ViT框架。","VAG-ViT能有效估算咖啡叶片生理指标，兼顾精度与轻量化。","可探索多视角RGB与双任务学习在更多作物生理指标估算中的泛化能力及田间部署。","2026-09-20T23:30:01.826242Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":8,"source_name":383,"source_url":380,"published_at":384,"category":78,"cover_url":8,"hotness":12,"is_selected":13,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":394,"secondary_topics":395,"classification":396,"search_phrases":413,"slug":416,"view_count":21,"doi":417,"paper":418,"created_at":435},2651,"Development and Web-Based Visualization of a Domain-Specific Semantic Model for Coffee Culture and Production Processes","https:\u002F\u002Fdoi.org\u002F10.34248\u002Fbsengineering.1991498","The global agricultural supply chain is a complex network with different data silos, complicated cultivation parameters and highly subjective sensory evaluation metrics. Formal knowledge representation is a must, if real semantic interoperability is to be achieved across this multidimensional domain. This work consists of the rigorous design, implementation and quantitative evaluation of the “Coffee Culture and Production Processes Ontology” and a custom client-side web-application named the “Coffee Ontology Explorer”. The semantic model, developed with standard ontology design principles and the Web Ontology Language (OWL), captures the entire coffee lifecycle from botanical taxonomy and harvesting methods to specific roasting profiles and subjective brewing evaluations. Meanwhile, the React.js based ontology explorer tackles the ongoing limitations of traditional, heavy-client ontology visualization tools by utilizing a browser-native document parsing application programming interface for high efficiency, serverless ontology parsing. The quantitative estimation of the developed semantic model gives a 0.297 relation richness indicator, which confirms a highly interconnected and structurally complex knowledge graph, and not a simple taxonomic flat. Moreover, the performance profiling of the web application shows less than one second for parsing and less than 200 milliseconds for rendering complex graphical data even for large ontological hierarchies. This technical framework is a highly scalable, multilingual semantic infrastructure that is uniquely suited for integration into larger industrial traceability systems and provides a fundamental template for computer scientists, agritech developers, and agricultural stakeholders who want to exploit semantic web technologies for precision agriculture.","全球农业供应链是一个复杂的网络，包含不同的数据孤岛、复杂的种植参数以及高度主观的感官评价指标。若要实现这一多维领域真正的语义互操作，形式化知识表示必不可少。本研究包括“咖啡文化与生产过程本体”（Coffee Culture and Production Processes Ontology）的严格设计、实现与定量评估，以及一个名为“咖啡本体浏览器”（Coffee Ontology Explorer）的定制客户端网页应用。该语义模型依据标准本体设计原则并使用Web本体语言（OWL）开发，涵盖从植物分类学和采收方法到特定烘焙曲线及主观冲泡评价的整个咖啡生命周期。与此同时，基于React.js的本体浏览器利用浏览器原生文档解析应用程序编程接口实现高效的无服务器本体解析，从而解决了传统重型客户端本体可视化工具的持续局限。对所开发语义模型的定量评估给出了0.297的关系丰富度指标，这证实了一个高度互联且结构复杂的知识图谱，而非简单的分类扁平结构。此外，该网页应用的性能分析表明，即使面对大型本体层级结构，解析时间也不到一秒，复杂图形数据的渲染时间不到200毫秒。该技术框架是一个高度可扩展的多语言语义基础设施，特别适合集成到更大的工业追溯系统中，并为希望利用语义网技术实现精准农业的计算机科学家、农业科技开发者和农业利益相关者提供了一个基础模板。","Black Sea Journal of Engineering and Science","2026-09-14T00:00:00Z",69,{"impact":45,"substance":228,"depth":183,"authority":45,"freshness":247,"relevant":21,"comment":387},"咖啡全生命周期本体与浏览器端可视化工具，方法新颖、性能数据扎实，为农业语义互操作与溯源提供可复用模板，但属细分作物领域，产业影响有限。",[389],{"name":383,"url":380},[26,391,28,392,393],"本体建模","农业知识图谱","语义互操作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":46},[399,403,407],{"code":32,"score":101,"supported":46,"title":400,"abstract":401,"tags":402},[103],[48,103],[28],{"code":52,"score":113,"supported":46,"title":404,"abstract":405,"tags":406},[],[116,117],[26],{"code":408,"score":344,"supported":46,"title":409,"abstract":410,"tags":412},"J3",[],[411],"全球农业",[],[414,415],"农业知识图谱 语义互操作 咖啡产业 智慧农业","农业知识图谱 语义互操作","农业知识图谱语义互操作咖啡产业智慧农业-2651","10.34248\u002Fbsengineering.1991498",{"doi":417,"openalex_id":419,"authors":420,"venue":383,"cited_by_count":162,"oa_url":427,"card":428,"direction":434,"ingested_from":169},"W7213004624",[421,424],{"name":422,"orcid":423},"Ömer Doğan","https:\u002F\u002Forcid.org\u002F0000-0003-4815-8619",{"name":425,"orcid":426},"Alpay Doruk","https:\u002F\u002Forcid.org\u002F0000-0002-6190-288X","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6208007",{"tldr":429,"method":430,"finding":431,"direction":432,"opportunity":433},"构建咖啡文化与生产过程的OWL本体，并开发浏览器端可视化工具。","用OWL本体建模咖啡全生命周期，React.js实现无服务器本体解析与可视化。","本体关系丰富度0.297，解析\u003C1秒、渲染\u003C200毫秒，可扩展至溯源系统。","数字乡村与农业信息化","可借鉴该语义本体框架，为其他农产品构建轻量级浏览器端知识图谱与溯源集成方案。","智慧农业 \u002F 农业物联网","2026-09-16T23:30:15.075138Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":8,"source_name":442,"source_url":439,"published_at":443,"category":78,"cover_url":8,"hotness":12,"is_selected":13,"score":314,"score_detail":444,"sources":446,"tags":448,"primary_topic":452,"secondary_topics":453,"classification":460,"search_phrases":502,"slug":505,"view_count":128,"doi":506,"paper":507,"created_at":522},2308,"Coffee phytobiome dynamics: integrating multitrophic interactions and plant physiology for climate-resilient production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmicb.2026.1951767","Coffee ( Coffea arabica L. and Coffea canephora Pierre ex A. Froehner) is a globally important perennial plantation crop that sustains the livelihoods of millions of smallholder farmers while making substantial contributions to agricultural economies worldwide global agricultural economies. Nevertheless, sustainable coffee production is increasingly constrained by climate change, declining soil fertility, emerging pests and diseases and the environmental costs associated with intensive use of synthetic agrochemicals. Recent advances in plant microbiome research have transformed the understanding of coffee from an individual organism to a holobiont, where the host plant and its associated microorganisms operate as an integrated biological system. The coffee phytobiome encompasses diverse microbial communities inhabiting the rhizosphere, rhizoplane, endosphere, phyllosphere, anthosphere, carposphere and spermosphere, together with complex multitrophic interactions involving shade trees, soil fauna, insects and the surrounding environment. These interactions collectively regulate nutrient acquisition, carbon assimilation, water-use efficiency, hormonal balance, stress tolerance, immune responses and overall plant productivity. This review critically examines current advances in multitrophic plant–microbe interactions that influence physiological adaptation in coffee, with particular emphasis on sustainable production under Indian agroecological conditions. It highlights the functional functions of plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF), endophytic microorganisms and other beneficial microbes in enhancing root development, nutrient cycling, and resilience to biotic and abiotic stresses. Furthermore, the review evaluates recent progress in metagenomics, metatranscriptomics, metabolomics and microbiome engineering for harnessing indigenous microbial resources. Finally, it outlines future research priorities integrating plant physiology, microbial ecology, systems biology, and precision agriculture to develop climate-resilient, resource-efficient, and environmentally sustainable coffee production systems.","咖啡（Coffea arabica L. 和 Coffea canephora Pierre ex A. Froehner）是全球重要的多年生种植作物，维系着数百万小农户的生计，同时为全球农业经济作出重大贡献。然而，可持续咖啡生产日益受到气候变化、土壤肥力下降、新发病虫害以及集约化使用合成农用化学品所带来的环境成本的制约。近年来植物微生物组研究的进展已将人们对咖啡的认知从单一生物体转变为全息生物（holobiont），即宿主植物与其相关微生物作为一个整合的生物系统协同运作。咖啡植物微生物组（phytobiome）涵盖栖息于根际、根表、内生圈、叶际、花际、果际和种际的多样化微生物群落，以及涉及遮荫树、土壤动物、昆虫和周围环境的复杂多营养级相互作用。这些相互作用共同调控养分获取、碳同化、水分利用效率、激素平衡、胁迫耐受性、免疫应答和整体植物生产力。本文综述批判性地审视了影响咖啡生理适应的多营养级植物–微生物相互作用的最新进展，特别关注印度农业生态条件下的可持续生产。文章重点阐述了植物促生根际细菌（PGPR）和丛枝菌根真菌（AMF）、内生微生物及其他有益微生物在促进根系发育、养分循环以及增强生物和非生物胁迫韧性方面的功能。此外，本文评估了宏基因组学、宏转录组学、代谢组学和微生物组工程在利用本土微生物资源方面的最新进展。最后，文章展望了未来研究重点，即整合植物生理学、微生物生态学、系统生物学和精准农业，以开发气候韧性、资源高效和环境可持续的咖啡生产系统。","Frontiers in Microbiology","2026-09-10T00:00:00Z",{"impact":16,"substance":228,"depth":183,"authority":18,"freshness":247,"relevant":21,"comment":445},"系统综述咖啡植物微生物组与多营养级互作，为气候韧性生产提供微生物组工程与组学新思路，专业性强但属细分作物领域，产业影响中等。",[447],{"name":442,"url":439},[26,449,450,28,451],"气候韧性","土壤健康","植物微生物组",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[454],{"code":455,"slug":456,"name":457,"path":458,"parent_code":459},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":461},{"candidates":462,"agricultural_evidence":46},[463,467,474,481,485,492,498],{"code":32,"score":101,"supported":46,"title":464,"abstract":465,"tags":466},[103],[48,103],[28],{"code":455,"score":12,"supported":46,"title":468,"abstract":469,"tags":473},[],[470,471,456,472],"土壤","根际","rhizosphere",[450],{"code":475,"score":113,"supported":46,"title":476,"abstract":477,"tags":480},"H7",[],[478,479],"气候变化","climate change",[449],{"code":52,"score":113,"supported":46,"title":482,"abstract":483,"tags":484},[],[116,117],[26],{"code":486,"score":68,"supported":46,"title":487,"abstract":488,"tags":491},"G2",[],[489,490],"小农户","smallholder",[],{"code":493,"score":344,"supported":46,"title":494,"abstract":495,"tags":497},"H6",[],[496],"农业生态",[],{"code":408,"score":344,"supported":46,"title":499,"abstract":500,"tags":501},[],[411],[],[503,504],"植物微生物组 咖啡产业 土壤健康 智慧农业","植物微生物组 咖啡产业","植物微生物组咖啡产业土壤健康智慧农业-2308","10.3389\u002Ffmicb.2026.1951767",{"doi":506,"openalex_id":508,"authors":509,"venue":442,"cited_by_count":162,"oa_url":515,"card":516,"direction":434,"ingested_from":169},"W7212145113",[510,513],{"name":511,"orcid":512},"Somashekhargouda Patil","https:\u002F\u002Forcid.org\u002F0000-0002-0362-0745",{"name":514,"orcid":8},"C. S. Rudragouda","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmicrobiology\u002Farticles\u002F10.3389\u002Ffmicb.2026.1951767\u002Fpdf",{"tldr":517,"method":518,"finding":519,"direction":520,"opportunity":521},"综述咖啡植物微生物组与多营养级互作，提出整合植物生理与微生物组提升气候韧性生产。","综述微生物组学、多组学与微生物组工程，聚焦印度咖啡农生态。","咖啡作为全生物体，其根际、内生等微生物组通过多营养级互作调控养分、抗逆与生产力。","农业绿色发展与碳","可探索咖啡全生物体多组学数据整合与微生物组工程，构建气候韧性精准调控模型。","2026-09-13T23:30:11.231069Z"]