[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3856":3,"related-3856":94},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":69,"slug":72,"view_count":63,"doi":73,"paper":74,"created_at":93},3856,"Green Industry Strategy in Palm Oil Agribusiness: Integration of the Involution Index and Life Cycle Assessment","https:\u002F\u002Fdoi.org\u002F10.59188\u002Feduvest.v6i9.53375","Indonesia’s palm oil industry contributes substantially to employment generation, export earnings, and the national economy; however, its operations remain associated with greenhouse gas emissions, resource inefficiency, biodiversity loss, and increasing sustainability challenges. This study aims to identify internal and external factors influencing green industry practices at PT Teladan Prima Agro and formulate strategies to enhance environmental performance, operational efficiency, and cost efficiency. A case study approach was employed using primary data collected through questionnaires, in-depth interviews with purposively selected internal and external experts, and field observations, complemented by secondary data. The data were analyzed using the Internal Factor Evaluation (IFE) matrix, External Factor Evaluation (EFE) matrix, Internal–External (IE) matrix, TOWS analysis, and the four-action framework of the Blue Ocean Strategy. The company achieved an IFE score of 2.36 and an EFE score of 2.67, positioning it in quadrant V, which represents a hold-and-maintain strategic position. Six strategic alternatives were developed, consisting of two market penetration strategies and four product development strategies. The key strategic priorities include expanding circular economy practices and renewable energy initiatives, strengthening digital traceability and carbon Measurement, Reporting, and Verification (MRV) systems, enhancing climate risk identification and monitoring, adopting human rights-based labor standards, and implementing precision agriculture technologies. The study concludes that green industry initiatives require deeper integration into the business model to transform environmental improvements into tangible, measurable, sustainable, long-term operational and financial value.","印度尼西亚的棕榈油产业对就业创造、出口创汇和国民经济贡献显著；然而，其运营仍与温室气体排放、资源低效、生物多样性丧失以及日益严峻的可持续性挑战相关联。本研究旨在识别影响PT Teladan Prima Agro绿色产业实践的内外部因素，并制定提升环境绩效、运营效率和成本效率的战略。研究采用案例研究方法，通过问卷调查、对有意选取的内外部专家进行深度访谈以及实地观察收集一手数据，并辅以二手数据。数据分析采用内部因素评价（IFE）矩阵、外部因素评价（EFE）矩阵、内部—外部（IE）矩阵、TOWS分析以及蓝海战略的四步行动框架。该公司的IFE得分为2.36，EFE得分为2.67，处于第五象限，即保持和维持的战略定位。研究制定了六项战略备选方案，包括两项市场渗透战略和四项产品开发战略。关键战略优先事项包括：扩大循环经济实践和可再生能源举措，加强数字可追溯性和碳测量、报告与核查（MRV）系统，增强气候风险识别与监测，采用基于人权的劳工标准，以及实施精准农业技术。研究结论认为，绿色产业举措需要更深层次地融入商业模式，才能将环境改善转化为切实可衡量、可持续的长期运营和财务价值。",null,"Eduvest - Journal Of Universal Studies","2026-09-29T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,6,8,1,"以印尼棕榈油企业为案例，结合IE矩阵与蓝海战略提出绿色产业与碳MRV策略，方法有增量但属企业个案，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准农业","碳核算","棕榈油","绿色产业","循环经济",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":68},[39,47,54,61],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I1",7,true,[],[27,45],"precision agriculture",[27],{"code":48,"score":20,"supported":42,"title":49,"abstract":50,"tags":53},"B7",[],[51,52],"温室","greenhouse",[],{"code":55,"score":20,"supported":42,"title":56,"abstract":57,"tags":60},"H6",[],[58,59],"生物多样性","biodiversity",[],{"code":62,"score":63,"supported":42,"title":64,"abstract":65,"tags":67},"I7",3,[],[66],"研究方法",[],"多个产业证据相当，需要确认主归属",[70,71],"印尼 棕榈油 绿色产业","循环经济 精准农业 绿色产业 棕榈油","印尼棕榈油绿色产业-3856","10.59188\u002Feduvest.v6i9.53375",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":91,"ingested_from":92},"W7214801117",[77,79,81],{"name":78,"orcid":9},"Pryo Adi Lukito",{"name":80,"orcid":9},"Yudha Heryawan Asnawi",{"name":82,"orcid":83},"Popong Nurhayati","https:\u002F\u002Forcid.org\u002F0009-0003-5665-5671",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"以印尼棕榈油企业为例，用内外部因素矩阵与生命周期评估制定绿色产业战略。","案例研究、问卷与专家访谈，IFE\u002FEFE\u002FIE矩阵、TOWS与蓝海四步框架。","企业处于维持型战略位置，提出循环经济、数字追溯与碳MRV等六项战略。","农业绿色发展与碳","可将数字追溯与碳MRV结合，量化绿色战略对运营与财务绩效的长期影响。","数字乡村与农业信息化","openalex","2026-09-30T23:30:40.514702Z",{"total":20,"page":22,"page_size":20,"items":95},[96,220,276,387,439,518],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":111,"tags":113,"primary_topic":118,"secondary_topics":124,"classification":136,"search_phrases":194,"slug":197,"view_count":84,"doi":198,"paper":199,"created_at":219},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":106,"substance":107,"depth":106,"authority":108,"freshness":109,"relevant":22,"comment":110},18,22,13,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[112],{"name":102,"url":99},[114,115,116,27,117],"智慧农业","农业人工智能","物联网","气候韧性",{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[125,130],{"code":126,"slug":127,"name":128,"path":129,"parent_code":123},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":137,"locked":14,"version":35,"method":36,"evidence":138},"classified",{"candidates":139,"agricultural_evidence":42},[140,151,155,162,168,175,182,188],{"code":119,"score":106,"supported":42,"title":141,"abstract":143,"tags":150},[142],"artificial intelligence",[144,145,146,147,148,149],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[115],{"code":40,"score":19,"supported":42,"title":152,"abstract":153,"tags":154},[45],[27,45],[114,27],{"code":126,"score":13,"supported":42,"title":156,"abstract":157,"tags":161},[],[116,158,159,160],"边缘计算","IoT","edge computing",[116],{"code":131,"score":109,"supported":42,"title":163,"abstract":164,"tags":167},[],[165,166,132],"灌溉","水资源",[],{"code":169,"score":41,"supported":42,"title":170,"abstract":171,"tags":174},"H7",[],[172,173],"气候变化","climate change",[117],{"code":176,"score":20,"supported":42,"title":177,"abstract":178,"tags":181},"H2",[],[179,180],"土壤","soil",[],{"code":183,"score":63,"supported":42,"title":184,"abstract":185,"tags":187},"A7",[],[186],"间作",[],{"code":189,"score":63,"supported":42,"title":190,"abstract":191,"tags":193},"F3",[],[192],"养分管理",[],[195,196],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":198,"openalex_id":200,"authors":201,"venue":102,"cited_by_count":84,"oa_url":99,"card":212,"direction":218,"ingested_from":92},"W7223121456",[202,205,207,209],{"name":203,"orcid":204},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":206,"orcid":9},"Oluwadare OYEBODE",{"name":208,"orcid":9},"Iretiayo Adelaiye",{"name":210,"orcid":211},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":213,"method":214,"finding":215,"direction":216,"opportunity":217},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.151376Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":9,"content":9,"source_name":225,"source_url":223,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":232,"tags":234,"primary_topic":237,"secondary_topics":241,"classification":242,"search_phrases":255,"slug":258,"view_count":84,"doi":259,"paper":260,"created_at":275},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":19,"substance":229,"depth":106,"authority":230,"freshness":13,"relevant":22,"comment":231},21,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[233],{"name":225,"url":223},[114,115,27,235,236],"机器视觉","作物行检测",{"code":40,"slug":238,"name":239,"path":240,"parent_code":123},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":137,"locked":14,"version":35,"method":36,"evidence":243},{"candidates":244,"agricultural_evidence":42},[245,250],{"code":40,"score":246,"supported":42,"title":247,"abstract":248,"tags":249},5,[],[45],[114,27],{"code":119,"score":251,"supported":14,"title":252,"abstract":253,"tags":254},2,[],[],[115,235],[256,257],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":259,"openalex_id":261,"authors":262,"venue":225,"cited_by_count":84,"oa_url":9,"card":9,"direction":9,"ingested_from":92},"W7223117828",[263,266,269,272],{"name":264,"orcid":265},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":267,"orcid":268},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":270,"orcid":271},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":273,"orcid":274},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":289,"primary_topic":294,"secondary_topics":300,"classification":301,"search_phrases":349,"slug":352,"view_count":84,"doi":353,"paper":354,"created_at":386},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":107,"substance":18,"depth":106,"authority":108,"freshness":21,"relevant":22,"comment":286},"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[288],{"name":282,"url":279},[114,27,290,291,292,293],"基因编辑","植物病害","生物防治","纳米技术",{"code":295,"slug":296,"name":297,"path":298,"parent_code":299},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":137,"locked":14,"version":35,"method":36,"evidence":302},{"candidates":303,"agricultural_evidence":42},[304,308,313,320,324,328,335,340,344],{"code":40,"score":21,"supported":42,"title":305,"abstract":306,"tags":307},[],[27,45],[114,27],{"code":295,"score":41,"supported":42,"title":309,"abstract":310,"tags":312},[],[311,292],"农药",[292],{"code":314,"score":20,"supported":42,"title":315,"abstract":316,"tags":319},"A8",[],[317,318],"粮食安全","food security",[],{"code":176,"score":20,"supported":42,"title":321,"abstract":322,"tags":323},[],[179,180],[],{"code":169,"score":20,"supported":42,"title":325,"abstract":326,"tags":327},[],[172,173],[],{"code":329,"score":20,"supported":42,"title":330,"abstract":331,"tags":334},"I3",[],[332,333],"遥感","remote sensing",[],{"code":55,"score":63,"supported":42,"title":336,"abstract":337,"tags":339},[],[338],"农业生态",[],{"code":119,"score":63,"supported":42,"title":341,"abstract":342,"tags":343},[],[142],[],{"code":345,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},"F2",[],[],[290],[350,351],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":353,"openalex_id":355,"authors":356,"venue":282,"cited_by_count":84,"oa_url":279,"card":381,"direction":218,"ingested_from":92},"W7223044776",[357,360,363,366,369,372,375,378],{"name":358,"orcid":359},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":361,"orcid":362},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":364,"orcid":365},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":367,"orcid":368},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":370,"orcid":371},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":373,"orcid":374},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":376,"orcid":377},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":379,"orcid":380},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":382,"method":383,"finding":384,"direction":218,"opportunity":385},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":9,"content":9,"source_name":392,"source_url":9,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":406,"classification":407,"search_phrases":426,"slug":429,"view_count":84,"doi":9,"paper":430,"created_at":438},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":17,"substance":18,"depth":396,"authority":108,"freshness":21,"relevant":22,"comment":397},17,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[399],{"name":392,"url":390},[115,145,27,401,402],"土壤数据","作物分类",{"code":176,"slug":180,"name":404,"path":405,"parent_code":135},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":137,"locked":14,"version":35,"method":36,"evidence":408},{"candidates":409,"agricultural_evidence":42},[410,414,418,422],{"code":176,"score":18,"supported":42,"title":411,"abstract":412,"tags":413},[179,180],[179],[401],{"code":119,"score":251,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[115,145],{"code":40,"score":22,"supported":14,"title":419,"abstract":420,"tags":421},[],[],[27],{"code":329,"score":22,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[402],[427,428],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":431,"venue":9,"cited_by_count":84,"oa_url":9,"card":432,"direction":216,"ingested_from":437},[],{"tldr":433,"method":434,"finding":435,"direction":216,"opportunity":436},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":447,"score_detail":448,"sources":454,"tags":456,"primary_topic":458,"secondary_topics":459,"classification":460,"search_phrases":486,"slug":489,"view_count":477,"doi":490,"paper":491,"created_at":517},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":449,"substance":450,"depth":451,"authority":452,"freshness":109,"relevant":22,"comment":453},26,24,19,15,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[455],{"name":445,"url":442},[114,115,27,332,457],"全球评估",{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},[],{"status":137,"locked":14,"version":35,"method":36,"evidence":461},{"candidates":462,"agricultural_evidence":42},[463,468,472,476,481],{"code":40,"score":464,"supported":42,"title":465,"abstract":466,"tags":467},11,[],[114,27,45],[114,27],{"code":119,"score":41,"supported":42,"title":469,"abstract":470,"tags":471},[],[145,147],[115],{"code":131,"score":20,"supported":42,"title":473,"abstract":474,"tags":475},[],[165,132],[],{"code":329,"score":477,"supported":42,"title":478,"abstract":479,"tags":480},4,[],[332],[332],{"code":295,"score":63,"supported":42,"title":482,"abstract":483,"tags":485},[],[484],"除草",[],[487,488],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":490,"openalex_id":492,"authors":493,"venue":445,"cited_by_count":84,"oa_url":442,"card":512,"direction":218,"ingested_from":92},"W7222974188",[494,497,500,503,506,509],{"name":495,"orcid":496},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":498,"orcid":499},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":501,"orcid":502},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":504,"orcid":505},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":507,"orcid":508},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":510,"orcid":511},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":513,"method":514,"finding":515,"direction":218,"opportunity":516},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":519,"title":520,"url":521,"summary":522,"summary_zh":523,"content":9,"source_name":524,"source_url":521,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":525,"score_detail":526,"sources":528,"tags":530,"primary_topic":535,"secondary_topics":541,"classification":543,"search_phrases":579,"slug":582,"view_count":84,"doi":583,"paper":584,"created_at":594},5381,"Climate-Smart Floriculture: Strategies for Sustainable Flower Production Under a Changing Climate","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23467834","Floriculture is a high-value horticultural sector comprising cut flowers, potted flowering plants, foliage plants, ornamental bulbs and nursery plants. The sector is increasingly exposed to climate variability through rising temperature, drought, irregular rainfall, extreme weather events, elevated atmospheric CO₂ and changing pest and disease pressure. These stresses can influence vegetative growth, flowering, flower quality, production scheduling and postharvest performance, thereby affecting the economic value of ornamental crops. Climate-smart floriculture provides an integrated framework for improving resilience while maintaining productivity and resource-use efficiency. Key strategies include climate-resilient cultivar selection, efficient irrigation, soil-moisture conservation, protected cultivation, microclimate regulation, integrated pest management and precision technologies. Genetic improvement offers a long-term approach for developing cultivars capable of maintaining desirable ornamental traits under environmental stress, whereas protected cultivation can buffer crops against external climatic fluctuations. Efficient irrigation, rainwater harvesting and improved soil management can reduce vulnerability to water scarcity, while integrated pest management can address climate-associated changes in pest and disease pressure. Precision agriculture, controlled-environment agriculture and machine-learning-based decision systems offer additional opportunities for real-time monitoring and resource optimisation. However, technological adoption is influenced by investment costs, knowledge availability, extension services, market access and supportive policies. This article discusses the major climate-related risks to floriculture and evaluates integrated strategies for developing productive, resource-efficient and climate-resilient flower production systems.","花卉产业是一个高附加值的园艺部门，包括切花、盆栽开花植物、观叶植物、观赏球根植物和苗木。该产业日益受到气候变率的威胁，表现为气温升高、干旱、降雨不规律、极端天气事件、大气CO₂浓度升高以及病虫害压力变化。这些胁迫可影响营养生长、开花、花卉品质、生产调度和采后性能，进而影响观赏作物的经济价值。气候智慧型花卉产业提供了一个综合框架，可在保持生产力和资源利用效率的同时提高韧性。关键策略包括气候韧性品种选择、高效灌溉、土壤水分保持、设施栽培、微气候调控、病虫害综合治理和精准技术。遗传改良为培育能够在环境胁迫下保持理想观赏性状的品种提供了长期途径，而设施栽培可缓冲作物免受外部气候波动的影响。高效灌溉、雨水收集和改善的土壤管理可降低对水资源短缺的脆弱性，而病虫害综合治理可应对气候相关的病虫害压力变化。精准农业、受控环境农业和基于机器学习的决策系统为实时监测和资源优化提供了额外机会。然而，技术采用受到投资成本、知识可得性、推广服务、市场准入和支持性政策的影响。本文讨论了花卉产业面临的主要气候相关风险，并评估了发展高产、资源高效和气候韧性花卉生产系统的综合策略。","Zenodo (CERN European Organization for Nuclear Research)",77,{"impact":106,"substance":18,"depth":396,"authority":108,"freshness":109,"relevant":22,"comment":527},"系统综述花卉业气候风险与综合应对策略，具行业参考价值，但属综述性论文、非突破性成果。",[529],{"name":524,"url":521},[27,531,532,533,534],"病虫害防控","气候智慧农业","花卉产业","设施园艺",{"code":536,"slug":537,"name":538,"path":539,"parent_code":540},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[542],{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},{"status":137,"locked":14,"version":35,"method":36,"evidence":544},{"candidates":545,"agricultural_evidence":42},[546,553,557,563,567,571,575],{"code":536,"score":106,"supported":42,"title":547,"abstract":549,"tags":552},[548],"floriculture",[550,551,548],"花卉","ornamental",[533],{"code":131,"score":109,"supported":42,"title":554,"abstract":555,"tags":556},[],[165,166,132],[],{"code":169,"score":41,"supported":42,"title":558,"abstract":559,"tags":562},[],[560,561],"干旱","drought",[532],{"code":40,"score":41,"supported":42,"title":564,"abstract":565,"tags":566},[],[27,45],[27],{"code":176,"score":20,"supported":42,"title":568,"abstract":569,"tags":570},[],[179,180],[],{"code":119,"score":63,"supported":42,"title":572,"abstract":573,"tags":574},[],[145],[],{"code":48,"score":22,"supported":14,"title":576,"abstract":577,"tags":578},[],[],[534],[580,581],"花卉 可持续生产 气候变化","气候智慧农业 病虫害防控 精准农业 花卉产业","花卉可持续生产气候变化-5381","10.5281\u002Fzenodo.23467834",{"doi":583,"openalex_id":585,"authors":586,"venue":524,"cited_by_count":84,"oa_url":521,"card":589,"direction":218,"ingested_from":92},"W7222973900",[587],{"name":588,"orcid":9},"Shriya Gupta1 and Harish Kumar2",{"tldr":590,"method":591,"finding":592,"direction":218,"opportunity":593},"综述气候智慧型花卉生产策略，评估气候变化风险与综合适应措施。","文献综述，整合抗逆品种、灌溉、保护地栽培、IPM与精准技术。","综合策略可提升花卉气候韧性，但技术采纳受成本、知识与政策制约。","花卉领域精准农业与机器学习决策应用少，可研究低成本智能环境调控与品种-技术协同。","2026-10-20T23:30:08.681802Z"]