[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3969":3,"related-3969":84},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":83},3969,"Fusion of multi-branch attention mechanism and hybrid graph convolutional network for accurate cow behavior recognition","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112485","Fusion of multi-branch attention mechanism and hybrid graph convolutional network for accurate cow behavior recognition。Computers and Electronics in Agriculture","融合多分支注意力机制与混合图卷积网络的奶牛行为精准识别",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,9,1,"该论文提出多分支注意力与混合图卷积网络融合方法用于奶牛行为识别，方法新颖、发表于农业信息领域核心期刊，对智慧畜牧有参考价值，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","畜牧养殖","行为识别",{"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},[44,51,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},3,true,[],[49],"奶牛",[],{"code":52,"score":22,"supported":14,"title":53,"abstract":54,"tags":55},"I1",[],[],[27],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I2",[],[],[28],[62,63],"奶牛行为识别 图卷积网络","Computers and Electronics in Agriculture 奶牛","奶牛行为识别图卷积网络-3969",6,"10.1016\u002Fj.compag.2026.112485",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":75,"oa_url":9,"card":76,"direction":80,"ingested_from":82},"W7215066466",[70,72],{"name":71,"orcid":9},"Yang Liu",{"name":73,"orcid":74},"Lei Liu","https:\u002F\u002Forcid.org\u002F0009-0000-0312-0815",0,{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"提出融合多分支注意力与混合图卷积网络，实现奶牛行为精准识别。","多分支注意力机制与混合图卷积网络，用于奶牛行为识别。","该融合模型能有效提升奶牛行为识别准确率。","农业人工智能与决策模型","可探索轻量化图卷积模型在边缘设备上的实时奶牛行为监测与健康预警。","openalex","2026-10-02T23:30:02.089949Z",{"total":65,"page":22,"page_size":65,"items":85},[86,157,218,312,364,446],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":98,"tags":100,"primary_topic":103,"secondary_topics":108,"classification":110,"search_phrases":130,"slug":133,"view_count":22,"doi":134,"paper":135,"created_at":156},4975,"Detection of mounting behavior and mounted ewes in group-housed environments using HSM-YOLO algorithm","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102639","The detection of mounting behavior and the mounted ewe is critical for estrus monitoring in sheep. However, existing methods suffer from low detection efficiency and high false detection rates due to the similarity between non-sexual and sexual mounting behaviors and the complexity of group-housed environments. To address these challenges, this study introduced a fine-grained classification of mounting behaviors into sexual and non-sexual categories, and further proposed an improved detection algorithm named Hu Sheep Mounting-YOLO (HSM-YOLO) based on the YOLOv11n architecture for detecting sheep mounting behavior and the corresponding mounted ewes. The HSM-YOLO algorithm employed depthwise separable convolution (DWSConv) to reduce the model complexity. To improve behavioral feature extraction and pose discrimination capabilities, it further integrated the C3k2_RepViTBlock-RepBottleneck (C3k2_BiRep) module, an efficient multi-scale attention (EMA) module, and the Inner-SIoU loss function. To evaluate the performance of the proposed algorithm, comparative experiments were conducted on a multiple scenarios dataset. The experimental results showed that the HSM-YOLO model achieved an F1 score of 88.1%, a mean Average Precision (mAP) of 90.2%, with 2.2M parameters and 5.4G floating-point operations (FLOPs). Compared to the baseline YOLOv11n model, HSM-YOLO improved the F1 score by 5.8% and the mAP by 5.7%, while reducing both model parameters and computational complexity. When deployed and tested on an RTX 4090, the system delivered an average detection speed of 47.9 frames per second (FPS). Moreover, despite considerable variation in detection performance across the four different imaging scenarios, HSM-YOLO consistently demonstrated superior detection accuracy. These results confirm that the HSM-YOLO provides a robust and efficient solution for detecting sheep mounting behaviors and mounted ewes in complex and variable scenarios, paving the way for the development of automated estrus monitoring systems.","检测羊只的爬跨行为及被爬跨母羊对于绵羊发情监测至关重要。然而，由于非性爬跨行为与性爬跨行为之间的相似性以及群养环境的复杂性，现有方法存在检测效率低和误检率高的问题。为解决这些挑战，本研究将爬跨行为细粒度分类为性爬跨和非性爬跨两类，并进一步提出了一种基于YOLOv11n架构的改进检测算法——湖羊爬跨-YOLO（HSM-YOLO），用于检测绵羊爬跨行为及相应的被爬跨母羊。HSM-YOLO算法采用深度可分离卷积（DWSConv）以降低模型复杂度。为提高行为特征提取和姿态判别能力，该算法进一步集成了C3k2_RepViTBlock-RepBottleneck（C3k2_BiRep）模块、高效多尺度注意力（EMA）模块以及Inner-SIoU损失函数。为评估所提算法的性能，在多场景数据集上进行了对比实验。实验结果表明，HSM-YOLO模型的F1分数达到88.1%，平均精度均值（mAP）达到90.2%，参数量为2.2M，浮点运算量为5.4G。与基线YOLOv11n模型相比，HSM-YOLO的F1分数提高了5.8%，mAP提高了5.7%，同时降低了模型参数量和计算复杂度。在RTX 4090上部署测试时，系统平均检测速度达到47.9帧每秒（FPS）。此外，尽管在四种不同成像场景下检测性能存在较大差异，HSM-YOLO始终展现出优越的检测精度。这些结果证实，HSM-YOLO为复杂多变场景下检测绵羊爬跨行为和被爬跨母羊提供了一种鲁棒且高效的解决方案，为自动化发情监测系统的开发奠定了基础。","Smart Agricultural Technology","2026-10-12T00:00:00Z",81,{"impact":19,"substance":96,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":97},22,"提出HSM-YOLO算法实现羊只爬跨行为与受体母羊检测，为自动化发情监测提供高效方案，方法新颖、指标扎实，具行业参考价值。",[99],{"name":92,"url":89},[27,28,101,29,102],"目标检测","羊只发情监测",{"code":57,"slug":104,"name":105,"path":106,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[109],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,119,126],{"code":57,"score":114,"supported":46,"title":115,"abstract":117,"tags":118},13,[116],"YOLO",[116],[28,101],{"code":32,"score":21,"supported":46,"title":120,"abstract":121,"tags":125},[],[122,123,124],"湖羊","绵羊","sheep",[],{"code":52,"score":22,"supported":14,"title":127,"abstract":128,"tags":129},[],[],[27],[131,132],"HSM-YOLO 羊只爬跨行为 检测","绵羊 发情监测 目标检测","HSM-YOLO羊只爬跨行为检测-4975","10.1016\u002Fj.atech.2026.102639",{"doi":134,"openalex_id":136,"authors":137,"venue":92,"cited_by_count":75,"oa_url":89,"card":151,"direction":80,"ingested_from":82},"W7221573347",[138,140,142,144,147,149],{"name":139,"orcid":9},"Yanchao Wang",{"name":141,"orcid":9},"Ming Gao",{"name":143,"orcid":9},"Zhixin Hua",{"name":145,"orcid":146},"Asim Shakeel","https:\u002F\u002Forcid.org\u002F0000-0003-2942-9081",{"name":148,"orcid":9},"Chengyu Ren",{"name":150,"orcid":9},"Kaiying Wang",{"tldr":152,"method":153,"finding":154,"direction":80,"opportunity":155},"提出HSM-YOLO算法，在群养环境下检测羊爬跨行为及被爬跨母羊，用于发情监测。","基于YOLOv11n改进，引入DWSConv、C3k2_BiRep、EMA和In","F1达88.1%、mAP达90.2%，参数量2.2M，较基线提升且更轻量，检测速度47.9 FPS。","可延伸至多物种发情行为细粒度识别与轻量化边缘部署，结合时序信息提升监测实时性。","2026-10-15T23:30:03.393878Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":9,"content":9,"source_name":92,"source_url":160,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":168,"tags":170,"primary_topic":173,"secondary_topics":174,"classification":175,"search_phrases":197,"slug":200,"view_count":201,"doi":202,"paper":203,"created_at":217},3604,"HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102587","HEART-Net: An ultra-lightweight multimodal neural network for on-device cattle live-weight estimation and precision phytogenic dosing。Smart Agricultural Technology","2026-09-25T00:00:00Z",77,{"impact":19,"substance":165,"depth":19,"authority":114,"freshness":166,"relevant":22,"comment":167},20,8,"提出超轻量多模态网络实现端侧肉牛活重估计与精准植物源饲料投喂，方法新颖且面向实际养殖场景，具备智慧畜牧领域参考价值。",[169],{"name":92,"url":160},[27,28,171,29,172],"智能装备","精准饲喂",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":176},{"candidates":177,"agricultural_evidence":46},[178,184,189,193],{"code":32,"score":179,"supported":46,"title":180,"abstract":182,"tags":183},11,[181],"cattle",[181],[],{"code":185,"score":22,"supported":14,"title":186,"abstract":187,"tags":188},"F5",[],[],[171],{"code":52,"score":22,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[27],{"code":57,"score":22,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[28],[198,199],"HEART-Net 肉牛 体重估计","肉牛 活重估计 植物源饲料","HEART-Net肉牛体重估计-3604",2,"10.1016\u002Fj.atech.2026.102587",{"doi":202,"openalex_id":204,"authors":205,"venue":92,"cited_by_count":75,"oa_url":160,"card":9,"direction":9,"ingested_from":82},"W7214389961",[206,209,211,213,215],{"name":207,"orcid":208},"Ashif Shuvo","https:\u002F\u002Forcid.org\u002F0009-0003-5734-1519",{"name":210,"orcid":9},"Sakibul Hasan",{"name":212,"orcid":9},"Abir Anjum",{"name":214,"orcid":9},"Asaduzzaman Shahed",{"name":216,"orcid":9},"Mohammad Al-Mamun","2026-09-27T23:30:05.905976Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":9,"source_name":224,"source_url":221,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":229,"tags":231,"primary_topic":234,"secondary_topics":235,"classification":241,"search_phrases":274,"slug":277,"view_count":278,"doi":279,"paper":280,"created_at":311},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":166,"substance":19,"depth":17,"authority":13,"freshness":13,"relevant":22,"comment":228},"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。",[230],{"name":224,"url":221},[232,27,28,233,29],"数字农业","农业物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[236],{"code":237,"slug":238,"name":239,"path":240,"parent_code":107},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":242},{"candidates":243,"agricultural_evidence":46},[244,251,257,264,270],{"code":237,"score":19,"supported":46,"title":245,"abstract":247,"tags":250},[246],"IoT",[248,249,246],"物联网","internet of things",[233],{"code":32,"score":20,"supported":46,"title":252,"abstract":254,"tags":256},[253],"goat",[255,253],"山羊",[],{"code":57,"score":258,"supported":46,"title":259,"abstract":260,"tags":263},7,[],[261,262],"artificial intelligence","decision support",[28],{"code":36,"score":65,"supported":46,"title":265,"abstract":266,"tags":269},[],[267,268],"畜牧","livestock",[],{"code":52,"score":201,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[232,27],[275,276],"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":279,"openalex_id":281,"authors":282,"venue":224,"cited_by_count":75,"oa_url":221,"card":305,"direction":309,"ingested_from":82},"W7214075234",[283,285,287,289,291,293,296,299,301,303],{"name":284,"orcid":9},"Nicholas Renaldo",{"name":286,"orcid":9},"Sulaiman Musa",{"name":288,"orcid":9},"Jaswar Koto",{"name":290,"orcid":9},"Kristy Veronica",{"name":292,"orcid":9},"Umar Faruq",{"name":294,"orcid":295},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":297,"orcid":298},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":300,"orcid":9},"Wilda Susanti",{"name":302,"orcid":9},"Achmad Tavip Junaedi",{"name":304,"orcid":9},"Nabila Wahid",{"tldr":306,"method":307,"finding":308,"direction":309,"opportunity":310},"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。","智慧农业 \u002F 农业物联网","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":313,"title":314,"url":315,"summary":316,"summary_zh":9,"content":9,"source_name":317,"source_url":9,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":324,"tags":326,"primary_topic":330,"secondary_topics":331,"classification":333,"search_phrases":350,"slug":353,"view_count":75,"doi":9,"paper":354,"created_at":363},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":321,"substance":165,"depth":322,"authority":114,"freshness":65,"relevant":22,"comment":323},12,17,"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。",[325],{"name":317,"url":315},[27,28,327,328,329],"计算机视觉","肉牛养殖","体况评分",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[332],{"code":57,"slug":104,"name":105,"path":106,"parent_code":107},{"status":39,"locked":14,"version":40,"method":41,"evidence":334},{"candidates":335,"agricultural_evidence":46},[336,341,346],{"code":57,"score":337,"supported":46,"title":338,"abstract":339,"tags":340},24,[327,116],[327,116],[28,327],{"code":32,"score":321,"supported":46,"title":342,"abstract":344,"tags":345},[343],"肉牛",[343],[328],{"code":52,"score":22,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[27],[351,352],"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":355,"venue":9,"cited_by_count":75,"oa_url":9,"card":356,"direction":360,"ingested_from":362},[],{"tldr":357,"method":358,"finding":359,"direction":360,"opportunity":361},"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。","agent","2026-10-20T02:47:54.361149Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":9,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":375,"tags":377,"primary_topic":381,"secondary_topics":382,"classification":383,"search_phrases":408,"slug":411,"view_count":75,"doi":412,"paper":413,"created_at":445},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":166,"substance":19,"depth":20,"authority":278,"freshness":166,"relevant":22,"comment":374},"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。",[376],{"name":370,"url":367},[27,29,378,379,380],"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":384},{"candidates":385,"agricultural_evidence":46},[386,390,394,400,404],{"code":32,"score":179,"supported":46,"title":387,"abstract":388,"tags":389},[181],[181],[],{"code":36,"score":65,"supported":46,"title":391,"abstract":392,"tags":393},[],[267,268],[],{"code":395,"score":45,"supported":46,"title":396,"abstract":397,"tags":399},"C4",[],[398],"饲料",[],{"code":57,"score":45,"supported":46,"title":401,"abstract":402,"tags":403},[],[262],[],{"code":52,"score":22,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[27],[409,410],"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":412,"openalex_id":414,"authors":415,"venue":370,"cited_by_count":75,"oa_url":367,"card":439,"direction":80,"ingested_from":82},"W7221767999",[416,419,421,424,426,428,430,432,434,436],{"name":417,"orcid":418},"Hernawati Susanti Samosir","https:\u002F\u002Forcid.org\u002F0000-0002-8036-292X",{"name":420,"orcid":9},"Cynthia Deborah Nababan",{"name":422,"orcid":423},"Bella Wahmilyana Asril","https:\u002F\u002Forcid.org\u002F0009-0006-0953-6989",{"name":425,"orcid":9},"Tantri Harianti Silaen",{"name":427,"orcid":9},"Oktavia Letisya Simatupang",{"name":429,"orcid":9},"Keren Simanjuntak",{"name":431,"orcid":9},"Christian Jhon Pranata Panjaitan",{"name":433,"orcid":9},"Hasan Sinaga",{"name":435,"orcid":9},"Nita Herlinda Kurnyawati Simangunsong",{"name":437,"orcid":438},"Sahat Pandapotan Nainggolan","https:\u002F\u002Forcid.org\u002F0000-0001-6316-596X",{"tldr":440,"method":441,"finding":442,"direction":443,"opportunity":444},"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",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":454,"score_detail":455,"sources":457,"tags":459,"primary_topic":463,"secondary_topics":464,"classification":466,"search_phrases":512,"slug":515,"view_count":278,"doi":516,"paper":517,"created_at":535},4422,"Artificial intelligence driven framework for enhancing sustainability and resilience in communal cattle insurance in Lupane District Zimbabwe","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44279-026-00786-y","Communal cattle farming in Sub-Saharan Africa continues to face destabilization from non-linear climate variability, yet insurance penetration remains negligible. In Lupane District, Zimbabwe, uptake is exceptionally low at approximately 0.01%, despite recurrent droughts that threaten household livelihoods. This study aimed to develop an AI-driven framework to enhance sustainability and resilience in communal cattle insurance within the district. A positivist research philosophy guided the study, and a quantitative approach was adopted. The Kothari sample size determination formula was applied to derive a statistically representative sample of 219 households from a population of 49,841 communal cattle farmers distributed across four villages in 23 wards. Data were collected through KoboCollect, transmitted to secure servers, and analyzed using JAMOVI version 2.6.44. Descriptive statistics indicated that the majority of farmers were male (62%), with an average age of 47 years and herd sizes ranging from 3 to 25 cattle. Education levels were generally low, with 54% reporting primary schooling and only 12% having secondary or higher qualifications. Income sources were largely subsistence-based, and 71% of respondents relied on cattle as their primary asset. Binary logistic regression revealed that affordability (β = 1.25, p \u003C 0.01) and awareness (β = 0.99, p \u003C 0.01) were the strongest determinants of adoption, while institutional trust (β = 0.74, p \u003C 0.05) emerged as a significant direct predictor of adoption willingness. The study was theoretically anchored in the Technology Acceptance Model (TAM), which explains how perceived usefulness and perceived ease of use influence uptake. Informed by findings from Objectives 1 and 2, the study proposes the Tri-Modular AI Resilience Engine (TMAIE). TMAIE integrates Convolutional Neural Networks (CNN) for biometric cattle identification, Recurrent Neural Networks (RNN) for satellite-derived NDVI index triggers, and Transformer-based Natural Language Processing (NLP) localized in IsiNdebele and ChiShona for inclusive voice-based access. The study findings suggest that adoption is not a linear process but an adaptive evolution requiring both technological and social mechanisms. The proposed framework reflects these relationships through the integration of technological precision and community-based mediation. Overall, the findings provide empirical evidence on the determinants of cattle insurance adoption and inform the development of a conceptual AI-driven framework that may support future climate-resilient livestock insurance innovations, subject to further validation and testing.","撒哈拉以南非洲的社区养牛业持续面临非线性气候变率的冲击，然而保险渗透率仍然微不足道。在津巴布韦卢潘内区，尽管反复发生的干旱威胁着家庭生计，但参保率仍极低，约为0.01%。本研究旨在开发一个人工智能驱动的框架，以提升该区社区养牛保险的可持续性和韧性。本研究以实证主义研究哲学为指导，采用定量研究方法。应用Kothari样本量确定公式，从分布于23个选区4个村庄的49841名社区养牛户总体中，推导出具有统计代表性的219户样本。数据通过KoboCollect采集，传输至安全服务器，并使用JAMOVI 2.6.44版本进行分析。描述性统计表明，大多数农户为男性（62%），平均年龄47岁，牛群规模为3至25头。教育水平普遍较低，54%报告仅接受过小学教育，仅12%拥有中学或更高学历。收入来源大多以自给自足为基础，71%的受访者将牛作为其主要资产。二元逻辑回归显示，可负担性（β = 1.25，p \u003C 0.01）和认知度（β = 0.99，p \u003C 0.01）是参保的最强决定因素，而制度信任（β = 0.74，p \u003C 0.05）则是参保意愿的显著直接预测因素。本研究在理论上以技术接受模型（TAM）为锚点，该模型解释了感知有用性和感知易用性如何影响采纳行为。基于目标1和目标2的研究结果，本研究提出了三模块人工智能韧性引擎（TMAIE）。TMAIE整合了用于牛只生物特征识别的卷积神经网络（CNN）、用于卫星衍生NDVI指数触发的循环神经网络（RNN），以及基于Transformer的、以恩德贝莱语和绍纳语本地化的自然语言处理（NLP），以实现包容性的语音访问。研究结果表明，采纳并非线性过程，而是一种需要技术和社会机制共同作用的自适应演化。所提出的框架通过整合技术精确性与社区中介来反映这些关系。总体而言，研究结果提供了关于养牛保险采纳决定因素的实证证据，并为","Discover Agriculture","2026-10-06T00:00:00Z",70,{"impact":321,"substance":18,"depth":322,"authority":321,"freshness":166,"relevant":22,"comment":456},"以津巴布韦Lupane地区社区养牛保险为对象，提出融合CNN牛只识别、NDVI遥感触发与本地语言NLP的AI框架，方法具体、数据扎实，但属区域性案例研究，对国内农业信息化有借鉴意义而非直接影响。",[458],{"name":452,"url":449},[460,27,28,461,462],"数字乡村","农业保险","遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[465],{"code":57,"slug":104,"name":105,"path":106,"parent_code":107},{"status":39,"locked":14,"version":40,"method":41,"evidence":467},{"candidates":468,"agricultural_evidence":46},[469,473,477,481,487,493,498,502,507],{"code":32,"score":179,"supported":46,"title":470,"abstract":471,"tags":472},[181],[181],[],{"code":57,"score":21,"supported":46,"title":474,"abstract":475,"tags":476},[261],[],[28],{"code":36,"score":45,"supported":46,"title":478,"abstract":479,"tags":480},[],[268],[],{"code":482,"score":45,"supported":46,"title":483,"abstract":484,"tags":486},"H7",[],[485],"干旱",[],{"code":488,"score":45,"supported":46,"title":489,"abstract":490,"tags":492},"I7",[],[491],"研究方法",[],{"code":494,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},"G3",[],[],[461],{"code":52,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[27],{"code":503,"score":22,"supported":14,"title":504,"abstract":505,"tags":506},"I3",[],[],[462],{"code":508,"score":22,"supported":14,"title":509,"abstract":510,"tags":511},"I6",[],[],[460],[513,514],"津巴布韦 Lupane 牛保险","AI 牲畜保险 框架","津巴布韦Lupane牛保险-4422","10.1007\u002Fs44279-026-00786-y",{"doi":516,"openalex_id":518,"authors":519,"venue":452,"cited_by_count":75,"oa_url":529,"card":530,"direction":309,"ingested_from":82},"W7220655164",[520,523,526],{"name":521,"orcid":522},"Nyasha Nyakuchena","https:\u002F\u002Forcid.org\u002F0009-0005-3219-2275",{"name":524,"orcid":525},"Joseph P. Musara","https:\u002F\u002Forcid.org\u002F0000-0002-2226-3616",{"name":527,"orcid":528},"Emmanuel Zivenge","https:\u002F\u002Forcid.org\u002F0000-0003-4740-8225","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44279-026-00786-y.pdf",{"tldr":531,"method":532,"finding":533,"direction":443,"opportunity":534},"研究提出AI驱动的三模块框架，以提升津巴布韦Lupane地区社区牛保险的可持续性和韧性。","对219户农户进行问卷调查，用二元逻辑回归分析，并设计CNN、RNN和Trans","可负担性和意识是保险采纳的最强决定因素，机构信任也显著影响采纳意愿。","可验证TMAIE框架在类似地区的适用性，并探索AI语音交互对低教育水平农户保险采纳的长期影响。","2026-10-08T23:30:11.018464Z"]