[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4879":3,"related-4879":125},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":105,"slug":108,"view_count":109,"doi":110,"paper":111,"created_at":124},4879,"Smart and Mechanized Feeding Systems for Goat Farming: Integrating Agricultural Engineering, Precision Livestock Management, and Sustainable Rural Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23344398","2. Alternative Research Titles 1. Design and Development of an Automated Goat Feeding System for Improved Farm Productivity and Labour Efficiency 2. Mechanization of Goat Feeding Operations: Engineering Innovations for Sustainable Livestock Production 3. Smart Livestock Feeding Technologies: A Framework for Automated Feed Distribution and Precision Goat Farming 4. Low-Cost Automated Feeding Systems for Smallholder Goat Farms: Design Considerations, Operational Efficiency, and Economic Feasibility 5. Application of Sensor-Based Automation in Goat Farming: Advancing Feed Management and Rural Agricultural Productivity 6. Intelligent Feeding Mechanisms for Small Ruminants: Integrating Mechanical Design, Automation, and Digital Monitoring 7. Sustainable Goat Farming Through Feeding Automation: Opportunities, Challenges, and Technological Perspectives 8. Engineering Approaches to Livestock Feed Management: Development of Mechanized Feeding Systems for Small and Medium-Scale Goat Farms 9. Precision Feeding in Goat Production: Integrating Smart Sensors, Automated Dispensing, and Sustainable Farm Management 10. From Traditional Feeding to Smart Livestock Farming: A Technology-Driven Approach to Goat Feed Distribution 3. Suggested Subtitle An Engineering-Based Framework for Automated Feed Distribution, Labour Reduction, Feed-Waste Management, and Affordable Precision Livestock Production in India. 4. Detailed Research Description Research Description Research Description The proposed research investigates the design principles, operational requirements, and potential agricultural applications of smart and mechanized feeding systems for goat farming. Inspired by a Telugu newspaper article depicting a mechanized feeding arrangement, the study explores how engineering innovations can improve the efficiency, consistency, and sustainability of livestock feeding operations. Traditional goat-feeding practices frequently involve manual feed preparation, transportation, and distribution. These activities can require substantial labour and may result in inconsistent feed allocation, spillage, and limited documentation of feed consumption. The proposed research addresses these challenges through a conceptual framework that combines mechanical feed handling, controlled dispensing, suitable distribution mechanisms, and optional sensor-assisted monitoring. The study will examine the integration of feed-storage hoppers, metering devices, motor-driven mechanisms, weighing sensors, microcontrollers, and monitoring interfaces. Particular attention will be given to the suitability of these components for different feed materials, including chopped fodder, forage mixtures, and concentrated feed. The system will be designed around the operational requirements of small and medium-scale goat farms, with emphasis on affordability, reliability, ease of maintenance, and adaptability to Indian agricultural conditions. A central component of the research is the development of a quantitative evaluation framework for comparing mechanized and conventional feeding practices. Relevant performance indicators include labour time per feeding cycle, feed-distribution uniformity, feed wastage, energy consumption, equipment reliability, maintenance requirements, and overall operating cost. If a prototype is constructed, controlled testing and farm-based trials can be used to determine whether the proposed design provides measurable improvements under realistic operating conditions. The research will also consider the role of digital technologies in precision livestock farming. Sensors and automated recordkeeping could support monitoring of delivered feed quantities, feed-bin levels, and equipment performance. Future extensions may incorporate Internet of Things connectivity and data analytics to support farm-management decisions. However, the system will be designed to retain essential operating and safety functions even when internet connectivity is unavailable. Animal welfare, nutritional suitability, and farm economics will remain central considerations. Mechanization must ensure appropriate feed access, hygienic feeding conditions, safe equipment operation, and compatibility with the nutritional requirements of goats. Economic analysis will examine initial investment, labour savings, potential reductions in feed losses, maintenance costs, energy requirements, and estimated payback period. The expected contribution is an engineering framework for developing and evaluating practical feeding automation technologies for goat farming. By connecting mechanical design with livestock-management requirements and rural economic considerations, the study seeks to establish a foundation for affordable, scalable, and sustainable agricultural mechanization. The proposed benefits will need to be established through prototype development and empirical validation rather than assumed in advance. 5. Research Objectives To develop a conceptual design for a mechanized goat-feeding system. To identify suitable feed-storage, metering, and distribution mechanisms. To evaluate the potential integration of sensors and automated control. To establish quantitative measures of feeding efficiency and feed wastage. To assess operational safety, animal welfare, and maintenance requirements. To formulate an economic evaluation framework for smallholder adoption. To propose future opportunities for IoT-enabled precision livestock management. 6. Proposed Research Novelty The potential novelty lies in integrating affordable mechanical feeding, quantity-controlled dispensing, and sensor-assisted monitoring into a modular system specifically evaluated for small and medium-scale goat farms. The novelty should be established through a systematic review of existing patents and research publications, followed by a clear comparison with previously developed livestock-feeding systems. The combination of existing components alone does not establish scientific novelty; the contribution should be demonstrated through a new design, validated performance improvement, or original experimental findings. 7. Recommended Keywords Goat farming; mechanized feeding; automated feed distribution; precision livestock farming; agricultural engineering; smart agriculture; feed management; Internet of Things; sustainable livestock production; rural mechanization; sensor-based automation; smallholder farming. 8. Author Details Geruganti Sudhakar Faculty Rajiv Gandhi University of Knowledge Technologies (RGUKT), IIIT Basar Telangana, India. My recommendation: Use the combined title in Section 1 for a broad, interdisciplinary review paper. If you plan to fabricate and experimentally test a feeder, Alternative Title 1 is more appropriate because it clearly communicates an engineering-development and validation study.","2. 备选研究标题 1. 用于提高农场生产力和劳动效率的自动化山羊饲喂系统的设计与开发 2. 山羊饲喂作业的机械化：可持续畜牧生产的工程创新 3. 智能畜牧饲喂技术：自动化饲料分配与精准山羊养殖框架 4. 面向小农户山羊养殖场的低成本自动化饲喂系统：设计考量、运行效率与经济可行性 5. 基于传感器的自动化技术在山羊养殖中的应用：推进饲料管理与农村农业生产力 6. 小型反刍动物的智能饲喂机制：机械设计、自动化与数字化监测的集成 7. 通过饲喂自动化实现可持续山羊养殖：机遇、挑战与技术展望 8. 畜牧饲料管理的工程方法：面向中小规模山羊养殖场的机械化饲喂系统开发 9. 山羊生产中的精准饲喂：智能传感器、自动化投喂与可持续农场管理的集成 10. 从传统饲喂到智能畜牧养殖：一种技术驱动的山羊饲料分配方法 3. 建议副标题 一种基于工程的框架，用于印度自动化饲料分配、劳动力减少、饲料浪费管理及可负担的精准畜牧生产。 4. 详细研究描述 研究描述 研究描述 本研究拟探讨山羊养殖中智能化和机械化饲喂系统的设计原理、运行要求及潜在农业应用。受一篇描述机械化饲喂装置的泰卢固语报纸文章启发，本研究探索工程创新如何提升畜牧饲喂作业的效率、一致性和可持续性。传统的山羊饲喂方式通常涉及人工饲料制备、运输和分配。这些活动可能需要大量劳动力，并可能导致饲料分配不均、撒漏以及饲料消耗记录有限。本研究拟通过一个概念框架应对这些挑战，该框架结合机械饲料处理、受控投喂、适宜的分配机制以及可选的传感器辅助监测。研究将考察饲料储存料斗、计量装置、电机驱动机构、称重传感器、",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-13T00:00:00Z","论文",10,false,45,{"impact":17,"substance":18,"depth":13,"authority":19,"freshness":20,"relevant":21,"comment":22},8,12,6,9,1,"概念性研究提案，聚焦小规模山羊养殖的机械化与精准饲喂框架，对智慧畜牧方向有参考价值但尚无实证数据。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业物联网","农业机械化","精准畜牧","山羊养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,58,64,71,78,85,92,99],{"code":36,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},14,true,[48],"livestock",[50,48],"畜牧",[],{"code":32,"score":45,"supported":46,"title":53,"abstract":55,"tags":57},[54],"goat",[56,54],"山羊",[],{"code":59,"score":17,"supported":46,"title":60,"abstract":62,"tags":63},"G",[61],"rural development",[],[],{"code":65,"score":66,"supported":46,"title":67,"abstract":68,"tags":70},"C6",7,[],[29,69],"animal welfare",[29],{"code":72,"score":66,"supported":46,"title":73,"abstract":74,"tags":77},"I5",[],[75,76],"internet of things","IoT",[27],{"code":79,"score":19,"supported":46,"title":80,"abstract":81,"tags":84},"C4",[],[82,83],"饲料","forage",[],{"code":86,"score":19,"supported":46,"title":87,"abstract":88,"tags":91},"G2",[],[89,90],"小农户","smallholder",[],{"code":93,"score":94,"supported":46,"title":95,"abstract":96,"tags":98},"F5",4,[],[97],"机械化",[28],{"code":100,"score":94,"supported":46,"title":101,"abstract":102,"tags":104},"I1",[],[103],"smart agriculture",[26],[106,107],"山羊 机械化饲喂 系统","精准畜牧 自动投喂 印度","山羊机械化饲喂系统-4879",0,"10.5281\u002Fzenodo.23344398",{"doi":110,"openalex_id":112,"authors":113,"venue":10,"cited_by_count":109,"oa_url":6,"card":117,"direction":121,"ingested_from":123},"W7221716542",[114],{"name":115,"orcid":116},"Sudhakar Geruganti","https:\u002F\u002Forcid.org\u002F0009-0000-0039-7536",{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"提出山羊养殖智能机械化喂料系统的设计框架，整合工程、精准畜牧与农村可持续发展。","概念框架设计，结合料斗、计量装置、电机驱动、称重传感器等。","机械化喂料可减少劳动力、稳定投喂量、降低饲料浪费并提升记录能力。","智慧农业 \u002F 农业物联网","面向小农户的低成本自动化喂料系统缺乏实证，可研究其经济可行性与传感器集成效果。","openalex","2026-10-14T23:30:08.923193Z",{"total":19,"page":21,"page_size":19,"items":126},[127,185,272,345,434,484],{"id":128,"title":129,"url":130,"summary":131,"summary_zh":9,"content":9,"source_name":132,"source_url":9,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":141,"tags":143,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":171,"slug":174,"view_count":159,"doi":9,"paper":175,"created_at":184},4160,"DGF-YOLOv12n-LiteP2: Dynamic Gated Local-Context Fusion with a High-Resolution Detection Head for Cattle Body-Part Detection in Complex Barns（基于YOLOv12n动态门控局部上下文融合的复杂牛舍牛体部位检测）","https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F19\u002F6156","Yanhong Liu、Le Yang、Qingqing Li、Jiazi Han、Jingrong Wang、Meng Han、Hua Yang（MDPI Sensors 2026, 26(19): 6156；山西农业大学信息科学与工程学院、软件学院）。针对复杂牛舍场景中尺度变化、遮挡、光照变化、栏栅背景干扰等挑战，提出基于YOLOv12n的检测器DGF-YOLOv12n-LiteP2，结合动态局部-上下文特征融合与轻量级stride-4预测分支。在6991张图、132,302个身体部位标注的单站点数据集上：mAP50=93.92%、mAP75=79.27%、mAP50:95=69.88%（相对YOLOv12n提升9.61个百分点）。模型含1168万参数、需22.89 GFLOPs。","MDPI Sensors 2026, 26(19): 6156","2026-09-28T00:00:00Z",74,{"impact":136,"substance":137,"depth":138,"authority":139,"freshness":19,"relevant":21,"comment":140},15,22,18,13,"面向复杂牛舍的轻量化牛体部位检测模型，方法有创新、数据规模与精度指标扎实，对智慧畜牧视觉监测有实用参考价值。",[142],{"name":132,"url":130},[26,144,145,29,146],"农业人工智能","目标检测","牛体部位检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":150},{"candidates":151,"agricultural_evidence":46},[152,157,163,167],{"code":32,"score":17,"supported":46,"title":153,"abstract":155,"tags":156},[154],"cattle",[],[],{"code":158,"score":159,"supported":14,"title":160,"abstract":161,"tags":162},"I2",2,[],[],[144,145],{"code":65,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[29],{"code":100,"score":21,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[26],[172,173],"山西农业大学 牛体部位检测","DGF-YOLOv12n 牛舍检测","山西农业大学牛体部位检测-4160",{"doi":9,"openalex_id":9,"authors":176,"venue":9,"cited_by_count":109,"oa_url":9,"card":177,"direction":181,"ingested_from":183},[],{"tldr":178,"method":179,"finding":180,"direction":181,"opportunity":182},"提出DGF-YOLOv12n-LiteP2，实现复杂牛舍场景下牛体部位高精度检测。","基于YOLOv12n，融合动态门控局部-上下文特征与轻量stride-4预测分支","mAP50达93.92%，较YOLOv12n提升9.61个百分点，参数量1168万、22.89 GF","农业人工智能与决策模型","可探索多站点跨舍泛化、遮挡与光照鲁棒性提升，及轻量化模型在边缘设备的实时部署。","agent","2026-10-04T00:09:10.242632Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":209,"search_phrases":235,"slug":238,"view_count":239,"doi":240,"paper":241,"created_at":271},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":138,"depth":195,"authority":13,"freshness":13,"relevant":21,"comment":196},16,"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。",[198],{"name":191,"url":188},[200,26,144,27,201],"数字农业","畜牧养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[204],{"code":72,"slug":205,"name":206,"path":207,"parent_code":208},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":210},{"candidates":211,"agricultural_evidence":46},[212,217,221,227,231],{"code":72,"score":138,"supported":46,"title":213,"abstract":214,"tags":216},[76],[215,75,76],"物联网",[27],{"code":32,"score":45,"supported":46,"title":218,"abstract":219,"tags":220},[54],[56,54],[],{"code":158,"score":66,"supported":46,"title":222,"abstract":223,"tags":226},[],[224,225],"artificial intelligence","decision support",[144],{"code":36,"score":19,"supported":46,"title":228,"abstract":229,"tags":230},[],[50,48],[],{"code":100,"score":159,"supported":14,"title":232,"abstract":233,"tags":234},[],[],[200,26],[236,237],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347",5,"10.35145\u002F6e5wnv18",{"doi":240,"openalex_id":242,"authors":243,"venue":191,"cited_by_count":109,"oa_url":188,"card":266,"direction":121,"ingested_from":123},"W7214075234",[244,246,248,250,252,254,257,260,262,264],{"name":245,"orcid":9},"Nicholas Renaldo",{"name":247,"orcid":9},"Sulaiman Musa",{"name":249,"orcid":9},"Jaswar Koto",{"name":251,"orcid":9},"Kristy Veronica",{"name":253,"orcid":9},"Umar Faruq",{"name":255,"orcid":256},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":258,"orcid":259},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":261,"orcid":9},"Wilda Susanti",{"name":263,"orcid":9},"Achmad Tavip Junaedi",{"name":265,"orcid":9},"Nabila Wahid",{"tldr":267,"method":268,"finding":269,"direction":121,"opportunity":270},"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":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":315,"slug":318,"view_count":159,"doi":319,"paper":320,"created_at":344},2411,"Deep reinforcement learning for simulated autonomous heat stress mitigation in dairy cattle","https:\u002F\u002Fdoi.org\u002F10.1080\u002F1828051x.2026.2728340","Heat stress significantly constrains dairy cattle productivity and welfare, compromising milk production and fertility. Yet, despite advances in heat stress detection and prediction, conventional mitigation strategies still rely on fixed thresholds, overlooking inter-animal variability and cumulative thermal load. In this simulation-based proof-of-concept study, we evaluated deep reinforcement learning (DRL) for individualised heat-stress decision support. Using multi-modal sensor data from the MmCows dataset, we developed a Deep Q-Network (DQN) framework for simulated cooling decisions. Cooling interventions were not experimentally applied to animals; their expected effects were computationally modelled as action-specific reductions in effective thermal load with corresponding energy penalties. We trained and evaluated four DQN variants, namely Standard, Double, Duelling and Double Duelling DQN with Prioritised Experience Replay, on real-world sensor data from 10 Holstein dairy cows over a 14–day period. The environment incorporated core body temperature (CBT), Temperature-Humidity Index (THI), behavioural indicators and milk-yield. The Double DQN agent achieved the highest numerical reward (18.26 ± 3.89), but its learned policy selected no intervention in essentially all evaluation cases, and its reward was not significantly different from the No Action baseline (17.66 ± 5.10; Welch t-test, p = 0.510). This pattern was reproduced across five independent random seeds, with all agents converging on low- or no-intervention policies. The moderate heat-stress conditions and reward formulation favoured low-energy policies. These findings indicate that DRL is a promising framework for testing animal-centred control policies in precision livestock farming and developing field-validated autonomous cooling systems with safety constraints through controlled trials and reward-weight sensitivity analysis.","热应激显著制约奶牛的生产性能与福利，损害产奶量和繁殖力。然而，尽管热应激检测与预测技术取得了进展，传统的缓解策略仍依赖于固定阈值，忽视了动物个体差异和累积热负荷。在这项基于仿真的概念验证研究中，我们评估了深度强化学习（DRL）用于个体化热应激决策支持的可行性。利用MmCows数据集的多模态传感器数据，我们开发了一个深度Q网络（DQN）框架用于模拟降温决策。降温干预未在动物身上进行实验性施加；其预期效果通过计算建模为动作特定的有效热负荷降低及相应的能量代价。我们在10头荷斯坦奶牛为期14天的真实传感器数据上训练并评估了四种DQN变体，即标准DQN、双深度Q网络（Double DQN）、竞争深度Q网络（Duelling DQN）和带优先经验回放的双竞争深度Q网络。环境纳入的核心体温（CBT）、温湿度指数（THI）、行为指标和产奶量。Double DQN智能体获得了最高的数值奖励（18.26 ± 3.89），但其学习策略在几乎所有评估案例中均选择不进行干预，其奖励与不采取行动基线（17.66 ± 5.10；Welch t检验，p = 0.510）无显著差异。这一模式在五个独立随机种子下均得到重现，所有智能体均收敛于低干预或不干预策略。中等热应激条件和奖励函数设计倾向于低能量策略。这些发现表明，DRL是精准畜牧业中测试以动物为中心的控制策略的一个有前景的框架，并可通过对照试验和奖励权重敏感性分析开发具有安全约束的田间验证自主降温系统。","Italian Journal of Animal Science","2026-09-12T00:00:00Z",65,{"impact":17,"substance":138,"depth":282,"authority":139,"freshness":20,"relevant":21,"comment":283},17,"仿真验证的深度强化学习个体化降温决策研究，方法新颖但结论为阴性、未做田间验证，属细分领域前沿探索，可作为智慧畜牧主题聚合素材。",[285],{"name":278,"url":275},[26,144,287,29,288],"强化学习","奶牛热应激",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":292},{"candidates":293,"agricultural_evidence":46},[294,299,303,307,311],{"code":32,"score":45,"supported":46,"title":295,"abstract":296,"tags":298},[154],[297,154],"奶牛",[],{"code":36,"score":19,"supported":46,"title":300,"abstract":301,"tags":302},[],[50,48],[],{"code":158,"score":239,"supported":46,"title":304,"abstract":305,"tags":306},[],[225],[144,287],{"code":65,"score":94,"supported":46,"title":308,"abstract":309,"tags":310},[],[29],[29],{"code":100,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[26],[316,317],"农业人工智能 奶牛热应激 强化学习 智慧农业","农业人工智能 奶牛热应激","农业人工智能奶牛热应激强化学习智慧农业-2411","10.1080\u002F1828051x.2026.2728340",{"doi":319,"openalex_id":321,"authors":322,"venue":278,"cited_by_count":109,"oa_url":338,"card":339,"direction":121,"ingested_from":123},"W7212350875",[323,326,329,332,335],{"name":324,"orcid":325},"Jan Saro","https:\u002F\u002Forcid.org\u002F0009-0001-4355-5271",{"name":327,"orcid":328},"Jaromír Ducháček","https:\u002F\u002Forcid.org\u002F0000-0002-6347-8541",{"name":330,"orcid":331},"Jana Mazancová","https:\u002F\u002Forcid.org\u002F0000-0002-7735-5606",{"name":333,"orcid":334},"Luděk Stádník","https:\u002F\u002Forcid.org\u002F0000-0002-7880-0089",{"name":336,"orcid":337},"Vít Malinovský","https:\u002F\u002Forcid.org\u002F0000-0002-7138-6655","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F1828051X.2026.2728340?needAccess=true",{"tldr":340,"method":341,"finding":342,"direction":181,"opportunity":343},"用深度强化学习在仿真中为奶牛个体化热应激缓解做决策支持。","基于MmCows多模态传感器数据，训练四种DQN变体模拟降温决策。","Double DQN奖励最高但策略几乎不干预，与无动作基线无显著差异。","可引入安全约束与奖励权重敏感性分析，开展田间验证的自主降温系统研究。","2026-09-14T23:30:07.999063Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":353,"score_detail":354,"sources":358,"tags":360,"primary_topic":363,"secondary_topics":368,"classification":374,"search_phrases":410,"slug":413,"view_count":401,"doi":414,"paper":415,"created_at":433},2017,"Two Decades of Computer Vision in Precision Livestock Farming: an Umbrella Review of Applications, Benchmarking Evidence, and the Performance-to-Deployment Gap Across Cattle, Pig, and Poultry Systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112419","Computer vision is becoming a core technology in PLF, enabling non-contact monitoring, phenotyping, and decision support at both animal and group levels. This umbrella review synthesised review-level evidence from 112 reviews on cattle, pig, and poultry systems, covering visual applications for identification, phenotyping, health, behaviour, locomotion, body condition, growth, reproduction, mortality, and resource use. To preserve the review as the bibliographic unit while capturing multi-topic evidence, multi-label coding generated 328 review-domain assignments across six PLF computer vision domains. Health\u002Fstress and posture\u002Factivity were the most frequently reviewed areas; however, these patterns reflect review coverage rather than evidence strength. Benchmarking reporting averaged 11.09 out of 14 criteria, corresponding to 79.2%, but validation-critical items, including class balance, annotation protocols, validation splits, and farm or site reporting, remained less complete. Methodological confidence was limited: AMSTAR 2 classified 95 reviews as critically low and 17 as moderate, while ROBIS classified 78 as high, 17 as unclear, and 17 as low risk of bias. The corpus-level candidate-reference overlap estimate was 0.122% and is retained only as a sensitivity estimate, not as formal primary-study CCA or evidence of independence. Review-derived performance and readiness tiers identified a mean descriptive performance-to-deployment gap of 0.63 across 328 assignments. Overall, reported model performance continues to exceed documented farm-deployment evidence, highlighting the need for stronger external validation, transparent benchmarking, workflow integration, economic assessment, and user-readiness evidence before routine deployment claims can be supported.","计算机视觉正成为精准畜牧养殖（PLF）中的核心技术，能够在个体和群体层面实现非接触式监测、表型分析和决策支持。本伞状综述综合了112篇综述的综述级证据，涵盖牛、猪和家禽养殖系统，涉及识别、表型分析、健康、行为、运动、体况、生长、繁殖、死亡率和资源利用等视觉应用。为在保留综述作为文献计量单元的同时捕捉多主题证据，采用多标签编码在六个PLF计算机视觉领域中生成了328个综述-领域分配。健康\u002F应激和姿态\u002F活动是综述覆盖最多的领域；然而，这些模式反映的是综述覆盖情况而非证据强度。基准报告平均得分为14项标准中的11.09项，对应79.2%，但验证关键条目，包括类别平衡、标注方案、验证集划分以及农场或场地报告，仍不够完整。方法学可信度有限：AMSTAR 2将95篇综述评为极低质量，17篇评为中等质量；ROBIS将78篇评为高偏倚风险，17篇评为不明确，17篇评为低偏倚风险。语料库层面的候选参考文献重叠估计值为0.122%，仅作为敏感性估计保留，而非正式的一手研究CCA或独立性证据。基于综述推导的性能与就绪度分级在328个分配中识别出平均描述性性能-部署差距为0.63。总体而言，所报告的性能持续超过已记录的农场部署证据，凸显了在支持常规部署声明之前，需要更强的外部验证、透明的基准测试、工作流程整合、经济评估和用户就绪度证据。","Computers and Electronics in Agriculture","2026-09-09T00:00:00Z",87,{"impact":137,"substance":355,"depth":356,"authority":45,"freshness":20,"relevant":21,"comment":357},23,19,"基于112篇综述的伞式综述，系统揭示计算机视觉在牛猪禽精准养殖中性能与落地之间的差距，方法学与证据分级扎实，对智慧畜牧研发与部署具有较高参考价值。",[359],{"name":351,"url":348},[26,144,361,29,362],"计算机视觉","智能养殖",{"code":364,"slug":365,"name":366,"path":367,"parent_code":36},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry",[369,373],{"code":158,"slug":370,"name":371,"path":372,"parent_code":208},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":375},{"candidates":376,"agricultural_evidence":46},[377,382,386,391,395,399,406],{"code":158,"score":356,"supported":46,"title":378,"abstract":380,"tags":381},[379],"computer vision",[361,379,225],[144,361],{"code":364,"score":45,"supported":46,"title":383,"abstract":384,"tags":385},[365],[366,365],[],{"code":36,"score":387,"supported":46,"title":388,"abstract":389,"tags":390},11,[48],[50],[],{"code":32,"score":387,"supported":46,"title":392,"abstract":393,"tags":394},[154],[154],[],{"code":65,"score":94,"supported":46,"title":396,"abstract":397,"tags":398},[],[29],[29],{"code":400,"score":401,"supported":46,"title":402,"abstract":403,"tags":405},"I7",3,[],[404],"文献计量",[],{"code":100,"score":21,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[26],[411,412],"农业人工智能 计算机视觉 智慧农业 智能养殖","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业智能养殖-2017","10.1016\u002Fj.compag.2026.112419",{"doi":414,"openalex_id":416,"authors":417,"venue":351,"cited_by_count":109,"oa_url":348,"card":428,"direction":121,"ingested_from":123},"W7212062011",[418,420,423,425],{"name":419,"orcid":9},"Alexey Ruchay",{"name":421,"orcid":422},"Vladimir Kolpakov","https:\u002F\u002Forcid.org\u002F0000-0001-9658-7034",{"name":424,"orcid":9},"Hao Guo",{"name":426,"orcid":427},"Andrea Pezzuolo","https:\u002F\u002Forcid.org\u002F0000-0002-9955-9896",{"tldr":429,"method":430,"finding":431,"direction":121,"opportunity":432},"伞式综述112篇综述，梳理牛猪禽精准养殖计算机视觉应用与部署差距。","伞式综述，多标签编码328项，AMSTAR 2与ROBIS评估方法学质量。","模型性能普遍高于农场部署证据，平均性能-部署差距0.63，验证与基准报告不足。","可研究外部验证、标准化基准与农场工作流集成，弥合性能到部署的鸿沟。","2026-09-10T23:30:01.497855Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":9,"content":9,"source_name":439,"source_url":9,"published_at":440,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":441,"score_detail":442,"sources":445,"tags":447,"primary_topic":450,"secondary_topics":451,"classification":453,"search_phrases":471,"slug":474,"view_count":109,"doi":9,"paper":475,"created_at":483},5363,"牛肉牛区域特征计算机视觉分析:基于 YOLO 与深度估计的牛体况自动评分","https:\u002F\u002Fbioengineer.org\u002Fcameras-learn-to-read-a-cows-spine-tail-head-and-body-shape-to-score-its-condition","Islam M.N., Schrick F.N., Edwards J.L., Burns R.T., Qi H., Gan H. (2026) 开发一种基于区域特定计算机视觉特征的牛肉牛体况自动评分(BCS)方法。研究通过 YOLO 检测 + 深度估计提取脊柱、尾根、体廓等区域特征,使用 SHAP 进行可解释 AI 分析,并以 XGBoost 融合。在 5 折交叉验证中两种模式均稳定。验证集仅 72 只动物且极端值样本少,深度图来自推断而非实测。框架可作为后续发展的方法学基础。DOI: 10.1016\u002Fj.atech.2026.102628。","Smart Agricultural Technology Vol.15 Article 102628 2026-10-15","2026-10-15T00:00:00Z",68,{"impact":18,"substance":443,"depth":282,"authority":139,"freshness":19,"relevant":21,"comment":444},20,"方法学上有新意，但验证样本仅72头且深度图为推断值，属细分领域论文进展，未达每日精选门槛。",[446],{"name":439,"url":437},[26,144,361,448,449],"肉牛养殖","体况评分",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[452],{"code":158,"slug":370,"name":371,"path":372,"parent_code":208},{"status":39,"locked":14,"version":40,"method":41,"evidence":454},{"candidates":455,"agricultural_evidence":46},[456,462,467],{"code":158,"score":457,"supported":46,"title":458,"abstract":460,"tags":461},24,[361,459],"YOLO",[361,459],[144,361],{"code":32,"score":18,"supported":46,"title":463,"abstract":465,"tags":466},[464],"肉牛",[464],[448],{"code":100,"score":21,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[26],[472,473],"YOLO 深度估计 牛体况评分","牛肉牛 计算机视觉 BCS","YOLO深度估计牛体况评分-5363",{"doi":9,"openalex_id":9,"authors":476,"venue":9,"cited_by_count":109,"oa_url":9,"card":477,"direction":481,"ingested_from":183},[],{"tldr":478,"method":479,"finding":480,"direction":481,"opportunity":482},"用YOLO检测与深度估计提取牛体区域特征，实现肉牛体况自动评分。","YOLO检测、深度估计、SHAP可解释分析与XGBoost融合，5折交叉验证。","区域特征融合评分稳定，但验证集仅72头且深度图为推断值。","农业遥感与作物表型","可引入实测深度传感器与更大样本，提升极端体况评分鲁棒性。","2026-10-20T02:47:54.361149Z",{"id":485,"title":486,"url":487,"summary":488,"summary_zh":489,"content":9,"source_name":490,"source_url":487,"published_at":491,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":492,"score_detail":493,"sources":495,"tags":497,"primary_topic":501,"secondary_topics":502,"classification":503,"search_phrases":526,"slug":529,"view_count":109,"doi":530,"paper":531,"created_at":563},5017,"Design and Construction of Web-Based and Mobile Cattle Feed Management System and Cattle Health Monitoring System Using the Prototyping Method","https:\u002F\u002Fdoi.org\u002F10.61436\u002Frietm\u002Fv5i4.pp394-406","Livestock management operations are often inefficient and error-prone because agricultural institutions frequently rely on manual recording of cattle feed and health data. This study aims to develop an integrated web and mobile information system for cattle feed management and health monitoring, implemented for the Herbal and Horticultural Science and Technology Park (TSTH2), Pollung, Humbang Hasundutan Regency, North Sumatra, Indonesia. The system was designed using a prototyping methodology through three iterative cycles, each involving stakeholder communication, rapid planning, design, modeling, prototype construction, and user evaluation. The resulting system includes 68 functional components: 24 for feed management and 44 for health monitoring. The web platform was implemented using React.js for the front end, with Express.js serving as the back end for the feed management subsystem and Django REST Framework for the health monitoring subsystem. In contrast, Flutter was used for the mobile platform of both subsystems. Functional testing used black-box testing with a 100% success rate on both devices, and user acceptance testing had a satisfaction score of 76.96% (good\u002Facceptable). This system successfully replaced previously manual steps in rural livestock management, especially in feed scheduling, consumption tracking, and health monitoring. To improve the system's intelligence, future research should add flexible scheduling algorithms and AI-based decision support. Keywords: Information system; Cattle feed management; Cattle health monitoring; Prototyping methodology; Web and mobile information system.","畜牧管理作业往往效率低下且容易出错，因为农业机构通常依赖人工记录牛只饲料与健康数据。本研究旨在开发一套面向牛只饲料管理与健康监测的集成化网页与移动信息系统，并在印度尼西亚北苏门答腊省洪邦哈孙杜坦县波隆的草药与园艺科技园（TSTH2）实施。该系统采用原型化方法设计，经过三个迭代周期，每个周期均包括利益相关者沟通、快速规划、设计、建模、原型构建和用户评估。最终系统包含68个功能组件：24个用于饲料管理，44个用于健康监测。网页平台前端采用React.js实现，饲料管理子系统后端采用Express.js，健康监测子系统后端采用Django REST Framework。而两个子系统的移动平台均采用Flutter。功能测试采用黑盒测试，在两个设备上均达到100%的成功率，用户验收测试的满意度得分为76.96%（良好\u002F可接受）。该系统成功替代了农村畜牧管理中此前的人工操作步骤，尤其是在饲料调度、消耗量追踪和健康监测方面。为提高系统的智能化水平，未来研究应增加灵活调度算法和基于人工智能的决策支持。关键词：信息系统；牛只饲料管理；牛只健康监测；原型化方法；网页与移动信息系统。","Research in Education Technology and Multiculture","2026-10-14T00:00:00Z",53,{"impact":17,"substance":138,"depth":45,"authority":239,"freshness":17,"relevant":21,"comment":494},"印尼农村养牛饲料与健康监测系统的原型开发论文，方法完整、数据具体，但属区域性应用研究，公共影响有限。",[496],{"name":490,"url":487},[26,201,498,499,500],"健康监测","移动应用","农业信息系统",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":504},{"candidates":505,"agricultural_evidence":46},[506,510,514,518,522],{"code":32,"score":387,"supported":46,"title":507,"abstract":508,"tags":509},[154],[154],[],{"code":36,"score":19,"supported":46,"title":511,"abstract":512,"tags":513},[],[50,48],[],{"code":79,"score":401,"supported":46,"title":515,"abstract":516,"tags":517},[],[82],[],{"code":158,"score":401,"supported":46,"title":519,"abstract":520,"tags":521},[],[225],[],{"code":100,"score":21,"supported":14,"title":523,"abstract":524,"tags":525},[],[],[26],[527,528],"TSTH2 北苏门答腊 养牛系统","农业信息系统 健康监测 智慧农业 畜牧养殖","TSTH2北苏门答腊养牛系统-5017","10.61436\u002Frietm\u002Fv5i4.pp394-406",{"doi":530,"openalex_id":532,"authors":533,"venue":490,"cited_by_count":109,"oa_url":487,"card":557,"direction":181,"ingested_from":123},"W7221767999",[534,537,539,542,544,546,548,550,552,554],{"name":535,"orcid":536},"Hernawati Susanti Samosir","https:\u002F\u002Forcid.org\u002F0000-0002-8036-292X",{"name":538,"orcid":9},"Cynthia Deborah Nababan",{"name":540,"orcid":541},"Bella Wahmilyana Asril","https:\u002F\u002Forcid.org\u002F0009-0006-0953-6989",{"name":543,"orcid":9},"Tantri Harianti Silaen",{"name":545,"orcid":9},"Oktavia Letisya Simatupang",{"name":547,"orcid":9},"Keren Simanjuntak",{"name":549,"orcid":9},"Christian Jhon Pranata Panjaitan",{"name":551,"orcid":9},"Hasan Sinaga",{"name":553,"orcid":9},"Nita Herlinda Kurnyawati Simangunsong",{"name":555,"orcid":556},"Sahat Pandapotan Nainggolan","https:\u002F\u002Forcid.org\u002F0000-0001-6316-596X",{"tldr":558,"method":559,"finding":560,"direction":561,"opportunity":562},"开发基于Web和移动端的牛饲料管理与健康监测系统，替代人工记录。","原型法迭代开发，React\u002FExpress\u002FDjango\u002FFlutter，黑盒测","系统含68个功能组件，功能测试100%通过，用户满意度76.96%。","数字乡村与农业信息化","可引入灵活调度算法与AI决策支持，提升系统智能化与预测能力。","2026-10-15T23:30:55.216274Z"]