[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3263":3,"related-3263":114},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":49,"search_phrases":84,"slug":87,"view_count":88,"doi":89,"paper":90,"created_at":113},3263,"Identifying drought-responsive candidate genes in root and shoot tissues of wheat (Triticum aestivum L.) through RNA sequencing, cluster analysis, weighted gene co-expression network analysis, and machine learning models","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952456","Drought stress severely affects the growth, development, and yield formation of wheat. However, the differentiated regulatory mechanisms of wheat roots and shoots during drought adaptation remain to be further analyzed. Therefore, this study used RNA-seq datasets of roots and shoots from multiple wheat varieties, combined with differential expression analysis, Gene Ontology (GO) enrichment analysis, clustering analysis, weighted gene co-expression network analysis (WGCNA), and machine learning were used to characterize drought-responsive transcriptional changes. Differential expression analysis identified 90 and 3,991 differentially expressed genes (DEGs) in roots under DS1 and DS2 respectively, compared with 34 and 1,940 DEGs in shoots. Functional enrichment analysis indicated that root DEGs were mainly involved in root growth, cell wall remodeling, water transport, and osmotic adjustment processes, whereas shoot DEGs are mainly enriched in processes related to stomatal regulation, photosynthesis, lipid metabolism, and oxidative stress responses. WGCNA identified 18 and 19 co-expression modules in roots and shoots, respectively, among which the yellow module was significantly correlated with the response to drought stress in root and shoot tissues. To further screen drought-responsive candidate genes from these modules, we compared multiple machine learning models. The XGBoost model achieved the highest and AUC in both roots and shoots. Combined with SHAP feature contribution analysis, 30 drought-responsive candidate genes with high discriminatory power were identified separately in roots and shoots. Analysis of cis -acting elements in candidate gene promoters revealed abundant drought- and stress-responsive elements, including MYB, G-box, ABRE, MYC, MBS, and LTR. MYB (183 in roots and 164 in shoots), G-box (168 and 98), and ABRE (148 and 86) were particularly abundant, and several candidate genes contained more than five ABRE copies in their promoters, suggesting potential involvement in ABA-mediated drought responses. This study reveals the different transcriptional response processes of wheat roots and shoots under drought stress, and identifies key co-expression modules and core candidate genes through WGCNA combined with XGBoost and SHAP analysis. These findings provide a basis for understanding organ-specific molecular responses to drought stress and offer potential targets for improving drought tolerance in wheat.","干旱胁迫严重影响小麦的生长发育和产量形成。然而，小麦根系和地上部在干旱适应过程中的差异化调控机制仍有待深入解析。因此，本研究利用多个小麦品种根系和地上部的RNA-seq数据集，结合差异表达分析、基因本体（GO）富集分析、聚类分析、加权基因共表达网络分析（WGCNA）和机器学习方法，表征干旱响应转录变化。差异表达分析在DS1和DS2条件下分别鉴定出根系中90个和3,991个差异表达基因（DEGs），而地上部中分别为34个和1,940个DEGs。功能富集分析表明，根系DEGs主要参与根系生长、细胞壁重塑、水分运输和渗透调节过程，而地上部DEGs主要富集于气孔调控、光合作用、脂质代谢和氧化胁迫响应相关过程。WGCNA在根系和地上部中分别鉴定出18个和19个共表达模块，其中黄色模块与根系和地上部组织对干旱胁迫的响应显著相关。为进一步从这些模块中筛选干旱响应候选基因，我们比较了多种机器学习模型。XGBoost模型在根系和地上部中均取得了最高的AUC。结合SHAP特征贡献分析，分别在根系和地上部中鉴定出30个具有高判别力的干旱响应候选基因。候选基因启动子中的顺式作用元件分析揭示了丰富的干旱和胁迫响应元件，包括MYB、G-box、ABRE、MYC、MBS和LTR。其中MYB（根系183个，地上部164个）、G-box（168个和98个）和ABRE（148个和86个）尤为丰富，且多个候选基因的启动子中含有超过5个ABRE拷贝，表明其可能参与ABA介导的干旱响应。本研究揭示了小麦根系和地上部在干旱胁迫下不同的转录响应过程，并通过WGCNA结合XGBoost和SHAP分析鉴定了关键共表达模块和核心候选基因。这些发现为理解干旱胁迫的器官特异性分子响应提供了基础，并为提高小麦耐旱性提供了潜在靶点。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,22,13,1,"该研究结合多组学与机器学习挖掘小麦抗旱候选基因，方法新颖、数据扎实，对分子育种有参考价值，值得进入每日精选。",[23],{"name":10,"url":6},[25,26,27,28,29],"农业人工智能","种业振兴","基因挖掘","作物育种","小麦抗旱",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[37,43],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,63,68,74,79],{"code":44,"score":56,"supported":57,"title":58,"abstract":60,"tags":62},15,true,[59],"machine learning",[61,59],"机器学习",[25],{"code":31,"score":64,"supported":57,"title":65,"abstract":66,"tags":67},14,[32],[33,32],[],{"code":38,"score":64,"supported":57,"title":69,"abstract":71,"tags":73},[70],"drought",[72,70],"干旱",[],{"code":75,"score":20,"supported":14,"title":76,"abstract":77,"tags":78},"F1",[],[],[26],{"code":80,"score":20,"supported":14,"title":81,"abstract":82,"tags":83},"F2",[],[],[27],[85,86],"小麦 抗旱基因 RNA测序","小麦 根系 地上部 干旱响应","小麦抗旱基因RNA测序-3263",4,"10.3389\u002Ffsufs.2026.1952456",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":105,"oa_url":6,"card":106,"direction":110,"ingested_from":112},"W7214062209",[93,95,98,100,102],{"name":94,"orcid":9},"Tianle Ji",{"name":96,"orcid":97},"Baoyue Cui","https:\u002F\u002Forcid.org\u002F0009-0000-5881-9682",{"name":99,"orcid":9},"Jinjie Yin",{"name":101,"orcid":9},"Xianyong Meng",{"name":103,"orcid":104},"Jun Yan","https:\u002F\u002Forcid.org\u002F0000-0001-6883-8529",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"通过RNA-seq、WGCNA和XGBoost-SHAP分析，鉴定小麦根和茎中响应干旱的候选基因。","RNA-seq、差异表达、WGCNA、XGBoost与SHAP特征分析。","根和茎干旱响应机制不同，各鉴定出30个候选基因，启动子富含MYB、G-box和ABRE元件。","农业人工智能与决策模型","可结合多组学与深度学习构建器官特异性干旱调控网络，并开展候选基因功能验证。","openalex","2026-09-23T23:30:07.062126Z",{"total":115,"page":20,"page_size":115,"items":116},6,[117,185,271,340,422,504],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":132,"tags":134,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":161,"slug":164,"view_count":105,"doi":165,"paper":166,"created_at":184},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":127,"substance":128,"depth":129,"authority":127,"freshness":130,"relevant":20,"comment":131},12,21,17,9,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[133],{"name":123,"url":120},[25,135,136,137,138],"可解释AI","极端天气","遥感","干旱监测",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[141],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":143},{"candidates":144,"agricultural_evidence":57},[145,150,154],{"code":44,"score":146,"supported":57,"title":147,"abstract":148,"tags":149},16,[59],[61,59],[25,135],{"code":38,"score":56,"supported":57,"title":151,"abstract":152,"tags":153},[70],[72,70],[138],{"code":155,"score":156,"supported":57,"title":157,"abstract":158,"tags":160},"I3",7,[],[137,159],"remote sensing",[137],[162,163],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":165,"openalex_id":167,"authors":168,"venue":123,"cited_by_count":105,"oa_url":120,"card":178,"direction":183,"ingested_from":112},"W7222882764",[169,172,175],{"name":170,"orcid":171},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":173,"orcid":174},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":176,"orcid":177},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":179,"method":180,"finding":181,"direction":110,"opportunity":182},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","农业遥感与作物表型","2026-10-19T23:30:22.840943Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":196,"tags":198,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":253,"slug":256,"view_count":20,"doi":257,"paper":258,"created_at":270},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":146,"depth":56,"authority":127,"freshness":130,"relevant":20,"comment":195},"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[197],{"name":191,"url":188},[199,25,200,201,202],"智慧农业","可持续发展","气候变化","遥感监测",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[205],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":207},{"candidates":208,"agricultural_evidence":57},[209,216,221,228,232,239,246],{"code":44,"score":17,"supported":57,"title":210,"abstract":212,"tags":215},[211],"artificial intelligence",[213,61,59,214,211],"深度学习","deep learning",[25],{"code":38,"score":56,"supported":57,"title":217,"abstract":219,"tags":220},[218],"climate change",[201,218],[201],{"code":222,"score":156,"supported":57,"title":223,"abstract":224,"tags":227},"I1",[],[225,226],"精准农业","precision agriculture",[199],{"code":155,"score":156,"supported":57,"title":229,"abstract":230,"tags":231},[],[137,159],[202],{"code":233,"score":115,"supported":57,"title":234,"abstract":235,"tags":238},"H4",[],[236,237],"碳排放","carbon emission",[],{"code":240,"score":115,"supported":57,"title":241,"abstract":242,"tags":245},"H6",[],[243,244],"生物多样性","biodiversity",[],{"code":247,"score":115,"supported":57,"title":248,"abstract":249,"tags":252},"I5",[],[250,251],"物联网","internet of things",[],[254,255],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":257,"openalex_id":259,"authors":260,"venue":191,"cited_by_count":105,"oa_url":188,"card":263,"direction":269,"ingested_from":112},"W7222092855",[261],{"name":262,"orcid":9},"Deepmala Suryavanshi",{"tldr":264,"method":265,"finding":266,"direction":267,"opportunity":268},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":9,"source_name":277,"source_url":274,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":279,"sources":281,"tags":283,"primary_topic":287,"secondary_topics":288,"classification":290,"search_phrases":307,"slug":310,"view_count":20,"doi":311,"paper":312,"created_at":339},4806,"A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1837843","Introduction Wheat powdery mildew poses a serious threat to global wheat production, and its accurate identification is key to smart agriculture. Methods This study combines chlorophyll fluorescence imaging with deep learning algorithms to investigate the temporal dynamics of key fluorescence parameters (such as maximum photochemical efficiency, QYmax) during the progression of powderymildew. A QYmax-based chlorophyll fluorescence image dataset for wheat powdery mildew was constructed. Building on this, the LCRNet model was proposed, which effectively processes fluorescence image data through a collaborative mechanism integrating the Large Selective Adaptive Fusion (LSAF) and Coordinated Attention (CA) modules. Results On an independent test set, the model achieved an accuracy of 94% and an F1 score of 94.1%, outperforming comparison models. Discussion This study proposes a method for classifying the severity of powdery mildew in wheat that reduces the computational cost of fluorescence data processing, providing important methodological support and technical insights for advancing the intelligent, precise, and green transformation of crop disease prevention and control systems.","引言 小麦白粉病对全球小麦生产构成严重威胁，其准确识别是实现智慧农业的关键。方法 本研究将叶绿素荧光成像与深度学习算法相结合，探究白粉病发展过程中关键荧光参数（如最大光化学效率QYmax）的时间动态变化，构建了基于QYmax的小麦白粉病叶绿素荧光图像数据集。在此基础上，提出了LCRNet模型，该模型通过融合大选择性自适应融合（LSAF）模块与协调注意力（CA）模块的协同机制，有效处理荧光图像数据。结果 在独立测试集上，该模型达到了94%的准确率和94.1%的F1分数，优于对比模型。讨论 本研究提出了一种小麦白粉病严重程度分类方法，降低了荧光数据处理的计算成本，为推动作物病害防控体系向智能化、精准化和绿色化转型提供了重要的方法学支撑和技术启示。","Frontiers in Plant Science","2026-10-12T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":64,"freshness":130,"relevant":20,"comment":280},"将叶绿素荧光成像与深度学习结合用于小麦白粉病严重度识别，方法新颖、数据可靠，对智慧植保有实质参考价值。",[282],{"name":277,"url":274},[199,25,284,285,286],"病害识别","叶绿素荧光","小麦白粉病",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[289],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":291},{"candidates":292,"agricultural_evidence":57},[293,297,302],{"code":31,"score":64,"supported":57,"title":294,"abstract":295,"tags":296},[32],[33,32],[],{"code":44,"score":298,"supported":57,"title":299,"abstract":300,"tags":301},8,[],[213,214],[25,284],{"code":222,"score":156,"supported":57,"title":303,"abstract":304,"tags":306},[],[199,305],"smart agriculture",[199],[308,309],"小麦白粉病 叶绿素荧光 深度学习","LCRNet 小麦病害识别","小麦白粉病叶绿素荧光深度学习-4806","10.3389\u002Ffpls.2026.1837843",{"doi":311,"openalex_id":313,"authors":314,"venue":277,"cited_by_count":105,"oa_url":333,"card":334,"direction":269,"ingested_from":112},"W7221549180",[315,317,320,323,325,327,329,331],{"name":316,"orcid":9},"Yijia Chen",{"name":318,"orcid":319},"Chengyuan Hao","https:\u002F\u002Forcid.org\u002F0000-0002-6002-3963",{"name":321,"orcid":322},"Zhiyu Li","https:\u002F\u002Forcid.org\u002F0000-0003-3771-0199",{"name":324,"orcid":9},"Xupeng An",{"name":326,"orcid":9},"Jingxiang Huang",{"name":328,"orcid":9},"Xinchang Chen",{"name":330,"orcid":9},"Shidong Wang",{"name":332,"orcid":9},"ZhiChao Chen","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Fpdf",{"tldr":335,"method":336,"finding":337,"direction":110,"opportunity":338},"该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。","构建QYmax荧光图像数据集，提出融合LSAF与CA模块的LCRNet模型。","模型在独立测试集上准确率达94%、F1为94.1%，优于对比模型。","可探索多病害、多作物荧光图像通用模型，并融合田间时序数据实现早期预警。","2026-10-13T23:30:09.060156Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":348,"sources":351,"tags":353,"primary_topic":357,"secondary_topics":358,"classification":364,"search_phrases":398,"slug":401,"view_count":402,"doi":403,"paper":404,"created_at":421},4656,"Artificial Intelligence, Foundation Models, and Digital Twins for Climate-Resilient Agriculture: A Review","https:\u002F\u002Fdoi.org\u002F10.54963\u002Fia.v2i2.103039","Climate change, increasing food demand, and resource constraints are accelerating the transition from conventional precision agriculture toward smart, autonomous, and climate-resilient agricultural systems. Artificial intelligence (AI), foundation models, digital twins (DTs), the Internet of Things (IoT), remote sensing, edge–cloud computing, and big data analytics are emerging as key technologies for data-driven agricultural management. This review critically evaluates recent advances in AI-driven intelligent agriculture, with emphasis on machine learning, deep learning, explainable AI (XAI), generative and agentic AI, multimodal foundation models, and digital twins, together with the sensing and computing infrastructures required for their deployment. Relevant literature was identified using keywords related to AI, foundation models, digital twins, IoT, precision agriculture, and climate-resilient agriculture, with emphasis on recent peer-reviewed studies addressing technological applications, performance, limitations, challenges, and future prospects. The selected literature was critically synthesized to identify technological trends, knowledge gaps, and emerging research opportunities. The review highlights that the greatest potential of intelligent agriculture lies in integrating heterogeneous data from sensors, UAVs (unmanned aerial vehicles), satellites, weather systems, field operations, and agricultural knowledge to support context-aware prediction, decision-making, and autonomous action. However, data quality, model generalizability, explainability, interoperability, cybersecurity, computational requirements, and large-scale field validation remain major challenges. This review further develops an integrated perspective connecting AI, foundation models, multimodal data infrastructures, digital twins, and autonomous agricultural systems within a unified cyber-physical ecosystem. Future priorities include trustworthy AI, agentic decision-making, semantic interoperability, federated learning, continuous learning, and self-learning digital twins toward Agriculture 6.0.","气候变化、粮食需求增长与资源约束正加速推动传统精准农业向智能、自主且气候适应型的农业系统转型。人工智能（AI）、基础模型、数字孪生（DT）、物联网（IoT）、遥感、边缘—云计算和大数据分析正成为数据驱动农业管理的关键技术。本综述批判性地评估了人工智能驱动智能农业的最新进展，重点关注机器学习、深度学习、可解释人工智能（XAI）、生成式与代理式人工智能、多模态基础模型和数字孪生，以及其部署所需的传感与计算基础设施。相关文献通过人工智能、基础模型、数字孪生、物联网、精准农业和气候适应型农业等关键词检索获得，重点纳入近期同行评审研究，涵盖技术应用、性能、局限性、挑战与未来前景。对所选文献进行了批判性综合，以识别技术趋势、知识空白和新兴研究机遇。本综述强调，智能农业的最大潜力在于整合来自传感器、无人机（UAV）、卫星、气象系统、田间作业和农业知识的异构数据，以支持情境感知预测、决策和自主行动。然而，数据质量、模型泛化能力、可解释性、互操作性、网络安全、计算需求和大规模田间验证仍是主要挑战。本综述进一步提出了一种整合视角，将人工智能、基础模型、多模态数据基础设施、数字孪生和自主农业系统连接于统一的网络物理生态系统中。未来优先方向包括可信人工智能、代理式决策、语义互操作性、联邦学习、持续学习以及面向农业6.0的自学习数字孪生。","Intelligent Agriculture","2026-10-10T00:00:00Z",{"impact":18,"substance":349,"depth":17,"authority":19,"freshness":298,"relevant":20,"comment":350},20,"系统综述AI、基础模型与数字孪生在气候韧性农业中的集成路径与挑战，学术价值高，对智慧农业方向有较强参考意义。",[352],{"name":346,"url":343},[199,25,354,355,356],"农业大模型","数字孪生","气候韧性农业",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[359,360],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"code":247,"slug":361,"name":362,"path":363,"parent_code":48},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":365},{"candidates":366,"agricultural_evidence":57},[367,372,377,382,387,391],{"code":44,"score":368,"supported":57,"title":369,"abstract":370,"tags":371},19,[211],[213,61,59,214,211],[25,354],{"code":222,"score":373,"supported":57,"title":374,"abstract":375,"tags":376},11,[],[225,355,226],[355,199],{"code":38,"score":13,"supported":57,"title":378,"abstract":379,"tags":381},[],[201,380,218],"气候适应",[356],{"code":247,"score":130,"supported":57,"title":383,"abstract":384,"tags":386},[],[250,251,385],"IoT",[],{"code":155,"score":115,"supported":57,"title":388,"abstract":389,"tags":390},[],[137,159],[],{"code":392,"score":393,"supported":57,"title":394,"abstract":395,"tags":397},"A7",3,[],[396],"间作",[],[399,400],"农业基础模型 数字孪生","气候韧性农业 人工智能","农业基础模型数字孪生-4656",2,"10.54963\u002Fia.v2i2.103039",{"doi":403,"openalex_id":405,"authors":406,"venue":346,"cited_by_count":105,"oa_url":343,"card":416,"direction":269,"ingested_from":112},"W7221321372",[407,410,413],{"name":408,"orcid":409},"Krishan Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-7569-4590",{"name":411,"orcid":412},"Annu Khatri","https:\u002F\u002Forcid.org\u002F0009-0000-3103-9520",{"name":414,"orcid":415},"Indu Shekhar Thakur","https:\u002F\u002Forcid.org\u002F0000-0003-2382-1218",{"tldr":417,"method":418,"finding":419,"direction":110,"opportunity":420},"综述AI、基础模型与数字孪生如何支撑气候韧性农业，并指出关键挑战与未来方向。","文献综述，聚焦机器学习、深度学习、可解释AI、多模态基础模型与数字孪生。","最大潜力在于融合多源异构数据实现情境感知预测与自主决策，但数据质量与可解释性等仍是瓶颈。","可探索可信AI、联邦学习与自学习数字孪生在真实农田的规模化验证与语义互操作。","2026-10-11T23:30:07.465569Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":430,"sources":432,"tags":434,"primary_topic":436,"secondary_topics":437,"classification":440,"search_phrases":485,"slug":488,"view_count":402,"doi":489,"paper":490,"created_at":503},4213,"Agri digital twin–driven intelligent computing framework for real-time crop yield forecasting and sustainable decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116418","In the context of climate change, resource constraints, and increasing food demand, sustainable agricultural systems must become more productive and efficient in their use of resources. The Internet of Things (IoT) sensing and Artificial Intelligence (AI) technologies are allowing data-driven agriculture, but a common platform for real-time prediction of crop yield and sustainable agricultural decision-making is still not available. We propose an Agri Digital Twin (ADT) framework that combines a Gated Recurrent Unit (GRU) model with an IoT-based sensing system for temporal crop yield prediction and climate-resilient farm management. The framework constantly updates soil and weather parameters in real time, such as soil moisture, air temperature, humidity, atmospheric pressure, wind speed, wind gust, and wind direction, to create a real-time digital model of the crop field. The data collected is used to derive temporal features to predict yield, which are then categorized as low, medium, and high, and then further into sustainable, warning, and unsustainable to enable irrigation scheduling and crop stress management. The experimental results show that the proposed framework outperforms the others by achieving the Mean Absolute Error (MAE) of 0.0243, Root Mean Square Error (RMSE) of 0.0459, and Coefficient of Determination ( R 2 ) of 0.9254. Furthermore, it achieves an average prediction accuracy of 97.41% (95% CI: 95.03%, 99.80%), representing improvements of 4.09%, 6.32%, and 8.66% over BiLSTM, LSTM, and 1D-CNN, respectively ( p \u003C 0.05 ). The decision-support module also achieves a classification accuracy of 94.03%, showing its potential for sustainable and climate-resilient smart agriculture.","在气候变化、资源约束和粮食需求不断增长的背景下，可持续农业系统必须提高生产力并更高效地利用资源。物联网（IoT）传感与人工智能（AI）技术正在推动数据驱动型农业发展，但目前仍缺乏一个用于作物产量实时预测和可持续农业决策的通用平台。我们提出了一种农业数字孪生（Agri Digital Twin, ADT）框架，该框架将门控循环单元（Gated Recurrent Unit, GRU）模型与基于物联网的传感系统相结合，用于时序作物产量预测和气候适应型农场管理。该框架实时持续更新土壤和气象参数，如土壤湿度、气温、湿度、大气压、风速、阵风和风向，从而构建作物田地的实时数字模型。所采集的数据用于提取时序特征以预测产量，随后将产量划分为低、中、高，并进一步划分为可持续、警告和不可持续，以实现灌溉调度和作物胁迫管理。实验结果表明，所提框架优于其他方法，其平均绝对误差（MAE）为0.0243，均方根误差（RMSE）为0.0459，决定系数（R²）为0.9254。此外，其平均预测准确率达到97.41%（95% CI：95.03%，99.80%），相较于BiLSTM、LSTM和1D-CNN分别提升了4.09%、6.32%和8.66%（p \u003C 0.05）。决策支持模块的分类准确率也达到94.03%，显示出其在可持续且气候适应型智慧农业中的应用潜力。","Engineering Applications of Artificial Intelligence","2026-10-04T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":64,"freshness":130,"relevant":20,"comment":431},"提出农业数字孪生与GRU融合的实时产量预测与决策支持框架，方法新颖、指标扎实，对智慧农业研究有参考价值。",[433],{"name":428,"url":425},[199,25,250,355,435],"作物产量预测",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[438,439],{"code":247,"slug":361,"name":362,"path":363,"parent_code":48},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":441},{"candidates":442,"agricultural_evidence":57},[443,448,453,457,461,468,475,481],{"code":222,"score":368,"supported":57,"title":444,"abstract":446,"tags":447},[445],"digital twin",[199,355,305,445],[355,199],{"code":44,"score":127,"supported":57,"title":449,"abstract":451,"tags":452},[450],"decision support",[211],[25],{"code":247,"score":13,"supported":57,"title":454,"abstract":455,"tags":456},[],[250,251,385],[250],{"code":38,"score":130,"supported":57,"title":458,"abstract":459,"tags":460},[],[201,380,218],[],{"code":462,"score":115,"supported":57,"title":463,"abstract":464,"tags":467},"H2",[],[465,466],"土壤","soil",[],{"code":469,"score":115,"supported":57,"title":470,"abstract":471,"tags":474},"H3",[],[472,473],"灌溉","irrigation",[],{"code":476,"score":393,"supported":57,"title":477,"abstract":478,"tags":480},"H8",[],[479],"可持续农业",[],{"code":155,"score":20,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[435],[486,487],"Agri Digital Twin GRU 产量预测","物联网 数字孪生 智慧农业","AgriDigitalTwinGRU产量预测-4213","10.1016\u002Fj.engappai.2026.116418",{"doi":489,"openalex_id":491,"authors":492,"venue":428,"cited_by_count":105,"oa_url":425,"card":498,"direction":269,"ingested_from":112},"W7219849006",[493,496],{"name":494,"orcid":495},"Rohit Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-8484-6588",{"name":497,"orcid":9},"Munish Bhatia",{"tldr":499,"method":500,"finding":501,"direction":269,"opportunity":502},"提出农业数字孪生框架，结合GRU与物联网实现实时产量预测与可持续决策支持。","基于物联网传感实时采集土壤气象数据，用GRU模型做时序产量预测与分类。","预测MAE 0.0243、R² 0.9254，平均准确率97.41%，优于BiLSTM、LSTM和1","可探索多源遥感与数字孪生融合、跨区域迁移学习及决策闭环验证等空白。","2026-10-05T23:30:09.119891Z",{"id":505,"title":506,"url":507,"summary":508,"summary_zh":509,"content":9,"source_name":510,"source_url":507,"published_at":511,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":512,"score_detail":513,"sources":515,"tags":517,"primary_topic":518,"secondary_topics":519,"classification":522,"search_phrases":544,"slug":547,"view_count":402,"doi":548,"paper":549,"created_at":561},4058,"Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100535","Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge computing, and Digital Twins provide unprecedented opportunities to transform sustainability management from reactive monitoring into intelligent, predictive, and autonomous decision-making. IoT enables continuous environmental sensing, AI extracts knowledge from large heterogeneous datasets, edge intelligence supports low-latency analytics, while Digital Twins create dynamic virtual representations of physical systems capable of simulation and optimization. This critical integrative review examines recent developments in AI-enabled sustainability across smart energy systems, intelligent water management, precision agriculture, transportation, smart buildings, circular economy, and climate resilience. Rather than reviewing these technologies independently, the paper analyzes their interactions within an integrated digital ecosystem and discusses emerging topics including Green AI, carbon-aware computing, explainable AI, federated learning, cybersecurity, and sustainability governance. The review method combines a structured multi-database search with targeted evidence and standards updates through June 2026. Building upon the reviewed literature, the paper introduces the Sustainability Intelligence Framework (SIF), a conceptual seven-layer architecture integrating IoT sensing, edge intelligence, AI analytics, Digital Twins, decision intelligence, and sustainability assessment within a continuous feedback cycle. The proposed framework provides a systems-level perspective for designing adaptive, resilient, and resource-efficient infrastructures capable of supporting future sustainable development. The synthesis identifies continuous, auditable sustainability assessment (Layer 7), rather than the underlying six-layer IoT\u002Fcyber–physical stack, as the framework’s distinct contribution. It also shows that reported benefits remain difficult to compare because field validation, system boundaries, rebound effects, and lifecycle digital burdens are inconsistently reported.","可持续性已成为二十一世纪具有决定性意义的科学与社会挑战之一。气候变化、快速城市化、人口增长、能源需求上升以及自然资源减少，要求决策必须持续、自适应，并由可靠的环境智能提供支撑。传统监测系统往往依赖周期性测量和孤立分析工具，已不足以管理高度动态的环境系统。人工智能（AI）、物联网（IoT）、边缘计算和数字孪生（Digital Twins）的最新进展，为将可持续性管理从被动监测转变为智能、预测性和自主决策提供了前所未有的机遇。物联网能够实现持续环境感知，人工智能可从大规模异构数据集中提取知识，边缘智能支持低时延分析，而数字孪生则创建物理系统的动态虚拟表示，以支持仿真与优化。这篇批判性综合综述考察了AI赋能可持续性在智能能源系统、智能水管理、精准农业、交通、智能建筑、循环经济和气候韧性等领域的最新进展。本文并未孤立地评述这些技术，而是分析它们在集成数字生态系统中的相互作用，并讨论绿色AI、碳感知计算、可解释AI、联邦学习、网络安全和可持续性治理等新兴议题。综述方法结合了结构化多数据库检索，并通过截至2026年6月的针对性证据与标准更新加以补充。在所评述文献的基础上，本文提出可持续性智能框架（Sustainability Intelligence Framework, SIF），这是一个概念性七层架构，在持续反馈循环中整合物联网感知、边缘智能、AI分析、数字孪生、决策智能和可持续性评估。所提出的框架为设计能够支持未来可持续发展的自适应、韧性和资源高效型基础设施提供了系统层面视角。该综合指出，框架的独特贡献在于持续、可审计的可持续性评估（第7层），而非底层六层物联网\u002F网络—物理技术栈。研究还表明，由于现场验证、","Next Sustainability","2026-10-02T00:00:00Z",78,{"impact":17,"substance":349,"depth":17,"authority":19,"freshness":130,"relevant":20,"comment":514},"该综述提出七层可持续智能框架，系统整合AI、物联网与数字孪生，对智慧农业与气候韧性基础设施具有较高参考价值，但偏概念性、缺乏实证数据。",[516],{"name":510,"url":507},[199,25,250,355,225],{"code":247,"slug":361,"name":362,"path":363,"parent_code":48},[520,521],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":50,"locked":14,"version":51,"method":52,"evidence":523},{"candidates":524,"agricultural_evidence":57},[525,531,535,539],{"code":247,"score":17,"supported":57,"title":526,"abstract":527,"tags":530},[251],[250,528,251,385,529],"边缘计算","edge computing",[250],{"code":222,"score":127,"supported":57,"title":532,"abstract":533,"tags":534},[],[225,355,226],[355,199,225],{"code":44,"score":127,"supported":57,"title":536,"abstract":537,"tags":538},[211],[211],[25],{"code":38,"score":130,"supported":57,"title":540,"abstract":541,"tags":543},[],[201,218,542],"climate resilience",[],[545,546],"农业人工智能 数字孪生 智慧农业 精准农业","农业人工智能 数字孪生","农业人工智能数字孪生智慧农业精准农业-4058","10.1016\u002Fj.nxsust.2026.100535",{"doi":548,"openalex_id":550,"authors":551,"venue":510,"cited_by_count":105,"oa_url":507,"card":555,"direction":269,"ingested_from":112},"W7216004668",[552],{"name":553,"orcid":554},"Wael Maged Badawy","https:\u002F\u002Forcid.org\u002F0000-0003-0251-9124",{"tldr":556,"method":557,"finding":558,"direction":559,"opportunity":560},"综述AI、物联网与数字孪生融合的可持续性智能，提出七层SIF框架。","多数据库结构化检索，整合IoT、边缘计算、AI与数字孪生文献。","持续可审计的可持续性评估是框架核心贡献，但实地验证仍难比较。","其他","农业场景下SIF七层框架的田间验证与可审计碳\u002F资源评估方法尚属空白。","2026-10-03T23:30:09.950820Z"]