[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3272":3,"related-3272":55},{"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":28,"search_phrases":33,"slug":36,"view_count":37,"doi":38,"paper":39,"created_at":54},3272,"Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891268","Smart Agriculture in Northern Nigeria: Prospects and Challenges for Graduate Farmers。Zenodo (CERN European Organization for Nuclear Research)","尼日利亚北部智慧农业：研究生农民的前景与挑战。Zenodo（CERN欧洲核子研究组织）",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z","论文",25,false,45,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,6,10,12,9,1,"主题相关但为尼日利亚区域研究，缺乏新数据与实质结论，仅具参考价值，不宜进入每日精选。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22891267",[29,30,31,32],"数字农业","智慧农业","尼日利亚","农业人才",[34,35],"尼日利亚 智慧农业 毕业生","农业人才 尼日利亚 数字农业 智慧农业","尼日利亚智慧农业毕业生-3272",0,"10.5281\u002Fzenodo.22891268",{"doi":38,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":37,"oa_url":6,"card":46,"direction":52,"ingested_from":53},"W7214014972",[42,44],{"name":43,"orcid":9},"Baman Abubakar",{"name":45,"orcid":9},"A.A Akinsoru",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"调查尼日利亚北部毕业农户对智慧农业的前景认知与实际挑战。","针对毕业农户的问卷调查与定性分析。","毕业农户认可智慧农业潜力，但受基础设施、资金与技能制约。","数字乡村与农业信息化","可延伸至小农户数字素养培训与低成本智慧农业适配方案研究。","智慧农业 \u002F 农业物联网","openalex","2026-09-23T23:30:09.435022Z",{"total":18,"page":22,"page_size":18,"items":56},[57,97,174,226,248,285],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":11,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":76,"slug":79,"view_count":37,"doi":80,"paper":81,"created_at":96},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org",67,{"impact":66,"substance":67,"depth":68,"authority":18,"freshness":17,"relevant":22,"comment":69},16,20,17,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[71],{"name":63,"url":60},[29,30,73,74,75],"农业人工智能","巴西农业","农村数字化",[77,78],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":80,"openalex_id":82,"authors":83,"venue":63,"cited_by_count":37,"oa_url":60,"card":91,"direction":50,"ingested_from":53},"W7214109425",[84,87,89],{"name":85,"orcid":86},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":88,"orcid":9},"Eduardo Dias",{"name":90,"orcid":9},"Lidia Scoton",{"tldr":92,"method":93,"finding":94,"direction":50,"opportunity":95},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"search_phrases":118,"slug":121,"view_count":37,"doi":122,"paper":123,"created_at":173},3351,"Integrated Land, Soil and Crop Information Systems in Ethiopia, Kenya, and Rwanda: Institutional Readiness and Implications for Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101776","Integrated land–soil–crop information systems are increasingly important for supporting climate-smart agricultural planning and implementation, yet their development in Eastern Africa is constrained by fragmented mandates, weak technical capacity, and limited data interoperability. This study assesses how institutional readiness, user demand, and technical and human-capacity conditions shape the integration of such systems into national Agricultural Knowledge and Innovation Systems (AKIS). A mixed-methods, multi-country assessment was conducted in Ethiopia, Kenya, and Rwanda (2022–2024), drawing on 145 semi-structured key-informant interviews, stakeholder mapping, national workshops, and a regional synthesis consultation. The analysis addressed three objectives: (i) diagnose institutional readiness and user demand; (ii) assess technical, infrastructural, and human-capacity requirements; and (iii) identify governance and design principles for embedding integrated information systems within AKIS and Climate-Smart Agriculture (CSA) strategies. The results show consistently high demand for spatially explicit soil and crop data but persistent fragmentation of mandates, uneven coordination, and substantial subnational capacity gaps. Despite these constraints, emerging digital-agriculture strategies, open-data policies, and regional soil-health initiatives provide potential entry points for integration. The study offers a comparative evidence base and design principles—covering governance, interoperability standards, co-production processes, and capacity strengthening—needed to transition from project-driven fragmentation toward interoperable and sustainable information systems. These findings provide diagnostic and design-oriented insights for national and regional efforts to strengthen agricultural information systems that support CSA implementation in Eastern Africa.","综合的土地—土壤—作物信息系统在支持气候智慧型农业规划与实施方面日益重要，但其在东非的发展受到职责分散、技术能力薄弱和数据互操作性有限的制约。本研究评估了制度准备度、用户需求以及技术和人力能力条件如何影响此类系统融入国家农业知识与创新系统（AKIS）。研究于2022—2024年在埃塞俄比亚、肯尼亚和卢旺达开展了混合方法、多国评估，基于145次半结构化关键知情人访谈、利益相关方映射、国家研讨会以及一次区域综合磋商。分析围绕三个目标展开：（i）诊断制度准备度和用户需求；（ii）评估技术、基础设施和人力能力需求；（iii）确定将综合信息系统嵌入AKIS和气候智慧型农业（CSA）战略的治理与设计原则。结果表明，对空间显式土壤和作物数据的需求持续较高，但职责分散、协调不均衡以及地方层面能力差距显著等问题长期存在。尽管存在这些制约，新兴的数字农业战略、开放数据政策和区域土壤健康倡议为整合提供了潜在切入点。本研究提供了比较性证据基础和设计原则——涵盖治理、互操作性标准、共同生产过程和能力建设——这些是从项目驱动的碎片化转向可互操作且可持续的信息系统所必需的。这些发现为国家和区域层面加强支持东非CSA实施的农业信息系统提供了诊断性和设计导向的见解。","Land","2026-09-23T00:00:00Z",81,{"impact":107,"substance":108,"depth":107,"authority":109,"freshness":21,"relevant":22,"comment":110},18,22,14,"基于三国145位关键知情人访谈的混合方法研究，为东非农业信息系统整合与气候智慧农业提供治理与设计原则，方法扎实、结论可靠，具区域政策参考价值。",[112],{"name":103,"url":100},[29,30,114,115,116,117],"土壤健康","气候智慧农业","农业数据","数据互操作",[119,120],"埃塞俄比亚 肯尼亚 卢旺达 农业信息系统","气候智慧农业 土壤作物数据","埃塞俄比亚肯尼亚卢旺达农业信息系统-3351","10.3390\u002Fland15101776",{"doi":122,"openalex_id":124,"authors":125,"venue":103,"cited_by_count":37,"oa_url":100,"card":168,"direction":52,"ingested_from":53},"W7214079859",[126,129,132,134,136,139,142,145,147,150,153,156,159,162,165],{"name":127,"orcid":128},"John Walker Recha","https:\u002F\u002Forcid.org\u002F0000-0002-1146-7197",{"name":130,"orcid":131},"A. Kooiman","https:\u002F\u002Forcid.org\u002F0000-0001-8208-6781",{"name":133,"orcid":9},"Thaïsa van der Woude",{"name":135,"orcid":9},"Hanneke Heesmans",{"name":137,"orcid":138},"Ermias Aynekulu","https:\u002F\u002Forcid.org\u002F0000-0002-1955-6995",{"name":140,"orcid":141},"Angela Nduta Gitau","https:\u002F\u002Forcid.org\u002F0000-0002-8963-2375",{"name":143,"orcid":144},"Pascal Debons","https:\u002F\u002Forcid.org\u002F0000-0001-6314-9935",{"name":146,"orcid":9},"Frank van Weert",{"name":148,"orcid":149},"Michael Okoti","https:\u002F\u002Forcid.org\u002F0000-0002-9550-8258",{"name":151,"orcid":152},"Elizabeth A. Okwuosa","https:\u002F\u002Forcid.org\u002F0000-0001-5941-7423",{"name":154,"orcid":155},"Kennedy Were","https:\u002F\u002Forcid.org\u002F0000-0002-8012-6812",{"name":157,"orcid":158},"Girma Mamo Diga","https:\u002F\u002Forcid.org\u002F0000-0002-2593-3187",{"name":160,"orcid":161},"Dejene Abera","https:\u002F\u002Forcid.org\u002F0000-0003-3692-8620",{"name":163,"orcid":164},"Pierre Celestin Ndayisaba","https:\u002F\u002Forcid.org\u002F0000-0002-8400-9146",{"name":166,"orcid":167},"Jules Rutebuka","https:\u002F\u002Forcid.org\u002F0000-0002-5236-3503",{"tldr":169,"method":170,"finding":171,"direction":50,"opportunity":172},"评估埃塞俄比亚、肯尼亚和卢旺达三国土地-土壤-作物综合信息系统的机构准备度与整合路径。","2022-2024年三国混合方法评估，含145个关键知情人访谈、利益相关方映射与","三国对空间化土壤作物数据需求高，但机构职责碎片化、协调不均、地方能力缺口大。","可研究开放数据政策与区域土壤健康倡议如何作为切入点，推动跨部门互操作标准与联合生产能力建设。","2026-09-24T23:30:10.120978Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":182,"score_detail":183,"sources":185,"tags":187,"search_phrases":190,"slug":193,"view_count":37,"doi":194,"paper":195,"created_at":225},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology","2026-09-24T00:00:00Z",62,{"impact":17,"substance":107,"depth":66,"authority":19,"freshness":19,"relevant":22,"comment":184},"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[186],{"name":180,"url":177},[29,30,73,188,189],"农业物联网","畜牧养殖",[191,192],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":194,"openalex_id":196,"authors":197,"venue":180,"cited_by_count":37,"oa_url":177,"card":220,"direction":52,"ingested_from":53},"W7214075234",[198,200,202,204,206,208,211,214,216,218],{"name":199,"orcid":9},"Nicholas Renaldo",{"name":201,"orcid":9},"Sulaiman Musa",{"name":203,"orcid":9},"Jaswar Koto",{"name":205,"orcid":9},"Kristy Veronica",{"name":207,"orcid":9},"Umar Faruq",{"name":209,"orcid":210},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":212,"orcid":213},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":215,"orcid":9},"Wilda Susanti",{"name":217,"orcid":9},"Achmad Tavip Junaedi",{"name":219,"orcid":9},"Nabila Wahid",{"tldr":221,"method":222,"finding":223,"direction":52,"opportunity":224},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":9,"content":9,"source_name":231,"source_url":9,"published_at":232,"category":233,"cover_url":9,"hotness":19,"is_selected":14,"score":234,"score_detail":235,"sources":237,"tags":239,"search_phrases":243,"slug":246,"view_count":37,"doi":9,"paper":9,"created_at":247},3301,"莲都区107个水稻新品种集中亮相——2026年第四届浙西南水稻新品种数字化展示现场观摩会","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7688050911050007074","9月21日，2026年第四届浙西南水稻新品种数字化展示现场观摩会在莲都区碧湖镇白口村举行。参会嘉宾实地查看107个水稻新品种的长势特点、田间管理和产量等情况。今年观摩会所在的国家级分子育种创新服务平台(长三角)分中心片区，由华智种谷智创科技(浙江)有限公司提供技术支撑，构建了BT生物技术+DT大数据技术的技术体系：运用水稻液相芯片对参试品种开展功能基因图谱鉴定，依托DT大数据技术搭建数字化品种展示评价系统。农业专家团队综合考量茎秆粗壮度、穗粒结构、综合抗性等多项指标，推介出春丰优7号、春优83、春诚优887等15个品种。","今日头条（莲都发布） 2026年09月22日","2026-09-21T18:04:00Z","报道",60,{"impact":109,"substance":107,"depth":109,"authority":18,"freshness":17,"relevant":22,"comment":236},"地市级观摩会，107个品种与BT+DT数字化评价体系有实质信息量，但影响层级与信源权威度有限，可作主题页聚合素材。",[238],{"name":231,"url":229},[29,30,240,241,242],"种业振兴","分子育种","水稻新品种",[244,245],"浙西南 水稻新品种 观摩会","莲都 水稻液相芯片 数字化展示","浙西南水稻新品种观摩会-3301","2026-09-24T00:03:59.270091Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":254,"source_url":251,"published_at":255,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":256,"score_detail":257,"sources":261,"tags":263,"search_phrases":267,"slug":270,"view_count":37,"doi":271,"paper":272,"created_at":284},3273,"Determinants of the Use and Extent of Digital Agriculture Among Moroccan Farmers","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412776","Digital agriculture, driven by advancements in financial engineering, holds significant potential to enhance productivity and sustainability in agricultural production. However, the adoption and extent of these technologies fundamentally depend on farmers’ willingness to accept and use them. While recent studies have identified key factors influencing the adoption of digital agriculture, to the best of our knowledge, no academic study has specifically examined the determinants of both the use and the extent of adoption, particularly within the Moroccan context. This study investigates both the adoption and intensity of digital agriculture among a sample of 250 Moroccan farmers, utilizing a paper-based survey and two econometric approaches: a multinomial logit model and the Heckman model. The findings reveal that farmer age has a negative and significant impact on digital agriculture adoption. At the same time, crop type and risk aversion emerge as significant positive determinants of both the adoption and the extent of smart farming use. Specifically, technology adoption is mainly influenced by age, crop type, and risk aversion, whereas the extent of use is primarily driven by risk aversion and the type of crops cultivated. These results highlight the importance of implementing targeted policies and training programs to promote broader and more intensive use of digital agriculture technologies. Additionally, these findings open up avenues for further research aimed at better understanding the underlying factors that shape Moroccan farmers' behavior toward digital agriculture adoption.","由金融工程进步所驱动的数字农业，在提升农业生产率与可持续性方面具有巨大潜力。然而，这些技术的采用及其程度从根本上取决于农民接受和使用它们的意愿。尽管近期研究已识别出影响数字农业采用的关键因素，但据我们所知，尚无学术研究专门考察使用与采用程度的决定因素，尤其是在摩洛哥背景下。本研究基于250名摩洛哥农民的样本，采用纸质问卷调查和两种计量经济学方法——多项Logit模型与Heckman模型——考察了数字农业的采用情况及使用强度。研究发现，农民年龄对数字农业采用具有显著负向影响。与此同时，作物类型与风险规避对智慧农业的采用及使用程度均呈现显著正向决定作用。具体而言，技术采用主要受年龄、作物类型和风险规避影响，而使用程度则主要由风险规避和所种植作物类型驱动。这些结果凸显了实施有针对性的政策与培训项目以促进数字农业技术更广泛、更深入应用的重要性。此外，这些发现为后续研究开辟了方向，有助于更深入理解塑造摩洛哥农民数字农业采用行为的潜在因素。","AgEcon Search (University of Minnesota, USA)","2026-09-21T00:00:00Z",72,{"impact":20,"substance":258,"depth":68,"authority":259,"freshness":21,"relevant":22,"comment":260},21,13,"基于250户摩洛哥农户调查，用多项Logit与Heckman模型揭示年龄、作物类型与风险规避对数字农业采纳及使用强度的差异化影响，方法规范、结论有新意，对发展中国家数字农业推广有借鉴价值。",[262],{"name":254,"url":251},[29,30,264,265,266],"农业技术推广","农户采纳","摩洛哥农业",[268,269],"摩洛哥 农户 数字农业","Heckman 模型 智慧农业 采纳","摩洛哥农户数字农业-3273","10.22004\u002Fag.econ.412776",{"doi":271,"openalex_id":273,"authors":274,"venue":254,"cited_by_count":37,"oa_url":251,"card":279,"direction":52,"ingested_from":53},"W7213999238",[275,277],{"name":276,"orcid":9},"Adil Jouamaa Mohammed",{"name":278,"orcid":9},"I. Mubarak Abdulilah",{"tldr":280,"method":281,"finding":282,"direction":50,"opportunity":283},"研究摩洛哥250位农民采用数字农业及其使用程度的决定因素。","纸质问卷，多项Logit模型与Heckman模型。","年龄负向影响采用，作物类型和风险规避正向影响采用与使用程度。","可针对不同作物和风险偏好农民设计差异化推广策略，并开展跨区域比较研究。","2026-09-23T23:30:11.088448Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":9,"content":290,"source_name":291,"source_url":9,"published_at":11,"category":233,"cover_url":9,"hotness":19,"is_selected":14,"score":234,"score_detail":292,"sources":294,"tags":296,"search_phrases":299,"slug":302,"view_count":37,"doi":9,"paper":9,"created_at":303},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 5](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[首页](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.cztv.com\u002Fheadlines)\n\n[文化](https:\u002F\u002Fwww.cztv.com\u002Fculture)\n\n 电视 \n\n 广播 \n\n[专区](https:\u002F\u002Fwww.cztv.com\u002Fzone)\n\n![Image 6](blob:http:\u002F\u002Flocalhost\u002F0062bfa43494c79fb356b384bfc51bc8)\n\n![Image 7: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faibtn_icon-Dqb11ejT.png)\n\n![Image 8](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode1-C1Z3XA1v.png)\n\n![Image 9: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_lxw-BTIOt7tT.png)更多精彩 中国蓝新闻\n\n![Image 10](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode2-CU2IzmPJ.png)\n\n![Image 11: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_zsj-C7ZTF9AF.png)更多精彩 下载Z视介\n\n[![Image 12](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_more_btn-DzzjA4z1.png)](https:\u002F\u002Fzmtv.cztv.com\u002Fcmsh5-share\u002Fprod\u002FcommonDownload\u002Findex.html)\n\n登录\n\n![Image 13: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FcreateCenter_btn-CP1dJyCT.png)\n\n![Image 14: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faiblue-BYHqI38h.png)\n\nNaN-NaN-NaN NaN:NaN\n\n编辑：\n\n作者：\n\n[](javascript:; \"分享到微信\") \n\n![Image 15](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)\n\nCopyright 2009-2024 cztv.com [浙ICP备05052141号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F#\u002FIntegrated\u002Findex) | [浙公网安备 33010602002235号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=33010602002235) | 信息网络传播视听节目许可证号：1107197\n\n[互联网新闻信息服务许可证号：33120170002](https:\u002F\u002Fwww.cztv.com\u002Fxuke) | 网络文化经营许可证号：浙网文(2023)1495-044号\n\n 地址：浙江省杭州市莫干山路111号 邮政编码：310005 邮箱：cztv@zmg.com.cn\n\n 网上有害信息举报专区 12321网络不良与垃圾信息举报受理中心 网络违法犯罪举报网站 网络举报APP下载 \n\n 违法和不良信息公开举报电话：12377、0571-81089789 有害信息举报邮箱：jubao@12377.cn 涉未成年人有害信息举报电话: 0571-81089789 \n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fvideo)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveTV)\n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fradio)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveRadio)","新蓝网",{"impact":66,"substance":109,"depth":20,"authority":21,"freshness":21,"relevant":22,"comment":293},"省级官媒报道浙江农科院数字农业研究所AI眼镜与害虫识别小程序落地，属智慧农业细分进展，时效性强但正文信息量有限。",[295],{"name":291,"url":288},[29,30,73,297,298],"智能农机","害虫识别",[300,301],"浙江农科院 数字农业研究所 AI眼镜","浙江 害虫识别 小程序","浙江农科院数字农业研究所AI眼镜-3215","2026-09-23T00:04:29.218317Z"]