[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-09-06":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-09-06","AI与数字化驱动农业转型：从精准作业到县域均衡发展",[7,8,9,10,11,12],"多篇研究聚焦AI、大模型及物联网在农技推广、精准施肥喷药中的应用，如UAV-UGV协同路径规划、马铃薯种薯切割决策等，提升作业效率与智能化水平。","中国数字乡村试点政策显著缩小县域间经济差距，基于1564县面板数据验证，为数字农业促进均衡发展提供实证。","精准农业结合生命周期评估（LCA）在意大利小麦生产中展现可持续性优势，为农业减排提供案例参考。","撒哈拉以南非洲数字化与粮食安全系统综述表明，数字技术有潜力提升生产力，但需关注公平获取与治理机制。","农业金融科技与治理、情商协同赋能印度小农可持续生计，从脆弱性转向赋权，强调多维因素互动。","农业4.0整合AI与IoT的综述指出，数据驱动、自动化生产模式是未来方向，但面临技术应用与推广挑战。","本期日报汇集12篇前沿研究，覆盖AI、数字乡村、精准农业、农业金融科技及区域发展等主题，展现数字化与智能技术在全球农业中的应用广度与深度，从技术突破到政策效应，再到可持续发展路径，为农业现代化提供多维度洞察。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[15,113,178,236,348,418,490,559,609,664,747,817,876,945,1028],{"id":16,"title":17,"url":18,"summary":19,"summary_zh":20,"content":21,"source_name":22,"source_url":18,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":27,"score_detail":28,"sources":35,"tags":37,"primary_topic":42,"secondary_topics":48,"classification":60,"search_phrases":87,"slug":90,"view_count":91,"doi":92,"paper":93,"created_at":112},1747,"Artificial Intelligence and Large Language Models in Agricultural Extension: Critical Synthesis of Adoption, Efficacy, and Equity in Smallholder Advisory Systems","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fair\u002F2026\u002Fv27i51723","Agricultural extension and advisory services are undergoing a rapid digital transformation driven by artificial intelligence (AI), machine learning, natural language processing, and, more recently, generative artificial intelligence and large language models (LLMs). These technologies have the potential to address persistent constraints in conventional agricultural extension, including limited adviser-to-farmer ratios, geographical isolation, inadequate localisation of information, and delays in the dissemination of climate, market, and agronomic information. LLM-based advisory systems are particularly significant because they can transform complex agricultural knowledge into conversational, multilingual, and potentially personalised guidance. However, their technical capability does not automatically translate into effective or equitable agricultural outcomes. This review synthesises emerging evidence on the adoption, efficacy, scalability, and equity implications of AI and LLM-based agricultural advisory systems, with particular emphasis on smallholder farmers in low- and middle-income countries. The literature indicates that AI-enabled advisory systems can improve access to timely information, facilitate question-answering, support crop and pest management, and extend the reach of human extension personnel. Emerging platforms such as Farmer.Chat demonstrate the feasibility of large-scale generative AI-enabled advisory delivery, while recent experiments with retrieval-augmented generation indicate promising pathways for improving factual grounding and contextual relevance. Nevertheless, evidence of direct impacts on farm productivity, profitability, resilience, and sustained behavioural change remains considerably weaker than evidence of technological feasibility and user engagement. Major limitations include hallucination, outdated or geographically inappropriate information, language and dialect constraints, weak interoperability with local agricultural databases, limited transparency, data governance concerns, and overreliance on automated recommendations. Equity is an equally important concern because digital access, literacy, gender, income, connectivity, and social norms influence who can benefit from AI-enabled services. The review argues that LLMs should therefore be positioned as augmentation technologies within pluralistic extension systems rather than as substitutes for human advisers. Future agricultural AI systems should combine retrieval from authoritative local knowledge bases, multimodal and multilingual interfaces, human oversight, participatory design, continuous evaluation, responsible data governance, and explicit equity metrics. The future value of AI in agricultural extension will ultimately depend not on model sophistication alone but on the quality of the socio-technical systems in which these technologies are embedded.","农业推广与咨询服务正在经历一场由人工智能（AI）、机器学习、自然语言处理以及近年来兴起的生成式人工智能和大语言模型（LLMs）驱动的快速数字化转型。这些技术有望解决传统农业推广中长期存在的制约因素，包括推广人员与农民比例有限、地理隔离、信息本地化不足，以及气候、市场和农艺信息传播的延迟。基于大语言模型的咨询系统尤为重要，因为它们能够将复杂的农业知识转化为对话式、多语言且可能实现个性化的指导。然而，其技术能力并不会自动转化为有效或公平的农业成果。本综述综合了关于基于AI和大语言模型的农业咨询系统在采用、有效性、可扩展性和公平性影响方面的新兴证据，特别关注低收入和中等收入国家的小农户。文献表明，AI赋能的咨询系统能够改善及时信息的获取，促进问答交互，支持作物与病虫害管理，并扩展人工推广人员的服务范围。诸如Farmer.Chat等新兴平台展示了大规模生成式AI赋能咨询服务的可行性，而近期关于检索增强生成的实验则表明，在改善事实依据和情境相关性方面存在有前景的路径。尽管如此，关于对农业生产率、盈利能力、韧性及持续性行为改变产生直接影响的证据，仍明显弱于关于技术可行性和用户参与度的证据。主要局限包括幻觉现象、信息过时或地理上不适用、语言和方言限制、与当地农业数据库的互操作性薄弱、透明度有限、数据治理问题，以及对自动化建议的过度依赖。公平性同样是一个重要关切，因为数字接入、素养、性别、收入、连通性和社会规范都会影响谁能够从AI赋能的服务中受益。本综述认为，大语言模型应被定位为多元推广体系中的增强技术，而非人类推广人员的替代品。未来的农业AI系统应将检索增强生成与结构化农业知识图谱、",null,"Advances in Research","2026-09-04T00:00:00Z","论文",10,false,82,{"impact":29,"substance":29,"depth":30,"authority":31,"freshness":32,"relevant":33,"comment":34},22,20,13,5,1,"系统综述AI与LLM在农技推广中的应用、效能与公平性，对智慧农业实践有重要参考价值。",[36],{"name":22,"url":18},[38,39,40,41],"农业人工智能","大语言模型","小农户","农技推广",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[49,55],{"code":50,"slug":51,"name":52,"path":53,"parent_code":54},"G1","extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"code":56,"slug":57,"name":58,"path":59,"parent_code":54},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services",{"status":61,"locked":26,"version":62,"method":63,"evidence":64},"classified","2026-10-22.2","rules",{"candidates":65,"agricultural_evidence":68},[66,75,82],{"code":43,"score":67,"supported":68,"title":69,"abstract":71,"tags":74},19,true,[70],"artificial intelligence",[72,73,70],"机器学习","machine learning",[38,39],{"code":50,"score":76,"supported":68,"title":77,"abstract":79,"tags":81},15,[78],"agricultural extension",[80,78],"农业推广",[41],{"code":56,"score":76,"supported":68,"title":83,"abstract":85,"tags":86},[84],"smallholder",[40,84],[40],[88,89],"农业人工智能 大语言模型 农技推广 小农户","农业人工智能 大语言模型","农业人工智能大语言模型农技推广小农户-1747",2,"10.9734\u002Fair\u002F2026\u002Fv27i51723",{"doi":92,"openalex_id":94,"authors":95,"venue":22,"cited_by_count":103,"oa_url":18,"card":104,"direction":110,"ingested_from":111},"W7208785535",[96,99,101],{"name":97,"orcid":98},"Swati Sucharita","https:\u002F\u002Forcid.org\u002F0000-0003-3409-3295",{"name":100,"orcid":21},"Pratima Sachan",{"name":102,"orcid":21},"T. Sravan Kumar",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"综述AI与LLM在农业推广中的应用，评估其采纳、效果与公平性，尤其关注小农户。","系统综述新兴证据，分析AI\u002FLLM咨询系统的采用、效能、可扩展性及公平性。","AI可提升信息获取，但生产力影响证据弱，存在幻觉、公平性等问题，应作为增强而非替代。","农业人工智能与决策模型","研究空白：LLM在农业推广中的实际生产力影响与公平性机制，以及如何结合本地数据与人类顾问实现增强。","数字乡村与农业信息化","openalex","2026-09-05T23:30:36.654266Z",{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":21,"source_name":119,"source_url":116,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":120,"score_detail":121,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":136,"classification":137,"search_phrases":154,"slug":157,"view_count":158,"doi":159,"paper":160,"created_at":177},1745,"National digital village pilot construction alleviates the economic gap between counties in China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69800-z","This paper exploits panel data on 1,564 Chinese counties from 2015 to 2023 and applies a difference-in-differences approach to evaluate the impact of digital village pilot on inter-county economic disparities. The results show that the pilots significantly narrow county-level economic gaps, a finding that remains robust across multiple tests. Mechanism analysis suggests that digital village initiatives enhance endogenous county development by promoting technological innovation and rural entrepreneurship, with industrial upgrading playing a moderating role. It has a stronger impact on regions with a lower degree of reliance on agriculture, regions with greater financial support, and the central and western regions. Policy implications include the steady expansion of digital village pilots, strengthening of innovation and entrepreneurship, guidance for industrial upgrading, and improved supportive policies to foster coordinated regional development.","本文利用2015至2023年中国1564个县的面板数据，采用双重差分法评估数字乡村试点对县域间经济差距的影响。结果显示，试点显著缩小了县级经济差距，这一发现在多项检验中依然稳健。机制分析表明，数字乡村建设通过促进技术创新和农村创业来增强县域内生发展动力，其中产业升级发挥了调节作用。对于农业依赖程度较低、金融支持力度较大的地区以及中西部地区，其影响更为显著。政策启示包括稳步扩大数字乡村试点范围、强化创新创业支持、引导产业升级，并完善配套政策以促进区域协调发展。","Scientific Reports",80,{"impact":122,"substance":29,"depth":123,"authority":31,"freshness":91,"relevant":33,"comment":124},25,18,"基于大规模面板数据评估数字乡村试点对县域经济差距的影响，结论可靠，政策启示明确。",[126],{"name":119,"url":116},[128,129,130],"数字乡村","乡村振兴","县域经济",{"code":132,"slug":133,"name":134,"path":135,"parent_code":47},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":138},{"candidates":139,"agricultural_evidence":68},[140,145,149],{"code":132,"score":76,"supported":68,"title":141,"abstract":143,"tags":144},[142],"digital village",[128,142],[128],{"code":54,"score":33,"supported":26,"title":146,"abstract":147,"tags":148},[],[],[129],{"code":150,"score":33,"supported":26,"title":151,"abstract":152,"tags":153},"G4",[],[],[130],[155,156],"乡村振兴 县域经济 数字乡村","乡村振兴 县域经济","乡村振兴县域经济数字乡村-1745",3,"10.1038\u002Fs41598-026-69800-z",{"doi":159,"openalex_id":161,"authors":162,"venue":119,"cited_by_count":103,"oa_url":116,"card":172,"direction":110,"ingested_from":111},"W7208750940",[163,166,168,170],{"name":164,"orcid":165},"Huili Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5534-0155",{"name":167,"orcid":21},"Mande Zhu",{"name":169,"orcid":21},"Wanci Tang",{"name":171,"orcid":21},"Chengxiu Li",{"tldr":173,"method":174,"finding":175,"direction":110,"opportunity":176},"数字乡村试点显著缩小了县域经济差距，通过促进技术创新和农村创业实现。","使用2015-2023年1564个县面板数据，采用双重差分法评估政策效应。","数字乡村试点显著缩小县域经济差距，且对非农依赖低、金融支持强及中西部区域效果更明显。","可探究数字乡村试点对农业全要素生产率或农户收入的影响，或结合空间溢出效应分析区域协同发展机制。","2026-09-05T23:30:35.454261Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":21,"source_name":184,"source_url":181,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":185,"score_detail":186,"sources":190,"tags":192,"primary_topic":197,"secondary_topics":198,"classification":199,"search_phrases":212,"slug":215,"view_count":158,"doi":216,"paper":217,"created_at":235},1730,"PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjxb\u002Ferag436","Abstract Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 ( capd2-2 ), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. Highlight We combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.","定量花粉活力分析是植物生殖生物学中一个关键但劳动密集的步骤。现有的深度学习分割一切模型（SAM）在亚历山大染色花药中无法可靠地分割有活力的花粉。为解决此问题，我们对现有的Cellpose-SAM模型进行了微调，用于花粉分割。我们将其整合到PAT（花粉分析工具）中，这是一个跨平台的桌面应用程序。PAT具备实例分割及交互式质量控制、应用内模型再训练模块以及可直接用于发表的统计输出功能。我们在半不育拟南芥smg7-6突变体的EMS抑制子筛选中部署了PAT，从而能够高效地对候选突变体进行优先级排序，以进行全基因组测序和候选突变定位。该筛选鉴定出CAP-D2（capd2-2）中的一个点突变，该基因编码凝缩蛋白I的一个亚基，该突变能够挽救smg7-6的减数分裂表型。值得注意的是，凝缩蛋白II亚基（CAP-D3和CAP-H2）的突变并未赋予挽救效果。进一步的特征分析表明，capd2-2等位基因属于亚效等位基因，在营养生长、染色质中心压缩或转座子沉默方面未显示任何缺陷。总体而言，我们证明了易于使用的人工智能工具具有弥合植物表型分析差距并加速生物学发现的潜力。亮点：我们将人工智能驱动的图像分析与易于使用的桌面应用相结合，实现了植物花粉计数的自动化，并利用该方法鉴定了一个新的减数分裂缺陷遗传抑制子。","Journal of Experimental Botany",79,{"impact":123,"substance":29,"depth":123,"authority":187,"freshness":188,"relevant":33,"comment":189},14,7,"AI工具用于花粉活力自动分析，加速遗传筛选，对育种和表型分析有实用价值。",[191],{"name":184,"url":181},[193,38,194,195,196],"智慧农业","育种","表型分析","图像识别",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},[],{"status":61,"locked":26,"version":62,"method":63,"evidence":200},{"candidates":201,"agricultural_evidence":68},[202,207],{"code":43,"score":32,"supported":68,"title":203,"abstract":204,"tags":206},[],[205],"深度学习",[38,196],{"code":208,"score":33,"supported":26,"title":209,"abstract":210,"tags":211},"I1",[],[],[193],[213,214],"农业人工智能 图像识别 智慧农业 表型分析","农业人工智能 图像识别","农业人工智能图像识别智慧农业表型分析-1730","10.1093\u002Fjxb\u002Ferag436",{"doi":216,"openalex_id":218,"authors":219,"venue":184,"cited_by_count":103,"oa_url":181,"card":229,"direction":234,"ingested_from":111},"W7160664980",[220,223,226],{"name":221,"orcid":222},"Darya Volkava","https:\u002F\u002Forcid.org\u002F0009-0003-3578-6592",{"name":224,"orcid":225},"Karel Říha","https:\u002F\u002Forcid.org\u002F0000-0002-6124-0118",{"name":227,"orcid":228},"Vivek K. Raxwal","https:\u002F\u002Forcid.org\u002F0000-0002-5182-6377",{"tldr":230,"method":231,"finding":232,"direction":108,"opportunity":233},"开发了花粉分析工具PAT，自动化评估花粉活力，并用于发现新的减数分裂抑制因子。","微调Cellpose-SAM模型，集成到跨平台桌面应用PAT，支持交互式质量控制","PAT高效筛选拟南芥突变体，发现CAP-D2点突变可挽救smg7-6表型，而Condensin II","将AI图像分析工具扩展到其他作物表型，如种子活力、果实发育，结合交互式平台加速遗传筛选。","农业遥感与作物表型","2026-09-05T23:30:19.596351Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":21,"source_name":242,"source_url":239,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":243,"score_detail":244,"sources":247,"tags":249,"primary_topic":254,"secondary_topics":259,"classification":266,"search_phrases":314,"slug":317,"view_count":103,"doi":318,"paper":319,"created_at":347},1725,"Sustainable Wheat Production Through Precision Agriculture: An LCA Case Study in Italy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091646","The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance of soft wheat production during the transition from conventional to smart and precision farming under real farm conditions. A Life Cycle Assessment (LCA) was conducted following ISO 14040 and 14044 standards using primary data collected from a farm located in Umbria (Italy). Two consecutive three-year production periods were compared: conventional farming (2019–2021) and smart and precision farming (2022–2024). Environmental impacts were assessed using the Environmental Footprint 3.1 method. The transition was associated with a 20.8% reduction in the overall environmental impact. This improvement reflects the combined effects of adopting smart and precision agriculture technologies together with the renewal of farm machinery, which results in lower fuel consumption and more efficient fertilizer and pesticide management. Most impact categories showed improvements, particularly climate change, ecotoxicity, and particulate matter. However, freshwater eutrophication increased due to changes in fertilization strategy, including reduced nitrogen fertilizer use and increased phosphorus fertilizer application. Sensitivity analyses confirmed the consistency of the results; despite some influence on yield variation, the overall comparison remained consistent under the tested scenarios. These findings highlight the potential of smart and precision agriculture to improve the environmental sustainability of soft wheat production under real farm conditions.","日益增长的粮食需求给农业系统带来了越来越大的压力，引发了对农业环境可持续性的担忧。在此背景下，精准农业（precision agriculture）代表了一种在保持高产的同时提高资源利用效率的有前景的方法。本研究旨在评估在真实农场条件下，软质小麦（soft wheat）生产从传统农业向智能与精准农业转型过程中的环境绩效。依据ISO 14040和ISO 14044标准，利用从意大利翁布里亚（Umbria）地区一个农场收集的一手数据，进行了生命周期评价（Life Cycle Assessment, LCA）。研究比较了两个连续的三生产周期：传统农业（2019—2021年）和智能与精准农业（2022—2024年）。采用环境足迹3.1方法（Environmental Footprint 3.1）评估环境影响。转型与总体环境影响降低20.8%相关联。这一改善反映了采用智能与精准农业技术以及农机更新的综合效应，从而降低了燃料消耗，并实现了肥料和农药的更高效管理。大多数影响类别均有所改善，尤其是气候变化、生态毒性和颗粒物。然而，淡水富营养化（freshwater eutrophication）有所增加，原因是施肥策略的变化，包括减少氮肥使用和增加磷肥施用。敏感性分析证实了结果的一致性；尽管产量变化对结果有一定影响，但在所测试情景下，总体比较结果保持一致。这些发现凸显了智能与精准农业在真实农场条件下改善软质小麦生产环境可持续性的潜力。","Land",75,{"impact":123,"substance":29,"depth":123,"authority":31,"freshness":245,"relevant":33,"comment":246},4,"基于真实农场数据的LCA研究，量化了精准农业转型的环境效益，对农业可持续发展具有参考价值。",[248],{"name":242,"url":239},[193,250,251,252,253],"小麦","精准农业","生命周期评价","意大利",{"code":255,"slug":256,"name":250,"path":257,"parent_code":258},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[260],{"code":261,"slug":262,"name":263,"path":264,"parent_code":265},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":61,"locked":26,"version":62,"method":63,"evidence":267},{"candidates":268,"agricultural_evidence":68},[269,275,279,288,296,303,309],{"code":208,"score":270,"supported":68,"title":271,"abstract":273,"tags":274},16,[272],"precision agriculture",[251,272],[193,251],{"code":255,"score":76,"supported":68,"title":276,"abstract":277,"tags":278},[256],[250,256],[250],{"code":261,"score":280,"supported":68,"title":281,"abstract":282,"tags":287},9,[],[283,284,285,286],"施肥","肥料","fertilizer","fertilization",[],{"code":289,"score":290,"supported":68,"title":291,"abstract":292,"tags":295},"F4",6,[],[293,294],"农药","pesticide",[],{"code":297,"score":290,"supported":68,"title":298,"abstract":299,"tags":302},"H7",[],[300,301],"气候变化","climate change",[],{"code":304,"score":158,"supported":68,"title":305,"abstract":306,"tags":308},"F5",[],[307],"农机",[],{"code":310,"score":33,"supported":26,"title":311,"abstract":312,"tags":313},"H4",[],[],[252],[315,316],"生命周期评价 智慧农业 精准农业 意大利","生命周期评价 智慧农业","生命周期评价智慧农业精准农业意大利-1725","10.3390\u002Fland15091646",{"doi":318,"openalex_id":320,"authors":321,"venue":242,"cited_by_count":103,"oa_url":239,"card":340,"direction":346,"ingested_from":111},"W7208758947",[322,324,327,330,332,334,337],{"name":323,"orcid":21},"Claudia Camplone",{"name":325,"orcid":326},"Luisa Paolotti","https:\u002F\u002Forcid.org\u002F0000-0002-8264-1674",{"name":328,"orcid":329},"Francesco Romagnoli","https:\u002F\u002Forcid.org\u002F0000-0003-1814-7310",{"name":331,"orcid":21},"Sara Allahverdiveva",{"name":333,"orcid":21},"Renzo Santi",{"name":335,"orcid":336},"Lorenzo Covarelli","https:\u002F\u002Forcid.org\u002F0000-0002-4568-6160",{"name":338,"orcid":339},"Lucia Rocchi","https:\u002F\u002Forcid.org\u002F0000-0002-3386-2390",{"tldr":341,"method":342,"finding":343,"direction":344,"opportunity":345},"通过LCA评估意大利软小麦从传统向精准农业转型的环境影响，发现总影响降低20.8%。","基于农场实地数据，采用LCA（ISO标准）和Environmental Foot","精准农业结合机械更新使环境影响降低20.8%，但磷肥增加导致淡水富营养化上升。","农业绿色发展与碳","可研究精准农业下施肥策略优化，平衡产量与富营养化风险，或扩展至其他作物和地区。","智慧农业 \u002F 农业物联网","2026-09-05T23:30:09.493129Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":21,"source_name":354,"source_url":351,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":243,"score_detail":355,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":392,"slug":395,"view_count":91,"doi":396,"paper":397,"created_at":417},1718,"Prescription-Map-Guided Bi-Level Multi-Objective Path Planning for UAV–UGV Collaborative Spraying and Fertilization in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fdrones10090677","Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty.","变量率农药喷洒与施肥作业要求异构农业机械协同运行，尤其是在不规则农田中，任务需求、车辆机动性、载重能力、能耗及补给需求均存在空间差异。然而，现有研究大多分别优化空中喷洒或地面施肥，未联合考虑处方图需求、空地同步、农药飘移风险及农业车辆约束。本研究将无人机喷洒与无人车施肥的协同作业建模为多目标混合整数非线性规划问题，包含三个优化目标：最小化系统总完工时间、加权能耗及农药飘移惩罚。提出一种处方图引导的双层规划框架。在上层，问题专用求解器TNSAOO确定无人机与无人车的任务序列及协同补给点激活方案；在下层，自适应Theta*算法与行约束Hybrid A*算法分别生成无人机喷洒与无人车施肥轨迹，同时沿有效作业段分配依赖处方图的施药指令，并通过时间窗机制检测和修正残余空地冲突。该框架基于30个真实农田边界和90个随机处方场景进行评估。无人机喷洒与无人车施肥的平均几何覆盖率分别达到98.82%和98.95%，平均处方符合性误差分别为6.21%和2.13%。此外，96.7%的批次运行中检测到的空地冲突不超过一次，平均修正等待时间为1.07秒。与传统独立作业相比，协同规划使平均系统完工时间降低9.32%，加权能耗降低7.21%，模型化飘移惩罚降低3.62%，总路径长度降低5.79%。在多目标对比中，TNSAOO获得的平均超体积（hypervolume）为0.597，平均反转世代距离（inverted generational distance）为0.375，相较于既有对比算法（尤其是NSGA-II）展现出具有竞争力的Pareto搜索性能。额外的地形与飘移敏感性分析表明，在受控参数扰动下，能耗、完工时间及模型化飘移风险均产生系统性变化。上述研究结果验证了在共享处方图下对异构变量率喷洒与施肥进行联合规划的仿真可行性。","Drones",{"impact":123,"substance":29,"depth":67,"authority":31,"freshness":158,"relevant":33,"comment":356},"提出处方图引导的无人机-无人车协同变量喷施与施肥双层规划框架，仿真验证效果显著，对智慧农业装备协同作业有参考价值。",[358],{"name":354,"url":351},[193,360,361,362],"农业机器人","路径规划","变量作业",{"code":261,"slug":262,"name":263,"path":264,"parent_code":265},[],{"status":61,"locked":26,"version":62,"method":63,"evidence":366},{"candidates":367,"agricultural_evidence":68},[368,373,378,382,387],{"code":261,"score":369,"supported":68,"title":370,"abstract":371,"tags":372},17,[286],[283,285,286],[],{"code":208,"score":280,"supported":68,"title":374,"abstract":376,"tags":377},[375],"smart agriculture",[],[193],{"code":289,"score":290,"supported":68,"title":379,"abstract":380,"tags":381},[],[293,294],[],{"code":304,"score":245,"supported":68,"title":383,"abstract":384,"tags":386},[],[385],"农业机械",[361],{"code":388,"score":33,"supported":26,"title":389,"abstract":390,"tags":391},"F6",[],[],[360],[393,394],"农业机器人 变量作业 智慧农业 路径规划","农业机器人 变量作业","农业机器人变量作业智慧农业路径规划-1718","10.3390\u002Fdrones10090677",{"doi":396,"openalex_id":398,"authors":399,"venue":354,"cited_by_count":103,"oa_url":351,"card":412,"direction":346,"ingested_from":111},"W7208727280",[400,403,406,409],{"name":401,"orcid":402},"Shiyang Li","https:\u002F\u002Forcid.org\u002F0000-0003-2675-0419",{"name":404,"orcid":405},"Jisong Lv","https:\u002F\u002Forcid.org\u002F0009-0007-5060-0521",{"name":407,"orcid":408},"Yuchen Lu","https:\u002F\u002Forcid.org\u002F0009-0003-3027-6462",{"name":410,"orcid":411},"Yuxuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8617-0435",{"tldr":413,"method":414,"finding":415,"direction":346,"opportunity":416},"提出处方图引导的无人机-无人车协同变量喷洒施肥双层多目标路径规划方法，实现高效协同作业。","双层框架：上层TNSAOO求解任务序列与补给点，下层自适应Theta*和Hybr","协同规划较独立作业减少9.32%时间、7.21%能耗，覆盖率约99%，处方符合率高，冲突少。","可探索实际田间验证、动态处方图更新、多机协同鲁棒性，以及将方法扩展至其他农业任务。","2026-09-05T23:30:08.846450Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":21,"source_name":424,"source_url":421,"published_at":425,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":243,"score_detail":426,"sources":429,"tags":431,"primary_topic":434,"secondary_topics":439,"classification":440,"search_phrases":461,"slug":464,"view_count":91,"doi":465,"paper":466,"created_at":489},1709,"EOL-DQN: An intelligent cutting decision of potato seed tubers based on improved deep reinforcement learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112372","EOL-DQN: An intelligent cutting decision of potato seed tubers based on improved deep reinforcement learning。Computers and Electronics in Agriculture","EOL-DQN：基于改进深度强化学习的马铃薯种薯智能切块决策。Computers and Electronics in Agriculture","Computers and Electronics in Agriculture","2026-09-05T00:00:00Z",{"impact":76,"substance":30,"depth":123,"authority":187,"freshness":427,"relevant":33,"comment":428},8,"提出基于改进深度强化学习的马铃薯种薯切块决策方法，属于智慧农业细分领域的技术创新，对种业智能化有参考价值。",[430],{"name":424,"url":421},[193,38,432,433,205],"马铃薯","种业",{"code":435,"slug":436,"name":437,"path":438,"parent_code":258},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":441},{"candidates":442,"agricultural_evidence":68},[443,448,452,457],{"code":435,"score":76,"supported":68,"title":444,"abstract":446,"tags":447},[445],"potato",[432,445],[432],{"code":43,"score":91,"supported":26,"title":449,"abstract":450,"tags":451},[],[],[38,205],{"code":453,"score":33,"supported":26,"title":454,"abstract":455,"tags":456},"F1",[],[],[433],{"code":208,"score":33,"supported":26,"title":458,"abstract":459,"tags":460},[],[],[193],[462,463],"农业人工智能 智慧农业 深度学习 马铃薯","农业人工智能 智慧农业","农业人工智能智慧农业深度学习马铃薯-1709","10.1016\u002Fj.compag.2026.112372",{"doi":465,"openalex_id":467,"authors":468,"venue":424,"cited_by_count":103,"oa_url":21,"card":484,"direction":108,"ingested_from":111},"W7208757680",[469,471,473,475,477,479,481],{"name":470,"orcid":21},"Xuyang Wang",{"name":472,"orcid":21},"Yun Liu",{"name":474,"orcid":21},"Tianshuo Su",{"name":476,"orcid":21},"Yulu Sun",{"name":478,"orcid":21},"Binheng Huang",{"name":480,"orcid":21},"Yangqian Zhang",{"name":482,"orcid":483},"Wanzhi Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6196-8037",{"tldr":485,"method":486,"finding":487,"direction":108,"opportunity":488},"提出EOL-DQN改进深度强化学习模型，用于马铃薯种薯切块智能决策。","改进深度强化学习（DQN），结合视觉识别与决策模型。","EOL-DQN能有效提高切块决策的准确性和效率。","可探索将强化学习应用于其他农产品加工或农业机器人操作，优化决策策略。","2026-09-05T23:30:01.927676Z",{"id":491,"title":492,"url":493,"summary":494,"summary_zh":495,"content":21,"source_name":496,"source_url":493,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":497,"score_detail":498,"sources":500,"tags":502,"primary_topic":507,"secondary_topics":512,"classification":513,"search_phrases":537,"slug":540,"view_count":158,"doi":541,"paper":542,"created_at":558},1744,"From Vulnerability to Empowerment: The Interplay of Agri‐Fintech, Governance, and Emotional Intelligence in Indian Sustainable Farming","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsd.71617","ABSTRACT Drawing on the sustainable livelihoods framework, our study investigates the influence of agri‐fintech adoption, geo‐environmental conditions, governance\u002Fvalue chain mechanisms, and emotional intelligence on sustainable crop productivity among 650 rural Indian farmers. Results indicate that agri‐fintech adoption is the strongest predictor of perceived crop productivity outcomes, followed by geo‐environmental conditions, governance and value chain mechanisms, and emotional intelligence, each significantly. Agri‐fintech positively moderates the governance‐crop productivity link. The model accounts for 49.6% of the variance, highlighting fintech's pivotal role in rural empowerment amid climate vulnerabilities. Theoretically, it extends TAM and vulnerability theory by uncovering interdependencies and potential trade‐offs in digital agriculture. In practice, the findings advocate for integrated policies that combine fintech with institutional support and adaptive training to enhance sustainability, food security, and alignment with the SDGs in emerging economies.","基于可持续生计框架，本研究探讨了农业金融科技采纳、地理环境条件、治理\u002F价值链机制以及情绪智力对650名印度农村农民可持续作物生产力的影响。结果表明，农业金融科技采纳是感知作物生产力结果的最强预测因子，其次是地理环境条件、治理与价值链机制以及情绪智力，且各因素均具有显著性。农业金融科技正向调节治理与作物生产力之间的关系。该模型解释了49.6%的方差，凸显了金融科技在气候脆弱性背景下对农村赋权的关键作用。在理论上，本研究通过揭示数字农业中的相互依赖关系及潜在权衡，扩展了技术接受模型（TAM）和脆弱性理论。在实践层面，研究结果倡导将金融科技与制度支持及适应性培训相结合的综合政策，以增强新兴经济体的可持续性、粮食安全，并与可持续发展目标（SDGs）保持一致。","Sustainable Development",74,{"impact":123,"substance":29,"depth":123,"authority":187,"freshness":91,"relevant":33,"comment":499},"研究揭示农业金融科技对印度可持续农业的关键作用，数据详实，对新兴经济体有借鉴意义。",[501],{"name":496,"url":493},[503,504,505,506],"数字农业","可持续农业","农业金融科技","印度农业",{"code":508,"slug":509,"name":510,"path":511,"parent_code":258},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":514},{"candidates":515,"agricultural_evidence":68},[516,521,527,532],{"code":208,"score":188,"supported":68,"title":517,"abstract":518,"tags":520},[],[503,519],"digital agriculture",[503],{"code":508,"score":290,"supported":68,"title":522,"abstract":523,"tags":526},[],[524,525],"粮食安全","food security",[],{"code":528,"score":33,"supported":26,"title":529,"abstract":530,"tags":531},"H8",[],[],[504],{"code":533,"score":33,"supported":26,"title":534,"abstract":535,"tags":536},"J3",[],[],[506],[538,539],"农业金融科技 可持续农业 印度农业 数字农业","农业金融科技 可持续农业","农业金融科技可持续农业印度农业数字农业-1744","10.1002\u002Fsd.71617",{"doi":541,"openalex_id":543,"authors":544,"venue":496,"cited_by_count":103,"oa_url":21,"card":553,"direction":110,"ingested_from":111},"W7208720139",[545,548,550],{"name":546,"orcid":547},"Giridhari Mohanta","https:\u002F\u002Forcid.org\u002F0000-0002-6086-237X",{"name":549,"orcid":21},"Subhendu Patnaik",{"name":551,"orcid":552},"Ashutosh Dash","https:\u002F\u002Forcid.org\u002F0000-0001-6625-5004",{"tldr":554,"method":555,"finding":556,"direction":110,"opportunity":557},"研究印度农民采用农业金融科技、治理、情商对可持续生产力的影响，发现金融科技作用最强。","基于可持续生计框架，对650名印度农民进行问卷调查和统计分析。","农业金融科技采用是作物生产力的最强预测因子，并正向调节治理与生产力的关系。","可探索农业金融科技与治理机制在不同区域或作物类型中的交互作用，以及情商培训对技术采纳的长期影响。","2026-09-05T23:30:35.340989Z",{"id":560,"title":561,"url":562,"summary":563,"summary_zh":564,"content":21,"source_name":565,"source_url":562,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":566,"score_detail":567,"sources":570,"tags":572,"primary_topic":575,"secondary_topics":579,"classification":580,"search_phrases":591,"slug":593,"view_count":91,"doi":594,"paper":595,"created_at":608},1737,"A hawk-inspired metaheuristic for trajectory optimization of UAVs in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.65746\u002Fjbrha139","Unmanned Aerial Vehicles (UAVs) have become central to precision agriculture, where efficient trajectory planning over large fields directly determines mission feasibility, energy use, and the quality of acquired data. We address the problem of generating an optimal, fully covering trajectory for a single UAV operating over a plantation field modeled as a graph of waypoints. The problem is a constrained variant of the Travelling Salesman Problem in which all waypoints must be visited, total path length and turning cost must be minimized, and an energy budget must be respected. Existing metaheuristics such as Particle Swarm Optimization, Grey Wolf Optimizer, and Harris’s Hawks Optimization struggle to balance exploration with the strict coverage constraint of plantation scenarios, and tend to converge prematurely on irregular layouts. We propose the Hawk-Inspired Trajectory Optimizer (HITO), a new population-based metaheuristic whose search behavior is inspired by the soaring, patrolling, and stooping phases of hawks. HITO introduces a thermal-soaring exploration operator, a territorial-patrol transition operator, and a stoop-dive exploitation operator, supported by a coverage-repair mechanism that guarantees feasibility at every iteration. We provide a Markov-chain argument for convergence and an O(N·n²) per-iteration complexity bound, where N is population size and n are the number of waypoints. HITO is benchmarked on simulated plantation fields of 1, 3, and 5 hectares against seven baselines, and on twelve CEC 2017 benchmark functions. Across thirty independent runs, HITO reduces path length by 12.4 % on average over the best baseline, lowers energy consumption by 14.1 %, improves coverage robustness, and converges in fewer generations. Statistical significance is confirmed using Wilcoxon signed-rank and Friedman tests.","无人机（UAV）已成为精准农业的核心工具，在大面积农田上高效的轨迹规划直接决定了任务可行性、能源消耗以及所采集数据的质量。本文针对单一无人机在 plantation 场景（以航点图建模）中生成最优全覆盖轨迹的问题展开研究。该问题属于旅行商问题（TSP）的一种受约束变体，要求访问所有航点，同时最小化总路径长度和转弯成本，并满足能量预算约束。现有元启发式算法，如粒子群优化（PSO）、灰狼优化器（GWO）和哈里斯鹰优化（HHO），在平衡探索能力与 plantation 场景严格覆盖约束方面存在困难，且在不规则布局下容易过早收敛。为此，我们提出了一种新型基于种群的元启发式算法——鹰启发轨迹优化器（HITO），其搜索行为受鹰的翱翔、巡逻和俯冲三个阶段启发。HITO 引入了热气流翱翔探索算子、领地巡逻过渡算子以及俯冲下击开发算子，并辅以覆盖修复机制，确保每次迭代的可行性。我们提供了基于马尔可夫链的收敛性论证，并给出了每迭代复杂度上界 O(N·n²)，其中 N 为种群规模，n 为航点数量。HITO 在模拟的 1、3 和 5 公顷 plantation 农田上，与七种基线算法进行了对比测试，并在十二个 CEC 2017 基准函数上进行了验证。在三十次独立运行中，HITO 相较于最佳基线算法，平均路径长度减少了 12.4%，能耗降低了 14.1%，覆盖鲁棒性得到提升，且收敛所需的迭代次数更少。统计显著性通过 Wilcoxon 符号秩检验和 Friedman 检验得到确认。","Journal of Biological Regulators and Homeostatic Agents\u002FJournal of Biological Regulators & Homeostatic Agents",72,{"impact":123,"substance":29,"depth":123,"authority":568,"freshness":91,"relevant":33,"comment":569},12,"提出新型元启发式算法优化无人机植保轨迹，显著提升效率，对精准农业有实质贡献。",[571],{"name":565,"url":562},[193,573,38,574],"无人机","路径优化",{"code":208,"slug":576,"name":577,"path":578,"parent_code":47},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":581},{"candidates":582,"agricultural_evidence":68},[583,587],{"code":208,"score":76,"supported":68,"title":584,"abstract":585,"tags":586},[272],[251,272],[193],{"code":43,"score":33,"supported":26,"title":588,"abstract":589,"tags":590},[],[],[38],[592,463],"农业人工智能 智慧农业 路径优化 无人机","农业人工智能智慧农业路径优化无人机-1737","10.65746\u002Fjbrha139",{"doi":594,"openalex_id":596,"authors":597,"venue":565,"cited_by_count":103,"oa_url":602,"card":603,"direction":234,"ingested_from":111},"W7208744610",[598,600],{"name":599,"orcid":21},"Gigin Durai Chelladurai",{"name":601,"orcid":21},"Lenin Fred Alfred","https:\u002F\u002Fjournal.csrp-pub.com\u002Findex.php\u002FJBRHA\u002Farticle\u002Fdownload\u002F139\u002F138",{"tldr":604,"method":605,"finding":606,"direction":108,"opportunity":607},"提出鹰启发式元启发算法HITO，优化无人机在精准农业中的全覆盖轨迹，降低路径长度和能耗。","模拟鹰的翱翔、巡逻、俯冲阶段，设计热上升探索、领地巡逻转换、俯冲开发算子及覆盖修","HITO相比最优基线平均路径缩短12.4%，能耗降低14.1%，收敛更快，统计显著。","可探索将HITO扩展到多无人机协同、动态障碍物环境，或结合实时传感器数据实现自适应路径规划。","2026-09-05T23:30:27.619181Z",{"id":610,"title":611,"url":612,"summary":613,"summary_zh":614,"content":21,"source_name":615,"source_url":612,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":616,"score_detail":617,"sources":619,"tags":621,"primary_topic":624,"secondary_topics":625,"classification":626,"search_phrases":641,"slug":644,"view_count":158,"doi":645,"paper":646,"created_at":663},1746,"Digitization in Agriculture and Food Security in Sub-Saharan Africa: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fajbar.v13i1.032","Digitization is increasingly recognized as a transformative force in Sub-Saharan Africa’s (SSA) agricultural sector, with significant implications for productivity enhancement and food security improvement. This systematic review examines the role of digital technologies in agriculture, focusing on their contributions to food availability, accessibility, utilization, and stability across SSA. The study adopts a systematic literature review (SLR) methodology, drawing evidence from peer-reviewed journals and reports published by institutions such as the World Bank, FAO, IFAD, and the African Development Bank between 2015 and 2025. Findings reveal that digital agriculture interventions, including mobile advisory systems, precision agriculture tools, digital financial services, and e-market platforms, significantly improve agricultural productivity, reduce transaction costs, strengthen market integration, and enhance farmers’ access to financial services. However, adoption remains uneven due to infrastructural deficits, digital illiteracy, gender inequality, and weak institutional frameworks. The review concludes that while digitization holds strong potential to transform food systems in SSA, its effectiveness depends on enabling environments such as infrastructure development, inclusive policies, and capacity building. The study recommends strengthening digital infrastructure, improving literacy programs, and expanding public–private partnerships to accelerate inclusive digital transformation in agriculture.","数字化日益被视为撒哈拉以南非洲（SSA）农业部门的变革性力量，对提高生产力和改善粮食安全具有重大意义。本系统综述考察了数字技术在农业中的作用，重点关注其对撒哈拉以南非洲粮食可得性、可及性、利用和稳定性的贡献。研究采用系统文献综述（SLR）方法，借鉴了2015年至2025年间同行评审期刊以及世界银行、联合国粮农组织（FAO）、国际农业发展基金（IFAD）和非洲开发银行等机构发布的报告中的证据。研究结果表明，包括移动咨询系统、精准农业工具、数字金融服务和电子市场平台在内的数字农业干预措施，显著提高了农业生产力，降低了交易成本，加强了市场整合，并增强了农民获取金融服务的机会。然而，由于基础设施不足、数字文盲、性别不平等和制度框架薄弱，数字技术的采用仍不均衡。本综述得出结论：尽管数字化在改变撒哈拉以南非洲粮食系统方面具有巨大潜力，但其有效性取决于有利环境，如基础设施发展、包容性政策和能力建设。研究建议加强数字基础设施建设，改善扫盲计划，并扩大公私伙伴关系，以加速农业领域的包容性数字化转型。","Journal of Biotechnology and Agricultural Research",70,{"impact":123,"substance":30,"depth":123,"authority":568,"freshness":91,"relevant":33,"comment":618},"系统综述非洲数字农业对粮食安全的影响，证据扎实，但时效性低，适合精选。",[620],{"name":615,"url":612},[503,622,524,623],"系统综述","非洲农业",{"code":508,"slug":509,"name":510,"path":511,"parent_code":258},[],{"status":61,"locked":26,"version":62,"method":63,"evidence":627},{"candidates":628,"agricultural_evidence":68},[629,633,637],{"code":508,"score":76,"supported":68,"title":630,"abstract":631,"tags":632},[525],[524,525],[524],{"code":208,"score":25,"supported":68,"title":634,"abstract":635,"tags":636},[],[251,503,272,519],[503],{"code":533,"score":33,"supported":26,"title":638,"abstract":639,"tags":640},[],[],[623],[642,643],"数字农业 粮食安全 系统综述 非洲农业","数字农业 粮食安全","数字农业粮食安全系统综述非洲农业-1746","10.70382\u002Fajbar.v13i1.032",{"doi":645,"openalex_id":647,"authors":648,"venue":615,"cited_by_count":103,"oa_url":612,"card":658,"direction":110,"ingested_from":111},"W7208759473",[649,652,654,656],{"name":650,"orcid":651},"Funso O. Alabuja","https:\u002F\u002Forcid.org\u002F0000-0002-7579-1041",{"name":653,"orcid":21},"Ejiofor D. Chukwubuike",{"name":655,"orcid":21},"Lateefah O. Lama-Abdul",{"name":657,"orcid":21},"Olabiyi Fatimah Adebisi",{"tldr":659,"method":660,"finding":661,"direction":110,"opportunity":662},"系统综述撒哈拉以南非洲农业数字化对粮食安全的影响，发现其提升生产力但采用不均。","系统文献综述，分析2015-2025年同行评审文献及国际机构报告。","数字农业提高生产力、降低交易成本，但受基础设施、数字素养等制约。","可研究数字农业采用不均的驱动因素，如性别差异、制度障碍，探索包容性数字化路径。","2026-09-05T23:30:35.595189Z",{"id":665,"title":666,"url":667,"summary":668,"summary_zh":669,"content":21,"source_name":670,"source_url":667,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":616,"score_detail":671,"sources":673,"tags":675,"primary_topic":677,"secondary_topics":682,"classification":689,"search_phrases":729,"slug":731,"view_count":245,"doi":732,"paper":733,"created_at":746},1724,"Integrating Artificial Intelligence and IoT for Precision Farming: A Review of Agriculture 4.0 Technologies, Applications, and Challenges","https:\u002F\u002Fdoi.org\u002F10.67564\u002Firassjms.v3.i9.0186","Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.","农业4.0标志着从机械化、化学密集型农业向数据驱动、自动化、互联的食品生产模式的决定性转变。这一转变的核心在于人工智能（AI）与物联网（IoT）的融合，二者共同使农场能够以人工方法无法企及的精度感知、解读并响应田间条件。本综述综合了当前关于精准农业中AI-IoT整合的文献体系，涵盖核心使能技术——智能传感器、无人机（UAVs）、地理信息系统（GIS）、边缘与云计算，以及基于区块链的可追溯性——以及它们组合成功能性智慧农业系统的分层架构。基于主要发表于2019年至2025年间的同行评审研究，本综述考察了智能灌溉、作物病害检测、产量预测和供应链可追溯性中的代表性应用，整合了文献中反复出现的障碍，并对十项代表性研究进行了按年份排序的技术、数据集、性能指标及报告局限性的详细比较。综述最后指出了研究空白——特别是围绕经济实惠的边缘AI模型、互操作性数据标准，以及在印度等小农情境下的区域特定验证——这些值得在未来工作中予以关注。","IRASS Journal of Multidisciplinary Studies",{"impact":123,"substance":29,"depth":123,"authority":25,"freshness":91,"relevant":33,"comment":672},"综述AI与IoT在精准农业中的集成应用，涵盖技术、应用与挑战，对行业有参考价值，但时效性较低。",[674],{"name":670,"url":667},[193,38,676,251],"物联网",{"code":678,"slug":679,"name":680,"path":681,"parent_code":47},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[683,684],{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},{"code":685,"slug":686,"name":687,"path":688,"parent_code":47},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":61,"locked":26,"version":62,"method":63,"evidence":690},{"candidates":691,"agricultural_evidence":68},[692,698,702,709,713,720,725],{"code":678,"score":123,"supported":68,"title":693,"abstract":695,"tags":697},[694],"IoT",[676,696,694],"internet of things",[676],{"code":43,"score":568,"supported":68,"title":699,"abstract":700,"tags":701},[70],[70],[38],{"code":685,"score":280,"supported":68,"title":703,"abstract":704,"tags":708},[],[705,706,707],"地理信息","GIS","geographic information",[],{"code":208,"score":427,"supported":68,"title":710,"abstract":711,"tags":712},[],[193,251],[193,251],{"code":714,"score":290,"supported":68,"title":715,"abstract":716,"tags":719},"H3",[],[717,718],"灌溉","irrigation",[],{"code":304,"score":158,"supported":68,"title":721,"abstract":722,"tags":724},[],[723],"机械化",[],{"code":56,"score":158,"supported":68,"title":726,"abstract":727,"tags":728},[],[84],[],[730,463],"农业人工智能 智慧农业 精准农业 物联网","农业人工智能智慧农业精准农业物联网-1724","10.67564\u002Firassjms.v3.i9.0186",{"doi":732,"openalex_id":734,"authors":735,"venue":670,"cited_by_count":103,"oa_url":740,"card":741,"direction":346,"ingested_from":111},"W7208758888",[736,738],{"name":737,"orcid":21},"G Swetha",{"name":739,"orcid":21},"Prof T Anuradha","https:\u002F\u002Firasspublisher.com\u002Fassets\u002Farticles\u002F1788493097.pdf",{"tldr":742,"method":743,"finding":744,"direction":346,"opportunity":745},"综述AI与IoT在精准农业中的集成技术、应用与挑战，提出未来研究方向。","系统综述2019-2025年文献，分析架构、应用案例及十项代表性研究。","AI-IoT集成提升农业效率，但存在成本、互操作性和小农验证等挑战。","研究空白包括低成本边缘AI模型、数据标准互操作及小农场景的区域验证，可探索适应性方案。","2026-09-05T23:30:09.434649Z",{"id":748,"title":749,"url":750,"summary":751,"summary_zh":752,"content":21,"source_name":753,"source_url":750,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":616,"score_detail":754,"sources":756,"tags":758,"primary_topic":764,"secondary_topics":768,"classification":769,"search_phrases":792,"slug":795,"view_count":91,"doi":796,"paper":797,"created_at":816},1715,"Efficiency over expansion: spatial spillovers and mechanism-based drivers of agricultural output in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1877767","Introduction Agricultural productivity remains central to food security and sustainable development, particularly in China, where persistent regional disparities, resource constraints, and input inefficiencies continue to shape production outcomes. This study therefore examines how fertilizer input (AGFU), agricultural mechanization (TPAM), and irrigation efficiency (EFIA) jointly influence agricultural output (AGOV) within a spatially interconnected production system. Methods Grounded in endogenous growth theory and the production function framework, the study addresses key gaps related to spatial dependence, input efficiency, and heterogeneous agricultural structures. Using provincial panel data from 31 Chinese provinces over the period 2005–2023. The Spatial Durbin Model (SDM) is used to estimate both direct and spillover effects. Robustness is ensured through IV, 2SLS, and System GMM estimations to address potential endogeneity. A mediation analysis is also conducted using agricultural patent counts (AGPC) to capture the role of technological diffusion. Results and discussion The results indicate that AGFU, TPAM, and EFIA significantly enhance AGOV, with mechanization (TPAM) showing the most stable and consistent positive effect across specifications. Irrigation efficiency (EFIA) contributes significantly to agricultural output and generates positive spatial spillovers. At the same time, fertilizer input (AGFU) shows strong local effects but relatively weaker or negative spillover effects, suggesting diminishing returns and inefficiencies linked to overuse. Mediation results confirm that AGPC significantly transmits the effects of agricultural inputs to AGOV, highlighting the importance of technology diffusion as a key channel in agricultural productivity. Heterogeneity analysis further shows that the effects of AGFU, TPAM, and EFIA vary significantly across regions, farm operation scales, policy environments, and planting structures, confirming strong structural differences in agricultural production dynamics.","引言 农业生产率始终是粮食安全与可持续发展的核心议题，尤其在中国，区域发展不平衡、资源约束以及投入要素效率低下等问题持续影响着生产结果。为此，本研究考察化肥投入（AGFU）、农业机械化（TPAM）和灌溉效率（EFIA）如何在空间互联的生产系统中共同影响农业产出（AGOV）。方法 基于内生增长理论与生产函数框架，本研究针对空间依赖性、投入效率及农业结构异质性等关键研究空白展开探讨。利用2005—2023年中国31个省份的面板数据，采用空间杜宾模型（SDM）估计直接效应与空间溢出效应。为应对潜在内生性问题，通过工具变量法（IV）、两阶段最小二乘法（2SLS）及系统广义矩估计（System GMM）进行稳健性检验。此外，以农业专利数量（AGPC）为中介变量开展中介效应分析，以捕捉技术扩散的作用。结果与讨论 研究结果显示，AGFU、TPAM和EFIA均显著促进AGOV，其中农业机械化（TPAM）在不同模型设定下表现出最为稳定且一致的正向效应。灌溉效率（EFIA）对农业产出具有显著贡献，并产生正向空间溢出效应。与此同时，化肥投入（AGFU）表现出较强的本地效应，但其溢出效应相对较弱甚至为负，表明化肥过度使用带来的边际收益递减与效率损失。中介效应结果证实，AGPC在农业投入向AGOV传导过程中发挥显著中介作用，凸显技术扩散作为农业生产率提升关键渠道的重要性。异质性分析进一步表明，AGFU、TPAM和EFIA的影响在不同地区、农业经营规模、政策环境及种植结构之间存在显著差异，证实了农业生产动态中存在显著的结构性差异。","Frontiers in Sustainable Food Systems",{"impact":123,"substance":30,"depth":123,"authority":568,"freshness":91,"relevant":33,"comment":755},"基于中国省级面板数据，运用空间杜宾模型揭示农业投入要素的本地效应与空间溢出，为区域农业协调发展提供实证依据。",[757],{"name":753,"url":750},[759,760,761,762,763],"空间溢出","农业机械化","农业生产力","灌溉效率","化肥投入",{"code":304,"slug":765,"name":766,"path":767,"parent_code":265},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":770},{"candidates":771,"agricultural_evidence":68},[772,776,780,784,788],{"code":304,"score":188,"supported":68,"title":773,"abstract":774,"tags":775},[],[385,723],[760],{"code":508,"score":290,"supported":68,"title":777,"abstract":778,"tags":779},[],[524,525],[],{"code":714,"score":290,"supported":68,"title":781,"abstract":782,"tags":783},[],[717,718],[],{"code":261,"score":158,"supported":68,"title":785,"abstract":786,"tags":787},[],[285],[],{"code":685,"score":33,"supported":26,"title":789,"abstract":790,"tags":791},[],[],[759],[793,794],"农业机械化 农业生产力 化肥投入 灌溉效率","农业机械化 农业生产力","农业机械化农业生产力化肥投入灌溉效率-1715","10.3389\u002Ffsufs.2026.1877767",{"doi":796,"openalex_id":798,"authors":799,"venue":753,"cited_by_count":103,"oa_url":810,"card":811,"direction":108,"ingested_from":111},"W7208743754",[800,802,805,808],{"name":801,"orcid":21},"Jiaqi Xiao",{"name":803,"orcid":804},"Kecheng Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-7528-1279",{"name":806,"orcid":807},"Hazrat Hassan","https:\u002F\u002Forcid.org\u002F0000-0003-0297-483X",{"name":809,"orcid":21},"Agyemang K. Sampene","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1877767\u002Fpdf",{"tldr":812,"method":813,"finding":814,"direction":108,"opportunity":815},"研究中国农业产出效率，发现机械化、灌溉效率提升产出，化肥有负溢出，技术扩散是关键。","使用2005-2023年省级面板数据，采用空间杜宾模型、IV\u002F2SLS\u002F系统GM","机械化最稳定提升产出，灌溉有正溢出，化肥负溢出，技术专利中介作用显著。","可探索不同作物种植结构下技术扩散的异质性，或结合遥感数据细化灌溉效率的空间溢出机制。","2026-09-05T23:30:07.590548Z",{"id":818,"title":819,"url":820,"summary":821,"summary_zh":822,"content":21,"source_name":823,"source_url":820,"published_at":23,"category":24,"cover_url":21,"hotness":122,"is_selected":26,"score":824,"score_detail":825,"sources":827,"tags":831,"primary_topic":833,"secondary_topics":834,"classification":835,"search_phrases":858,"slug":861,"view_count":33,"doi":862,"paper":863,"created_at":875},1717,"AI-Driven Smart Agriculture And Food Security In Developing Countries: A Comparative Perspective","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303847","Agriculture remains a cornerstone of economic development and food security in many developing countries. However, the sector continues to face challenges such as climate change, declining natural resources, labour shortages, and increasing production costs. Recent advances in artificial intelligence (AI) and smart agriculture provide new opportunities to improve agricultural productivity, optimize resource use, and strengthen food security. This study examines the role of AI-driven smart agriculture in enhancing food security through a comparative analysis of India, China, Brazil, Vietnam, Kenya, and Indonesia. The findings reveal considerable differences in AI readiness and the adoption of smart agricultural technologies among the countries. Nations with stronger digital infrastructure and higher AI readiness demonstrate greater adoption of precision farming practices and better food security outcomes. The study concludes that expanding digital infrastructure, improving farmers' access to AI-based technologies, strengthening technical capacity, and encouraging collaboration among governments, research institutions, and agritech firms are essential for achieving sustainable agricultural development and improving food security in developing countries. The novelty of this study lies in integrating AI readiness, digital infrastructure, precision farming adoption, and food security into a comparative analysis of six developing countries using 2024 secondary data.","农业仍然是许多发展中国家经济发展和粮食安全的基石。然而，该部门持续面临气候变化、自然资源衰退、劳动力短缺以及生产成本上升等挑战。人工智能（AI）和智慧农业的最新进展为提高农业生产率、优化资源利用以及增强粮食安全提供了新的机遇。本研究通过对印度、中国、巴西、越南、肯尼亚和印度尼西亚的比较分析，探讨了AI驱动的智慧农业在提升粮食安全方面的作用。研究结果显示，各国在AI就绪度和智慧农业技术采用方面存在显著差异。数字基础设施更完善、AI就绪度更高的国家，在精准农业实践的采用率和粮食安全成果方面表现更优。研究得出结论：扩大数字基础设施、提升农民获取AI技术的机会、加强技术能力，并鼓励政府、研究机构和农业科技企业之间的合作，对于实现发展中国家的可持续农业发展及改善粮食安全至关重要。本研究的创新之处在于，利用2024年二手数据，将AI就绪度、数字基础设施、精准农业采用率及粮食安全整合到对六个发展中国家的比较分析中。","International Journal for Research Trends in Social Science & Humanities.",69,{"impact":123,"substance":30,"depth":123,"authority":25,"freshness":158,"relevant":33,"comment":826},"跨国比较研究，提供AI农业应用与粮食安全的新数据，但时效性较低。",[828,829],{"name":823,"url":820},{"name":823,"url":830},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22303846",[193,38,524,832],"发展中国家",{"code":508,"slug":509,"name":510,"path":511,"parent_code":258},[],{"status":61,"locked":26,"version":62,"method":63,"evidence":836},{"candidates":837,"agricultural_evidence":68},[838,842,846,850,854],{"code":208,"score":123,"supported":68,"title":839,"abstract":840,"tags":841},[375],[193,251,375],[193],{"code":508,"score":76,"supported":68,"title":843,"abstract":844,"tags":845},[525],[524,525],[524],{"code":297,"score":290,"supported":68,"title":847,"abstract":848,"tags":849},[],[300,301],[],{"code":43,"score":245,"supported":68,"title":851,"abstract":852,"tags":853},[],[70],[38],{"code":528,"score":158,"supported":68,"title":855,"abstract":856,"tags":857},[],[504],[],[859,860],"农业人工智能 发展中国家 智慧农业 粮食安全","农业人工智能 发展中国家","农业人工智能发展中国家智慧农业粮食安全-1717","10.5281\u002Fzenodo.22303847",{"doi":862,"openalex_id":864,"authors":865,"venue":823,"cited_by_count":103,"oa_url":820,"card":870,"direction":346,"ingested_from":111},"W7208717815",[866,868],{"name":867,"orcid":21},"Dr.V. KALEESWARI",{"name":869,"orcid":21},"Dr. P. Anita",{"tldr":871,"method":872,"finding":873,"direction":108,"opportunity":874},"比较六国AI驱动智慧农业对粮食安全的影响，发现数字基础设施和AI准备度是关键。","基于2024年二手数据，对印度、中国等六国进行AI准备度、数字基础设施与精准农业","数字基础设施强、AI准备度高的国家，精准农业采纳多，粮食安全结果更好。","可深入研究发展中国家数字基础设施与AI准备度对精准农业采纳的因果机制，或构建评估框架指导政策。","2026-09-05T23:30:08.783371Z",{"id":877,"title":878,"url":879,"summary":880,"summary_zh":881,"content":21,"source_name":753,"source_url":879,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":882,"score_detail":883,"sources":885,"tags":887,"primary_topic":892,"secondary_topics":898,"classification":899,"search_phrases":916,"slug":919,"view_count":33,"doi":920,"paper":921,"created_at":944},1714,"A simplified compartmental model for assessing nitrogen flows and losses in dairy effluent management systems: evidence from intensive farms in the Po Valley (Italy)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1926333","Introduction Nitrogen management in dairy production systems is a major determinant of environmental performance and nutrient use efficiency, as substantial nitrogen losses can occur along the manure management chain. Although life cycle assessment and nutrient balance approaches are commonly used to estimate emissions, they often provide limited insight into internal nitrogen transformations and provide restricted support for farm-scale decision making. This study aimed to develop a simplified compartmental model to quantify nitrogen flows and losses across the main components of intensive dairy farms. Methods The model combines process-based relationships with a limited set of farm-specific input data, including herd composition, feeding strategy, housing systems, and manure management practices. Nitrogen dynamics are represented through interconnected compartments describing the animal, barn, treatment, storage, and land application phases, enabling the identification of key stages contributing to gaseous nitrogen losses. The model was applied to ten commercial Holstein dairy farms located in the Po Valley (Northern Italy). Results Total gaseous nitrogen losses averaged 7.20 ± 6.43% of nitrogen intake in untreated systems, with ammonia volatilization representing the dominant loss pathway. Farms adopting anaerobic digestion showed increased nitrogen retention in digestate, despite higher overall losses associated with subsequent handling stages. A preliminary comparison between simulated and measured nitrogen concentrations in selected manure and digestate matrices indicated a generally consistent representation of nitrogen partitioning, although systematic overestimations were observed for liquid matrices during the early management stages. Discussion Given the limited number of monitored farms and the absence of a comprehensive quantitative validation, the results should be interpreted as indicative of nitrogen flow patterns rather than precise emission estimates. Nevertheless, the proposed framework provides a transparent and data-efficient tool for exploring farm-specific nitrogen balances, supporting scenario analyses and nutrient management strategies, and contributing to the transition toward more circular and environmentally efficient dairy production systems.","引言 奶牛生产系统中的氮管理是决定环境绩效和养分利用效率的关键因素，因为在粪污管理链条中可能发生大量氮损失。尽管生命周期评价和养分平衡方法常用于估算排放量，但它们往往对系统内部氮转化过程的揭示有限，对牧场尺度的决策支持也较为受限。本研究旨在开发一个简化的分室模型，以量化集约化奶牛场主要组成部分中的氮流动与损失。方法 该模型将基于过程的机理关系与有限的牧场特定输入数据相结合，包括畜群结构、饲喂策略、圈舍系统及粪污管理措施。氮动态通过描述动物、牛舍、处理、储存和农田施用等阶段的相互关联分室进行表征，从而识别导致气态氮损失的关键环节。该模型应用于位于波河平原（意大利北部）的十个商业荷斯坦奶牛场。结果 在未处理系统中，总气态氮损失平均占氮摄入量的7.20±6.43%，其中氨挥发是主要的损失途径。采用厌氧消化的牧场在消化液中表现出更高的氮保留率，尽管后续处理环节相关的总损失有所增加。对选定粪污和消化液基质中模拟氮浓度与实测氮浓度的初步比较表明，模型对氮分配的模拟总体一致，但在管理早期阶段对液态基质存在系统性高估。讨论 鉴于监测牧场数量有限且缺乏全面的定量验证，研究结果应被解读为氮流动模式的指示性参考，而非精确的排放估算值。尽管如此，所提出的框架为探索牧场特定氮平衡提供了一种透明且数据高效的工​​具，可支持情景分析和养分管理策略制定，并有助于推动奶牛生产系统向更加循环和环境高效的方向转型。",68,{"impact":568,"substance":30,"depth":123,"authority":31,"freshness":32,"relevant":33,"comment":884},"论文提出简化分区模型量化奶牛场氮流与损失，为农场氮管理提供决策工具，具行业参考价值。",[886],{"name":753,"url":879},[888,889,890,891],"氮管理","循环农业","农业环保","奶牛养殖",{"code":893,"slug":894,"name":895,"path":896,"parent_code":897},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":61,"locked":26,"version":62,"method":63,"evidence":900},{"candidates":901,"agricultural_evidence":68},[902,907,912],{"code":893,"score":245,"supported":68,"title":903,"abstract":904,"tags":906},[],[905],"奶牛",[891],{"code":261,"score":158,"supported":68,"title":908,"abstract":909,"tags":911},[],[910],"养分管理",[],{"code":528,"score":33,"supported":26,"title":913,"abstract":914,"tags":915},[],[],[889],[917,918],"农业环保 奶牛养殖 循环农业 氮管理","农业环保 奶牛养殖","农业环保奶牛养殖循环农业氮管理-1714","10.3389\u002Ffsufs.2026.1926333",{"doi":920,"openalex_id":922,"authors":923,"venue":753,"cited_by_count":103,"oa_url":938,"card":939,"direction":344,"ingested_from":111},"W7208709391",[924,927,929,932,935],{"name":925,"orcid":926},"Giulia Ferronato","https:\u002F\u002Forcid.org\u002F0000-0001-9153-8540",{"name":928,"orcid":21},"Anna Simonetto",{"name":930,"orcid":931},"V. Caprarulo","https:\u002F\u002Forcid.org\u002F0000-0003-3691-4608",{"name":933,"orcid":934},"Elena Scaglia","https:\u002F\u002Forcid.org\u002F0000-0002-4225-5234",{"name":936,"orcid":937},"Gianni Gilioli","https:\u002F\u002Forcid.org\u002F0000-0001-7672-2577","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1926333\u002Fpdf",{"tldr":940,"method":941,"finding":942,"direction":344,"opportunity":943},"开发简化分区模型量化奶牛场氮流与损失，应用于意大利波河河谷集约化农场。","结合过程关系与农场数据，构建动物、牛舍等分区模型，应用于10个荷斯坦奶牛场。","未处理系统气态氮损失占氮摄入7.2%，氨挥发为主；厌氧消化增加消化物氮保留。","模型需全面验证，可扩展至其他地区或加入碳氮耦合，优化管理决策。","2026-09-05T23:30:07.545453Z",{"id":946,"title":947,"url":948,"summary":949,"summary_zh":950,"content":21,"source_name":951,"source_url":948,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":952,"score_detail":953,"sources":955,"tags":957,"primary_topic":961,"secondary_topics":966,"classification":967,"search_phrases":1003,"slug":1006,"view_count":158,"doi":1007,"paper":1008,"created_at":1027},1751,"Downscaling SMAP Soil Moisture to 1 km with Machine Learning and MODIS Data for Agricultural Drought Assessment in Békés County, Hungary","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8090373","Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R2 = 0.71, RMSE = 0.0295 m3\u002Fm3) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling.","土壤水分（SM）的精确制图对于有效监测农业干旱至关重要。然而，被动微波产品（包括9公里分辨率的土壤水分主动被动（SMAP）反演数据）空间分辨率较粗，限制了其在区域和地方尺度上的有效性。为解决这一局限，本研究比较了三种基于机器学习的降尺度框架，旨在将匈牙利贝凯什县的SMAP土壤水分分辨率从9公里提升至1公里。研究时段覆盖2020年至2023年的生长季（4月至10月）。研究中将一组多时相MODIS衍生变量——包括植被指数（NDVI、EVI）、白天和夜间地表温度、蒸散量——以及土地覆盖分类和地形高程作为辅助预测变量。三种机器学习算法，即随机森林（RF）、极限梯度提升（XGBoost）和梯度提升机（GBM），被训练和评估。结果表明：（1）RF模型在测试阶段（R² = 0.71，RMSE = 0.0295 m³\u002Fm³）精度最高，且经四个原位监测站验证，确认其在小尺度上具有可靠的土壤水分估算能力；（2）白天地表温度是所有模型中最重要的预测变量，凸显了控制地表土壤水分动态的强烈热-湿耦合关系；（3）经过验证的RF模型生成的1公里标准化土壤水分指数（SSI）图有效捕捉了年际干旱变率，识别出2022年7月的严重干旱事件。总体而言，本研究提出了一种适用于中欧农业环境的降尺度方法，用于生成高分辨率土壤水分数据。所得到的1公里土壤水分和SSI产品为决策者在干旱期加强规划、通过改进灌溉调度减少农业损失提供了有价值的工具。","AgriEngineering",66,{"impact":568,"substance":30,"depth":123,"authority":568,"freshness":245,"relevant":33,"comment":954},"研究利用机器学习将SMAP土壤水分降尺度至1km，提升农业干旱监测精度，方法新颖，数据详实，对区域农业管理有参考价值。",[956],{"name":951,"url":948},[193,958,72,959,960],"农业遥感","干旱监测","土壤水分",{"code":962,"slug":963,"name":964,"path":965,"parent_code":21},"H","environment","资源环境与气候变化","\u002Ftopic\u002Fenvironment",[],{"status":968,"locked":26,"version":62,"method":63,"evidence":969},"partial",{"candidates":970,"agricultural_evidence":68,"reason":1002},[971,978,984,988,993,997],{"code":972,"score":76,"supported":68,"title":973,"abstract":975,"tags":977},"H2",[974],"soil",[976,974],"土壤",[960],{"code":297,"score":76,"supported":68,"title":979,"abstract":981,"tags":983},[980],"drought",[982,980],"干旱",[959],{"code":43,"score":76,"supported":68,"title":985,"abstract":986,"tags":987},[73],[72,73],[72],{"code":714,"score":280,"supported":68,"title":989,"abstract":990,"tags":992},[],[717,718,991],"evapotranspiration",[],{"code":208,"score":33,"supported":26,"title":994,"abstract":995,"tags":996},[],[],[193],{"code":998,"score":33,"supported":26,"title":999,"abstract":1000,"tags":1001},"I3",[],[],[958],"一级领域明确，二级方向需要细分",[1004,1005],"农业遥感 土壤水分 干旱监测 智慧农业","农业遥感 土壤水分","农业遥感土壤水分干旱监测智慧农业-1751","10.3390\u002Fagriengineering8090373",{"doi":1007,"openalex_id":1009,"authors":1010,"venue":951,"cited_by_count":103,"oa_url":948,"card":1022,"direction":108,"ingested_from":111},"W7208719644",[1011,1013,1016,1019],{"name":1012,"orcid":21},"Mahrokh Shafiei",{"name":1014,"orcid":1015},"István Waltner","https:\u002F\u002Forcid.org\u002F0000-0002-6704-8936",{"name":1017,"orcid":1018},"Z. Vekerdy","https:\u002F\u002Forcid.org\u002F0000-0002-5677-8298",{"name":1020,"orcid":1021},"Gábor Halupka","https:\u002F\u002Forcid.org\u002F0000-0003-4640-0061",{"tldr":1023,"method":1024,"finding":1025,"direction":234,"opportunity":1026},"用机器学习将SMAP土壤水分降尺度至1km，评估匈牙利农业干旱。","比较RF、XGBoost、GBM，结合MODIS植被指数、地表温度等预测变量。","RF精度最高，白天LST最重要，成功捕捉2022年7月严重干旱。","可探索将降尺度方法应用于其他区域或作物，结合更高分辨率遥感或气象数据提升精度。","2026-09-05T23:30:50.054057Z",{"id":1029,"title":1030,"url":1031,"summary":1032,"summary_zh":1033,"content":21,"source_name":1034,"source_url":1031,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":952,"score_detail":1035,"sources":1037,"tags":1039,"primary_topic":21,"secondary_topics":1043,"classification":1044,"search_phrases":1053,"slug":1056,"view_count":33,"doi":1057,"paper":1058,"created_at":1085},1749,"LED lighting as a solution for turf shading: insights from a systemic assessment of Seashore paspalum and Zoysia japonica","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1936506","Introduction The roof structures of professional enclosed stadiums often cause shading problems on the turf, posing a major challenge to maintaining turf quality and efficient management. Artificial lighting has been applied to address this issue, particularly light-emitting diode (LED). Methods To evaluate the efficacy of LED with a specific wavelength in alleviating the adverse effects of shade stress on turfgrasses, we conducted a systematic and multi-dimensional assessment of Seashore paspalum ( Paspalum vaginatum Sw.) and Zoysia japonica ( Zoysia japonica Steud.) turf under three light conditions: natural lighting, shading, and LED, within a stadium. Results The results demonstrated that LED effectively alleviated shade-induced light limitation. It maintained turf normalized difference vegetation index (NDVI) and chlorophyll content at levels comparable to those under natural light. Furthermore, LED treatment improved physiological responses associated with shade stress, as indicated by enhanced cell membrane stability, reduced membrane lipid peroxidation, and altered antioxidant-related responses, including increased superoxide dismutase (SOD) activity and dynamic changes in peroxidase (POD) activity. Regarding root morphology, LED treatment reduced the shade-induced increases in specific root length (SRL) and specific root surface area (SRA), suggesting a modification of shade-induced root morphological responses. Principal component analysis (PCA) revealed distinct multivariate response patterns associated with different light environments. Correlation analysis further indicated significant associations among NDVI, chlorophyll content, and antioxidant-related traits across different light environments. These associations suggested that turf performance under LED lighting was related to concurrent variation in chlorophyll content and antioxidant-related traits. Discussion In conclusion, applying LED lighting can effectively improve turf recovery-related traits and physiological status through improvements in canopy characteristics, chlorophyll status, antioxidant-related responses, and root morphological traits. This study demonstrated under a real stadium environment that specific LED lighting can serve as an effective supplemental light source for improving turf performance in shaded areas, providing practical guidance for stadium turf management.","引言 专业封闭式体育场的屋顶结构常导致草坪遮荫问题，对维持草坪质量和高效管理构成重大挑战。人工照明已被应用于解决这一问题，尤其是发光二极管（LED）。方法 为评估特定波长的LED在缓解遮荫胁迫对草坪草不利影响方面的效果，我们在体育场内三种光照条件下——自然光照、遮荫和LED——对海滨雀稗（Paspalum vaginatum Sw.）和结缕草（Zoysia japonica Steud.）草坪进行了系统且多维度的评估。结果 结果表明，LED有效缓解了遮荫引起的光照限制。它使草坪归一化植被指数（NDVI）和叶绿素含量维持在接近自然光照条件下的水平。此外，LED处理改善了与遮荫胁迫相关的生理响应，表现为细胞膜稳定性增强、膜脂过氧化降低以及抗氧化相关响应的改变，包括超氧化物歧化酶（SOD）活性升高和过氧化物酶（POD）活性的动态变化。在根系形态方面，LED处理减少了遮荫引起的比根长（SRL）和比根表面积（SRA）的增加，表明其对遮荫诱导的根系形态响应具有调节作用。主成分分析（PCA）揭示了不同光照环境下不同的多变量响应模式。相关性分析进一步表明，在不同光照环境下，NDVI、叶绿素含量和抗氧化相关性状之间存在显著关联。这些关联表明，LED光照下草坪的表现与叶绿素含量和抗氧化相关性状的同步变化有关。讨论 总之，应用LED照明可通过改善冠层特征、叶绿素状态、抗氧化相关响应和根系形态性状，有效提升草坪恢复相关性状和生理状态。本研究在真实体育场环境下证明，特定LED照明可作为遮荫区域改善草坪表现的有效的补充光源，为体育场草坪管理提供了实践指导。","Frontiers in Plant Science",{"impact":568,"substance":123,"depth":123,"authority":31,"freshness":32,"relevant":33,"comment":1036},"针对体育场草坪遮阴问题，系统评估LED补光对两种暖季型草坪的恢复效果，为设施草坪管理提供科学依据。",[1038],{"name":1034,"url":1031},[193,1040,1041,1042],"设施农业","草坪管理","LED补光",[],{"status":1045,"locked":26,"version":62,"method":63,"evidence":1046},"needs_review",{"candidates":1047,"agricultural_evidence":26,"reason":1052},[1048],{"code":208,"score":33,"supported":26,"title":1049,"abstract":1050,"tags":1051},[],[],[193],"农业相关性需要复核",[1054,1055],"智慧农业 草坪管理 设施农业 LED补光","智慧农业 草坪管理","智慧农业草坪管理设施农业LED补光-1749","10.3389\u002Ffpls.2026.1936506",{"doi":1057,"openalex_id":1059,"authors":1060,"venue":1034,"cited_by_count":103,"oa_url":1079,"card":1080,"direction":108,"ingested_from":111},"W7208726798",[1061,1063,1065,1067,1069,1072,1074,1077],{"name":1062,"orcid":21},"Weihao Dou",{"name":1064,"orcid":21},"Wan Geng",{"name":1066,"orcid":21},"Weisen Sun",{"name":1068,"orcid":21},"Jie Xu",{"name":1070,"orcid":1071},"Wenchao Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8803-224X",{"name":1073,"orcid":21},"Guilong Song",{"name":1075,"orcid":1076},"Yufeng Chen","https:\u002F\u002Forcid.org\u002F0000-0003-2384-0777",{"name":1078,"orcid":21},"Liebao Han","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1936506\u002Fpdf",{"tldr":1081,"method":1082,"finding":1083,"direction":346,"opportunity":1084},"研究LED光照对体育场遮荫草坪的缓解效果，评估两种草坪草的多维响应。","在真实体育场设置自然光、遮荫、LED三种光照，系统评估草坪生理和形态指标。","LED光照有效缓解遮荫胁迫，维持NDVI和叶绿素，改善抗氧化和根系形态。","可探索LED光配方优化及在多种草坪草和不同遮荫程度下的应用，结合智能调控实现精准补光。","2026-09-05T23:30:45.430586Z"]