[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4343":3,"related-4343":131},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":33,"classification":34,"search_phrases":98,"slug":101,"view_count":92,"doi":102,"paper":103,"created_at":130},4343,"Modern Feeding and Nutritional Management Practices in Livestock Production: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i102206","Livestock production has undergone significant transformation through the adoption of modern feeding and nutritional management practices that enhance productivity, improve animal health, and promote sustainable resource utilisation. Nutrition remains the most influential factor affecting growth, reproduction, milk production, meat quality, feed efficiency, and overall economic performance of livestock enterprises. Recent advances in nutritional science have shifted feeding strategies from conventional ration formulation towards precision nutrition, enabling nutrient supply to be tailored according to species, physiological stage, genetic potential, production level, and environmental conditions. The utilisation of high-quality forages, balanced concentrate mixtures, alternative feed resources, functional feed additives, and advanced feed processing technologies has substantially improved nutrient digestibility and production efficiency. Emerging feed ingredients such as insect meal, algae, single-cell proteins, and agro-industrial by-products offer sustainable alternatives to conventional feedstuffs while supporting circular bioeconomy principles. Precision livestock nutrition supported by artificial intelligence, Internet of Things, machine learning, wearable sensors, automated feeding systems, and decision support tools has further enhanced feed intake monitoring, real-time nutrient adjustment, and farm management efficiency. Nutritional interventions targeting critical physiological stages, including neonatal development, growth, gestation, transition, lactation, reproduction, and heat stress adaptation, have improved animal welfare and lifetime productivity. Functional feed additives such as probiotics, prebiotics, synbiotics, enzymes, phytogenic compounds, antioxidants, buffers, and mycotoxin binders contribute to improved gut health, immune function, nutrient utilisation, and disease resistance. Sustainable feeding approaches emphasising methane mitigation, low-carbon feeding, waste valorisation, climate-smart nutrition, and carbon footprint reduction are becoming integral components of modern livestock systems. Continued integration of precision feeding technologies, functional nutrition, digital agriculture, and sustainable feed resources offers substantial opportunities to improve production efficiency while reducing environmental impacts. This review synthesises recent scientific advances in modern feeding strategies, nutritional management practices, precision livestock nutrition, functional feed additives, and sustainable feeding systems that collectively support resilient, profitable, and environmentally responsible livestock production.","畜牧业生产通过采用现代饲养与营养管理实践，已发生显著变革，这些实践提高了生产力、改善了动物健康并促进了资源的可持续利用。营养仍然是影响畜禽企业生长、繁殖、产奶、肉品质、饲料效率及整体经济效益的最重要因素。营养科学的最新进展已将饲养策略从传统日粮配制转向精准营养，使养分供给能够根据物种、生理阶段、遗传潜力、生产水平和环境条件进行定制。高质量粗饲料、平衡精料混合物、替代饲料资源、功能性饲料添加剂及先进饲料加工技术的利用，已大幅提高养分消化率和生产效率。昆虫粉、藻类、单细胞蛋白和农工业副产品等新兴饲料原料，在为传统饲料提供可持续替代品的同时，也支持了循环生物经济原则。由人工智能、物联网、机器学习、可穿戴传感器、自动化饲喂系统和决策支持工具支持的精准畜禽营养，进一步增强了采食量监测、实时养分调整和农场管理效率。针对关键生理阶段（包括新生儿发育、生长、妊娠、围产期、泌乳、繁殖和热应激适应）的营养干预，改善了动物福利和终生生产力。益生菌、益生元、合生元、酶制剂、植物源化合物、抗氧化剂、缓冲剂和霉菌毒素吸附剂等功能性饲料添加剂，有助于改善肠道健康、免疫功能、养分利用和抗病力。强调甲烷减排、低碳饲养、废弃物增值、气候智慧型营养和碳足迹降低的可持续饲养方法，正成为现代畜牧系统不可或缺的组成部分。精准饲喂技术、功能营养、数字农业和可持续饲料资源的持续整合，为提高生产效率同时减少环境影响提供了重大机遇。本综述综合了现代饲养策略、营养管理实践等方面的最新科学进展。",null,"Archives of Current Research International","2026-10-06T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述精准营养与数字饲喂技术在畜牧生产中的应用，信息密度高但属综述性论文，产业落地影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"农业物联网","智慧畜牧","精准饲喂","精准营养","功能性饲料添加剂","低碳养殖",[],{"status":35,"locked":14,"version":36,"method":37,"evidence":38},"needs_review","2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":42,"reason":97},[40,49,58,67,75,82,90],{"code":41,"score":19,"supported":42,"title":43,"abstract":45,"tags":48},"C",true,[44],"livestock",[46,47,44],"畜牧","畜禽",[],{"code":50,"score":51,"supported":42,"title":52,"abstract":53,"tags":57},"H4",9,[],[54,55,56],"甲烷减排","碳足迹","carbon footprint",[],{"code":59,"score":51,"supported":42,"title":60,"abstract":61,"tags":66},"I2",[],[62,63,64,65],"机器学习","machine learning","artificial intelligence","decision support",[],{"code":68,"score":69,"supported":42,"title":70,"abstract":71,"tags":74},"C6",7,[],[72,73],"动物福利","animal welfare",[28],{"code":76,"score":69,"supported":42,"title":77,"abstract":78,"tags":81},"I5",[],[79,80],"物联网","internet of things",[27],{"code":83,"score":84,"supported":42,"title":85,"abstract":86,"tags":89},"I1",6,[],[87,88],"数字农业","digital agriculture",[],{"code":91,"score":92,"supported":42,"title":93,"abstract":94,"tags":96},"C4",3,[],[95],"饲料",[],"多个产业证据相当，需要确认主归属",[99,100],"精准 livestock 营养 人工智能","昆虫蛋白 藻类 单细胞蛋白 饲料","精准livestock营养人工智能-4343","10.9734\u002Facri\u002F2026\u002Fv26i102206",{"doi":102,"openalex_id":104,"authors":105,"venue":10,"cited_by_count":122,"oa_url":6,"card":123,"direction":127,"ingested_from":129},"W7220718761",[106,109,111,114,116,118,120],{"name":107,"orcid":108},"Yashesh Singh","https:\u002F\u002Forcid.org\u002F0009-0009-8042-2572",{"name":110,"orcid":9},"Jitendra Choudhary",{"name":112,"orcid":113},"Mamta Mamta","https:\u002F\u002Forcid.org\u002F0000-0001-9156-874X",{"name":115,"orcid":9},"Ramdas Bhimrao Shende",{"name":117,"orcid":9},"Prashant Mohanrao Sathe",{"name":119,"orcid":9},"D. Aishwarya",{"name":121,"orcid":9},"Saurabh Sharma",0,{"tldr":124,"method":125,"finding":126,"direction":127,"opportunity":128},"综述现代精准营养与智能饲喂技术如何提升畜牧生产效率并降低环境影响。","文献综述，整合精准营养、AI\u002FIoT\u002F传感器、替代饲料与功能添加剂研究。","精准营养结合智能饲喂与可持续饲料资源，可兼顾生产效率和碳减排。","智慧农业 \u002F 农业物联网","可研究AI饲喂决策模型与昆虫\u002F藻类等替代饲料在低碳养殖中的协同优化。","openalex","2026-10-07T23:30:09.021998Z",{"total":84,"page":22,"page_size":84,"items":132},[133,191,257,330,406,469],{"id":134,"title":135,"url":136,"summary":137,"summary_zh":9,"content":138,"source_name":139,"source_url":9,"published_at":11,"category":140,"cover_url":9,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":147,"tags":149,"primary_topic":153,"secondary_topics":157,"classification":158,"search_phrases":186,"slug":189,"view_count":122,"doi":9,"paper":9,"created_at":190},4381,"数字赋能传统牧业 黑龙江林甸打造智慧畜牧高质量发展新样板","http:\u002F\u002Fwww.hlj.xinhuanet.com\u002F20261006\u002Fb85b3ee101f4435d85eac39d69ce98d2\u002Fc.html","大庆市林甸县聚焦奶牛产业,搭建全域奶牛产业大数据平台,覆盖 17 处规模牧场、5.9 万头奶牛,集成 DWeb\u002FDFeed\u002FDCalf\u002FDSmart 四大智能系统,实现良种覆盖率 100%、犊牛成活率稳定 99%、奶牛空怀率下降,推动当地奶业由粗放规模型向智能精细、绿色高效转型,日均商品奶超千吨,稳居全国县域前列。","  大庆市林甸县是黑龙江省内重点畜牧县域、全国优质奶源核心产区。当地立足寒地牧业优势，抢抓数字农业机遇，以大数据、物联网技术赋能传统养殖，通过搭建智慧平台、升级智能系统、完善配套服务，推动畜牧业由粗放规模型向智能精细、绿色高效转型，打造现代农业发展新样板。\n\n  畜牧业是林甸支柱富民产业，规模优势显著。全县现有规模化奶牛牧场32处，含3处万头牧场、8处中型牧场，奶牛存栏超10万头，日均产商品奶超千吨，生鲜乳产能稳居全国县域前列。针对传统养殖管理粗放、效率不高、风险难控等短板，林甸将奶业数字化转型列为重点工程，全面推进牧场智能升级，构建全流程数字化管控体系。\n\n  当地建成全域奶牛产业大数据平台，实现重点牧场数字化全覆盖。目前平台覆盖17处规模牧场、5.9万头奶牛，累计归集数千万条养殖、生产、防疫、环境数据，打通政企数据壁垒，实现监管、生产、决策数据协同。依托物联网智能设备，养殖全过程可自动采集、实时分析、智能预警，形成精准高效的数字化治理模式。\n\n  依托四大智能系统，林甸建成全链条精准养殖体系。DWeb系统为奶牛建立专属数字档案，实现良种覆盖率100%；DFeed系统依托大数据智能配比日粮，降低饲料损耗、节约养殖成本；DCalf系统精准培育犊牛，县域犊牛成活率稳定达99%；DSmart系统依托电子耳标实现牛只精准溯源，有效提升管理精度与效率。\n\n  数字化转型持续释放降本提质增效红利。智能设备替代传统人工巡查统计，大幅精简人力投入；通过监测牛只体征、活动数据，可提前预判疫病风险、及时处置，筑牢养殖安全底线。智能发情识别、标准化电子系谱，有效降低奶牛空怀率、提升繁育质量，助力牧场实现人力精简、效益提升。\n\n  为护航智慧畜牧发展，林甸县同步搭建全方位服务体系。县域组建专项服务专班，常态化驻场指导解决养殖、防疫、繁育难题；深化与省内农业高校校地合作，推动先进养殖技术落地转化，常态化开展技能培训，持续提升从业人员数字化、标准化养殖水平。\n\n  下一步，林甸县将持续迭代升级智慧畜牧体系，完善数据标准与产业数据库，优化AI分析、疫病预警、品质追溯等功能，搭建生鲜乳全链条溯源体系。持续打通政企数据链路，构建协同高效的数字畜牧发展格局，擦亮优质奶源品牌，以智慧牧业赋能乡村振兴和县域高质量发展。","新华网黑龙江频道 2026-10-06","报道",71,{"impact":17,"substance":143,"depth":144,"authority":145,"freshness":69,"relevant":22,"comment":146},19,15,12,"县域奶业数字化转型的实质案例，数据与系统细节充分，具行业示范价值。",[148],{"name":139,"url":136},[150,27,28,151,152],"数字乡村","奶业数字化","奶牛大数据",{"code":68,"slug":154,"name":155,"path":156,"parent_code":41},"smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock",[],{"status":159,"locked":14,"version":36,"method":37,"evidence":160},"classified",{"candidates":161,"agricultural_evidence":42},[162,166,170,177,181],{"code":68,"score":51,"supported":42,"title":163,"abstract":164,"tags":165},[28],[],[28],{"code":41,"score":21,"supported":42,"title":167,"abstract":168,"tags":169},[46],[],[],{"code":171,"score":84,"supported":42,"title":172,"abstract":173,"tags":176},"C2",[],[174,175],"奶牛","奶业",[],{"code":76,"score":22,"supported":14,"title":178,"abstract":179,"tags":180},[],[],[27],{"code":182,"score":22,"supported":14,"title":183,"abstract":184,"tags":185},"I6",[],[],[150],[187,188],"林甸 智慧畜牧 奶牛","黑龙江 奶业数字化 大数据平台","林甸智慧畜牧奶牛-4381","2026-10-08T00:06:06.982819Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":213,"classification":214,"search_phrases":241,"slug":244,"view_count":122,"doi":245,"paper":246,"created_at":256},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",49,{"impact":21,"substance":145,"depth":13,"authority":51,"freshness":13,"relevant":22,"comment":201},"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[203],{"name":197,"url":194},[205,27,206,207],"智慧农业","智能灌溉","土壤墒情",{"code":76,"slug":209,"name":210,"path":211,"parent_code":212},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":159,"locked":14,"version":36,"method":37,"evidence":215},{"candidates":216,"agricultural_evidence":42},[217,222,227,234],{"code":83,"score":144,"supported":42,"title":218,"abstract":220,"tags":221},[219],"smart agriculture",[205,219],[205],{"code":76,"score":144,"supported":42,"title":223,"abstract":225,"tags":226},[224],"IoT",[79,224],[27],{"code":228,"score":69,"supported":42,"title":229,"abstract":230,"tags":233},"H2",[],[231,232],"土壤","soil",[207],{"code":235,"score":236,"supported":42,"title":237,"abstract":238,"tags":240},"H3",4,[],[239],"水资源",[206],[242,243],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554","10.5281\u002Fzenodo.23531185",{"doi":245,"openalex_id":247,"authors":248,"venue":197,"cited_by_count":122,"oa_url":194,"card":251,"direction":127,"ingested_from":129},"W7223137846",[249],{"name":250,"orcid":9},"Prachi Khairnar",{"tldr":252,"method":253,"finding":254,"direction":127,"opportunity":255},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","2026-10-22T11:20:18.299106Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":197,"source_url":260,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":267,"tags":269,"primary_topic":274,"secondary_topics":279,"classification":286,"search_phrases":314,"slug":317,"view_count":122,"doi":318,"paper":319,"created_at":329},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":17,"substance":18,"depth":265,"authority":145,"freshness":51,"relevant":22,"comment":266},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[268],{"name":197,"url":260},[270,27,271,272,273],"农业人工智能","遥感","智慧灌溉","节水农业",{"code":235,"slug":275,"name":276,"path":277,"parent_code":278},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[280,281],{"code":76,"slug":209,"name":210,"path":211,"parent_code":212},{"code":282,"slug":283,"name":284,"path":285,"parent_code":212},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":159,"locked":14,"version":36,"method":37,"evidence":287},{"candidates":288,"agricultural_evidence":42},[289,294,298,303,307],{"code":235,"score":143,"supported":42,"title":290,"abstract":291,"tags":293},[275],[292,239,275],"灌溉",[272,273],{"code":76,"score":17,"supported":42,"title":295,"abstract":296,"tags":297},[224],[79,80,224],[27],{"code":282,"score":144,"supported":42,"title":299,"abstract":301,"tags":302},[300],"remote sensing",[271,300],[271],{"code":59,"score":145,"supported":42,"title":304,"abstract":305,"tags":306},[64],[64],[270],{"code":308,"score":84,"supported":42,"title":309,"abstract":310,"tags":313},"H8",[],[311,312],"可持续农业","sustainable agriculture",[],[315,316],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":318,"openalex_id":320,"authors":321,"venue":197,"cited_by_count":122,"oa_url":260,"card":324,"direction":127,"ingested_from":129},"W7223090389",[322],{"name":323,"orcid":9},"Patil",{"tldr":325,"method":326,"finding":327,"direction":127,"opportunity":328},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":197,"source_url":333,"published_at":336,"category":12,"cover_url":9,"hotness":337,"is_selected":14,"score":338,"score_detail":339,"sources":343,"tags":347,"primary_topic":350,"secondary_topics":351,"classification":353,"search_phrases":383,"slug":386,"view_count":122,"doi":387,"paper":388,"created_at":405},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",25,73,{"impact":340,"substance":341,"depth":19,"authority":20,"freshness":122,"relevant":22,"comment":342},22,21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[344,345],{"name":197,"url":333},{"name":197,"url":346},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[205,270,27,348,349],"区块链溯源","精准灌溉",{"code":235,"slug":275,"name":276,"path":277,"parent_code":278},[352],{"code":76,"slug":209,"name":210,"path":211,"parent_code":212},{"status":159,"locked":14,"version":36,"method":37,"evidence":354},{"candidates":355,"agricultural_evidence":42},[356,360,364,368,374,379],{"code":235,"score":144,"supported":42,"title":357,"abstract":358,"tags":359},[275],[292,275],[349],{"code":76,"score":13,"supported":42,"title":361,"abstract":362,"tags":363},[],[79,80,224],[27],{"code":59,"score":236,"supported":42,"title":365,"abstract":366,"tags":367},[],[64],[270],{"code":369,"score":92,"supported":42,"title":370,"abstract":371,"tags":373},"G2",[],[372],"小农户",[],{"code":375,"score":22,"supported":14,"title":376,"abstract":377,"tags":378},"E4",[],[],[348],{"code":83,"score":22,"supported":14,"title":380,"abstract":381,"tags":382},[],[],[205],[384,385],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":387,"openalex_id":389,"authors":390,"venue":197,"cited_by_count":122,"oa_url":333,"card":399,"direction":127,"ingested_from":129},"W7223034914",[391,394,396],{"name":392,"orcid":393},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":395,"orcid":9},"Dr.T.Baskar",{"name":397,"orcid":398},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":400,"method":401,"finding":402,"direction":403,"opportunity":404},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":414,"sources":416,"tags":418,"primary_topic":421,"secondary_topics":422,"classification":427,"search_phrases":443,"slug":446,"view_count":122,"doi":447,"paper":448,"created_at":468},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",{"impact":265,"substance":341,"depth":17,"authority":145,"freshness":51,"relevant":22,"comment":415},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[417],{"name":412,"url":409},[205,270,27,419,420],"大语言模型","传感器标准化",{"code":76,"slug":209,"name":210,"path":211,"parent_code":212},[423],{"code":59,"slug":424,"name":425,"path":426,"parent_code":212},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":159,"locked":14,"version":36,"method":37,"evidence":428},{"candidates":429,"agricultural_evidence":42},[430,434,439],{"code":76,"score":17,"supported":42,"title":431,"abstract":432,"tags":433},[224],[79,80,224],[27],{"code":59,"score":265,"supported":42,"title":435,"abstract":436,"tags":438},[65],[437,65],"large language model",[270,419],{"code":83,"score":69,"supported":42,"title":440,"abstract":441,"tags":442},[],[205,219],[205],[444,445],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":447,"openalex_id":449,"authors":450,"venue":412,"cited_by_count":122,"oa_url":409,"card":463,"direction":127,"ingested_from":129},"W7222920424",[451,453,455,457,459,461],{"name":452,"orcid":9},"Leqi Lai",{"name":454,"orcid":9},"Langtao Duan",{"name":456,"orcid":9},"Yuhao Chen",{"name":458,"orcid":9},"Yuhang Zheng",{"name":460,"orcid":9},"Jinyu Yang",{"name":462,"orcid":9},"Mingyao Wen",{"tldr":464,"method":465,"finding":466,"direction":127,"opportunity":467},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":474,"content":9,"source_name":475,"source_url":472,"published_at":476,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":477,"score_detail":478,"sources":480,"tags":482,"primary_topic":485,"secondary_topics":491,"classification":498,"search_phrases":541,"slug":544,"view_count":122,"doi":9,"paper":545,"created_at":563},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":145,"substance":341,"depth":19,"authority":51,"freshness":21,"relevant":22,"comment":479},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[481],{"name":475,"url":472},[205,270,27,483,484],"温室环境","小气候预测",{"code":486,"slug":487,"name":488,"path":489,"parent_code":490},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[492,493],{"code":76,"slug":209,"name":210,"path":211,"parent_code":212},{"code":494,"slug":495,"name":496,"path":497,"parent_code":490},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":159,"locked":14,"version":36,"method":37,"evidence":499},{"candidates":500,"agricultural_evidence":42},[501,505,512,521,527,533,537],{"code":76,"score":144,"supported":42,"title":502,"abstract":503,"tags":504},[224],[79,224],[27],{"code":486,"score":506,"supported":42,"title":507,"abstract":509,"tags":511},14,[508],"greenhouse",[510,508],"温室",[],{"code":494,"score":51,"supported":42,"title":513,"abstract":514,"tags":520},[],[515,516,517,518,519],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":83,"score":69,"supported":42,"title":522,"abstract":523,"tags":526},[],[524,525],"精准农业","precision agriculture",[205],{"code":59,"score":69,"supported":42,"title":528,"abstract":529,"tags":532},[],[530,531],"深度学习","deep learning",[270],{"code":228,"score":84,"supported":42,"title":534,"abstract":535,"tags":536},[],[231,232],[],{"code":235,"score":84,"supported":42,"title":538,"abstract":539,"tags":540},[],[292,275],[],[542,543],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":546,"authors":547,"venue":475,"cited_by_count":122,"oa_url":9,"card":558,"direction":127,"ingested_from":129},"W7222909837",[548,550,552,554,556],{"name":549,"orcid":9},"Siman Emmanuel*",{"name":551,"orcid":9},"Edward Onyebueke Agu",{"name":553,"orcid":9},"Musa Gani",{"name":555,"orcid":9},"Oladunjoye John Abiodun",{"name":557,"orcid":9},"Anagu Emmanuel John",{"tldr":559,"method":560,"finding":561,"direction":127,"opportunity":562},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z"]