[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2746":3,"related-2746":107},{"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":44,"search_phrases":84,"slug":87,"view_count":88,"doi":89,"paper":90,"created_at":106},2746,"Fostering artificial intelligence in the livestock sector: Adoption factors and policy insights from the case of Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104956","CONTEXT AND PURPOSE The integration of Artificial Intelligence (AI) and Precision Livestock Farming (PLF) offers significant opportunities to improve the productivity, animal welfare and sustainability of the livestock sector. However, the adoption of these technologies remains highly uneven. Moving beyond descriptive statistics, this study investigates the complex, interacting factors associated with AI adoption in the Spanish livestock sector. METHODOLOGY Utilising a survey-based sample of 666 Spanish livestock holdings, drawn from a national survey whose sampling was designed to be proportional to the 2020 Agricultural Census by subsector and region, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was employed to evaluate a comprehensive adoption framework across different farming systems and operational scales. AI adoption was modelled as a formative composite of five self-reported AI system categories, and the framework was tested through mediation, moderation, subgroup and out-of-sample predictive analyses with a 10,000-resample bootstrap. MAIN FINDINGS The empirical results indicate that AI adoption is most strongly associated with a holding's baseline technological infrastructure (β = 0.6464, p \u003C 0.001); advanced predictive tools appear to presuppose a pre-existing ecosystem of digital farm books, automated feeding systems and interoperable sensors. A ‘perception-capability gap’ is formally supported: perceived benefits are associated with adoption only indirectly, through the infrastructure baseline (indirect-only mediation), and their association strengthens as infrastructure increases (positive interaction). Recognition of AI's theoretical benefits or awareness of public subsidies is thus not independently associated with adoption in the absence of foundational digital capability. Furthermore, the study identifies a stark structural divide: adoption reaches 56.3% in intensive and semi-intensive holdings but only 24.4% in extensive ones (χ2 = 57.61, p \u003C 0.001), as traditional extensive systems face severe physical and connectivity barriers that leave them systematically disadvantaged. Results are robust across reflective, count, reduced-indicator and logistic specifications, and the model shows substantial out-of-sample predictive power (Q 2 predict ≈ 0.53). CONCLUSIONS To facilitate a more inclusive digital transformation, the findings suggest that policymakers should adopt a ‘readiness-conditional’ model, ensuring farms meet baseline digital prerequisites and have access to robust extension services before subsidising advanced AI tools.","背景与目的 人工智能（Artificial Intelligence, AI）与精准畜牧业（Precision Livestock Farming, PLF）的融合为提升畜牧部门的生产力、动物福利和可持续性提供了重要机遇。然而，这些技术的采用仍然极不均衡。本研究超越描述性统计，探讨了西班牙畜牧部门中与AI采用相关的复杂交互因素。方法 基于一项全国性调查中抽取的666个西班牙畜牧养殖场的样本（该调查的抽样设计按2020年农业普查的子部门和地区比例进行），采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modelling, PLS-SEM）方法，评估了一个跨不同养殖系统和经营规模的综合采用框架。AI采用被建模为五个自我报告的AI系统类别的形成性复合变量，并通过中介、调节、子组和样本外预测分析（10,000次重抽样自助法）对框架进行了检验。主要发现 实证结果表明，AI采用与养殖场的基线技术基础设施关联最为密切（β = 0.6464，p \u003C 0.001）；先进的预测工具似乎以已有的数字牧场记录、自动化饲喂系统和可互操作传感器生态系统为前提。“感知—能力差距”得到正式支持：感知收益仅通过基础设施基线间接与采用相关（仅间接中介），且其关联随基础设施的增加而增强（正向交互）。因此，在缺乏基础数字能力的情况下，对AI理论效益的认可或对公共补贴的知晓与采用并无独立关联。此外，研究识别出一条鲜明的结构性分界线：集约化和半集约化养殖场的采用率达到56.3%，而粗放型养殖场仅为24.4%（χ2 = 57.61，p \u003C 0.001），因为传统粗放型系统面临严重的物理和连接障碍，使其处于系统性劣势。结果在反映性、计数、简化指标和逻辑斯谛设定下均稳健，且模型显示出显著的样本外预测能力（Q 2 predict ≈ 0.53）。结论 为促进更具包容性的数字化转型，研究结果表明政策制定者应采取“就绪条件”模型，确保养殖场",null,"Agricultural Systems","2026-09-16T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"基于666个西班牙牧场样本的实证研究，揭示AI采纳受数字基础设施制约的“感知—能力鸿沟”，并提出“就绪条件式”补贴政策思路，对智慧畜牧推广与数字乡村政策设计有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","数字素养","农业补贴","精准畜牧",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","C",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,57,63,69,74,79],{"code":43,"score":19,"supported":51,"title":52,"abstract":54,"tags":56},true,[53],"livestock",[55,53],"畜牧",[],{"code":32,"score":58,"supported":51,"title":59,"abstract":61,"tags":62},12,[60],"artificial intelligence",[60],[27],{"code":39,"score":13,"supported":51,"title":64,"abstract":65,"tags":68},[],[30,66,67],"动物福利","animal welfare",[30],{"code":70,"score":21,"supported":14,"title":71,"abstract":72,"tags":73},"G3",[],[],[29],{"code":75,"score":21,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[26],{"code":80,"score":21,"supported":14,"title":81,"abstract":82,"tags":83},"I6",[],[],[28],[85,86],"农业人工智能 农业补贴 数字素养 智慧农业","农业人工智能 农业补贴","农业人工智能农业补贴数字素养智慧农业-2746",5,"10.1016\u002Fj.agsy.2026.104956",{"doi":89,"openalex_id":91,"authors":92,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":103,"ingested_from":105},"W7213425399",[93,95],{"name":94,"orcid":9},"Carlos Parra-Lopez",{"name":96,"orcid":97},"Carmen Carmona‐Torres","https:\u002F\u002Forcid.org\u002F0000-0002-6982-1363",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"基于西班牙666家养殖场调查，用PLS-SEM分析AI采纳的影响因素与政策启示。","666份养殖场问卷，PLS-SEM建模，含中介、调节与分组分析。","AI采纳最依赖既有数字基础设施；感知收益仅间接起作用，集约场采纳率远高于粗放场。","数字乡村与农业信息化","可研究粗放养殖场数字基础设施与连接性短板，设计‘就绪度条件’式推广与补贴机制。","openalex","2026-09-17T23:30:04.844104Z",{"total":108,"page":21,"page_size":108,"items":109},6,[110,173,276,343,434,511],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":123,"tags":125,"primary_topic":127,"secondary_topics":128,"classification":130,"search_phrases":151,"slug":154,"view_count":21,"doi":155,"paper":156,"created_at":172},4426,"Animal welfare in the world of digitalization: Computer Vision in the context of foundational structures","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43681-026-01417-4","Abstract Computer Vision is an emerging technology within Precision Livestock Farming (PLF), combining camera systems with artificial intelligence to monitor animal welfare. While animal ethicists recognize its potential benefits, they also raise concerns about possible negative implications for animal welfare. This paper argues that addressing ethical concerns cannot rely on solutions targeting isolated ethical risks; rather, potential solutions must account for the technology’s embeddedness within society-wide structures. The focus in this paper will be on foundational structures, these structures concern how the world is experienced and understood, what is taken as self-evident. Over centuries, humans have increasingly delegated tasks to machines and standardized the world in doing so. This process has fostered growing trust in the outcomes of (digital) technologies. These developments have changed human experience and understanding of the world in various ways. Farmers, as members of society, are unlikely to be exempt from this trend. Therefore, reflection on Computer Vision must go beyond the farming practice and take into account these foundational structures that are currently reshaping society at large. Computer Vision examined as embedded within foundational structures demonstrates that ethical issues are also embedded within these structures, consequently, potential solutions must take account of them to increase their effectiveness.","摘要 计算机视觉是精准畜牧业（PLF）中的一项新兴技术，它将摄像系统与人工智能相结合，以监测动物福利。尽管动物伦理学家认可其潜在益处，但也对其可能给动物福利带来的负面影响表示担忧。本文认为，解决伦理关切不能依赖于针对孤立伦理风险的方案；相反，潜在的解决方案必须考虑到该技术嵌入于全社会结构之中的事实。本文的关注重点将是基础性结构，这些结构关乎世界如何被体验和理解，以及什么被视为不言自明。几个世纪以来，人类越来越多地将任务委托给机器，并在此过程中使世界标准化。这一进程培育了人们对（数字）技术产物的日益信任。这些发展以多种方式改变了人类对世界的体验和理解。农民作为社会成员，不太可能置身于这一趋势之外。因此，对计算机视觉的反思必须超越养殖实践，并考虑到这些当前正在重塑整个社会的基础性结构。将计算机视觉置于基础性结构中加以审视表明，伦理问题同样嵌入于这些结构之中，因此，潜在的解决方案必须将其纳入考量，以提高其有效性。","AI and Ethics","2026-10-06T00:00:00Z",72,{"impact":58,"substance":120,"depth":17,"authority":19,"freshness":121,"relevant":21,"comment":122},20,8,"从社会基础结构层面反思计算机视觉在精准畜牧中的伦理嵌入，视角新颖、论证扎实，对智慧农业伦理治理有参考价值。",[124],{"name":116,"url":113},[26,27,126,66,30],"计算机视觉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[129],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":131},{"candidates":132,"agricultural_evidence":51},[133,138,143,147],{"code":39,"score":134,"supported":51,"title":135,"abstract":136,"tags":137},19,[67],[30,66,67],[66,30],{"code":32,"score":134,"supported":51,"title":139,"abstract":141,"tags":142},[140],"computer vision",[126,60,140],[27,126],{"code":43,"score":108,"supported":51,"title":144,"abstract":145,"tags":146},[],[55,53],[],{"code":75,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[26],[152,153],"计算机视觉 精准畜牧 动物福利","农业人工智能 计算机视觉 动物福利 智慧农业","计算机视觉精准畜牧动物福利-4426","10.1007\u002Fs43681-026-01417-4",{"doi":155,"openalex_id":157,"authors":158,"venue":116,"cited_by_count":98,"oa_url":165,"card":166,"direction":170,"ingested_from":105},"W7220695950",[159,162],{"name":160,"orcid":161},"Mariska Thalitha Bosschaert","https:\u002F\u002Forcid.org\u002F0000-0002-9160-7638",{"name":163,"orcid":164},"Bart Gremmen","https:\u002F\u002Forcid.org\u002F0000-0002-9652-6407","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43681-026-01417-4.pdf",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"论文论证计算机视觉用于动物福利时，伦理问题须置于社会基础结构中理解。","概念分析与伦理反思，结合精准畜牧中计算机视觉技术。","伦理问题嵌入社会基础结构，孤立风险方案无效，需整体考量。","智慧农业 \u002F 农业物联网","可实证研究农民对计算机视觉的信任如何受社会基础结构影响，并设计嵌入伦理的监测系统。","2026-10-08T23:30:12.350082Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":181,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":190,"classification":192,"search_phrases":234,"slug":237,"view_count":238,"doi":239,"paper":240,"created_at":275},3917,"Risk-based prioritization of official animal welfare inspections using machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffvets.2026.1938469","Introduction For official animal welfare inspections in Austria, at least 2% of agricultural livestock farms must be selected and inspected on the basis of a risk-based random sample. In the federal state of Styria, more than 19,000 farms keep livestock, while the inspection capacity of official veterinarians is limited, requiring efficient prioritization of farm checks. This study aimed to develop a machine learning model leveraging routinely collected administrative and animal health data to identify farms in Styria at higher risk of animal welfare violations. Methods The statistical analyses incorporated the outcomes of animal welfare inspections from 2019 to 2023, aggregated by farm and year. A total of 2,647 aggregated inspection results were used to train an Extreme Gradient Boosting (XGBoost) model, with a further 475 results used for testing purposes. Information about farms (e.g., production type, farmer and regional data, livestock data), as well as slaughter and meat inspection data, was acquired from the Consumer Health Information System database ( Verbrauchergesundheitsinformationssystem , VIS). Additionally, data from local fallen stock rendering plants and information on antibiotics dispensed to farmers were available and incorporated into the model. Results Results showed a moderate model performance with AUC (Area under the curve) of 0.75 (95%-CI [0.66, 0.82]), MCC (Matthews Correlation Coefficient) of 0.25 (95%-CI [0.19, 0.37]) and AP (Average Precision) of 0.19 (95%-CI [0.11–0.31]) for the test set. Post-hoc model interpretation revealed as most important influencing features antibiotic dispensing in cattle production, particularly overall quantities and European Medicines Agency Category D antibiotics, followed by farmer-related characteristics such as farmer age and the distance between the farm and the farmer’s residence. Membership in the Animal Health Service ( Tiergesundheitsdienst , TGD) was associated with a decrease in the model-predicted probability of animal welfare violations. Other relevant factors included the number of livestock units (cattle and poultry) on farm, cattle mortality recorded by rendering plants, and certain findings at meat inspection, such as pericarditis in pigs. Discussion These results indicate that routinely recorded administrative and veterinary data can provide meaningful indicators for identifying farms with a higher risk of animal welfare violations. Although some predictors appear indirectly linked to welfare, they likely reflect broader farm management practices and animal health status. Risk-based sampling could support veterinary authorities in prioritizing farms for inspection and allocating inspection resources more efficiently.","引言 在奥地利，官方动物福利检查要求至少对2%的农业牲畜养殖场进行基于风险的随机抽样检查。在施泰尔马克州，超过19,000个养殖场饲养牲畜，而官方兽医的检查能力有限，因此需要高效地确定养殖场检查的优先顺序。本研究旨在开发一种机器学习模型，利用常规收集的行政和动物健康数据，识别施泰尔马克州动物福利违规风险较高的养殖场。方法 统计分析纳入了2019年至2023年动物福利检查的结果，按养殖场和年份汇总。共有2,647条汇总检查结果用于训练极端梯度提升（XGBoost）模型，另有475条结果用于测试。养殖场相关信息（如生产类型、养殖者及区域数据、牲畜数据）以及屠宰和肉类检验数据均获取自消费者健康信息系统数据库（Verbrauchergesundheitsinformationssystem，VIS）。此外，当地病死动物无害化处理厂的数据以及养殖者抗生素发放信息也可获取，并纳入模型。结果 结果显示模型性能中等，测试集的AUC（曲线下面积）为0.75（95%-CI [0.66, 0.82]），MCC（马修斯相关系数）为0.25（95%-CI [0.19, 0.37]），AP（平均精度）为0.19（95%-CI [0.11–0.31]）。事后模型解释显示，最重要的影响因素是养牛生产中的抗生素发放，特别是总体用量和欧洲药品管理局D类抗生素，其次是养殖者相关特征，如养殖者年龄和养殖场与养殖者住所之间的距离。加入动物健康服务（Tiergesundheitsdienst，TGD）与模型预测的动物福利违规概率降低相关。其他相关因素包括养殖场牲畜单位数（牛和家禽）、无害化处理厂记录的牛死亡率，以及肉类检验中的某些发现，如猪心包炎。讨论 这些结果表明，常规记录的行政和兽医数据可为识别动物福利违规风险较高的养殖场提供有意义的指标。尽管一些预测因素似乎间接相关","Frontiers in Veterinary Science","2026-09-30T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":182},"奥地利用XGBoost整合屠宰、抗生素与病死畜数据预测动物福利违规风险，为有限兽医检验资源提供风险分级抽样依据，方法可迁移至我国畜牧监管信息化。",[184],{"name":179,"url":176},[26,186,187,66,188],"机器学习","畜牧兽医","风险分级监管",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[191],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":193},{"candidates":194,"agricultural_evidence":51},[195,200,205,212,217,224,230],{"code":39,"score":196,"supported":51,"title":197,"abstract":198,"tags":199},15,[67],[66,67],[66],{"code":32,"score":196,"supported":51,"title":201,"abstract":203,"tags":204},[202],"machine learning",[186,202],[186],{"code":206,"score":108,"supported":51,"title":207,"abstract":208,"tags":211},"C3",[],[209,210],"家禽","poultry",[],{"code":43,"score":213,"supported":51,"title":214,"abstract":215,"tags":216},4,[],[53],[187],{"code":218,"score":219,"supported":51,"title":220,"abstract":221,"tags":223},"C2",3,[],[222],"cattle",[],{"code":225,"score":219,"supported":51,"title":226,"abstract":227,"tags":229},"C5",[],[228],"veterinary",[],{"code":75,"score":21,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[26],[235,236],"奥地利 施蒂里亚 动物福利 机器学习","XGBoost 动物福利 风险评估","奥地利施蒂里亚动物福利机器学习-3917",2,"10.3389\u002Ffvets.2026.1938469",{"doi":239,"openalex_id":241,"authors":242,"venue":179,"cited_by_count":98,"oa_url":176,"card":269,"direction":273,"ingested_from":105},"W7214951739",[243,245,247,249,251,254,256,258,260,263,266],{"name":244,"orcid":9},"Eva Obenaus",{"name":246,"orcid":9},"Sabrina Kuchling",{"name":248,"orcid":9},"Tadej Zerak",{"name":250,"orcid":9},"Corina Schleicher",{"name":252,"orcid":253},"Ian Kopacka","https:\u002F\u002Forcid.org\u002F0000-0003-4106-1742",{"name":255,"orcid":9},"Sven Budik",{"name":257,"orcid":9},"Sabine Grimme",{"name":259,"orcid":9},"Jörg Hiesel",{"name":261,"orcid":262},"Peter Eckhardt","https:\u002F\u002Forcid.org\u002F0009-0005-5015-4846",{"name":264,"orcid":265},"Johannes Baumgartner","https:\u002F\u002Forcid.org\u002F0000-0001-8555-1378",{"name":267,"orcid":268},"Clair L. Firth","https:\u002F\u002Forcid.org\u002F0000-0002-4919-7786",{"tldr":270,"method":271,"finding":272,"direction":273,"opportunity":274},"用机器学习基于常规行政与兽医数据，对奥地利农场动物福利违规风险进行优先级排序。","XGBoost模型，使用2019-2023年2647条检查结果及VIS等行政兽医","模型AUC 0.75，抗生素发放量、农户年龄和距离等是动物福利违规的重要预测因子。","农业人工智能与决策模型","可探索将常规兽医行政数据与机器学习结合，用于其他农业合规监管的精准抽查与风险预警。","2026-10-01T23:30:54.115236Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":288,"tags":290,"primary_topic":293,"secondary_topics":294,"classification":296,"search_phrases":311,"slug":314,"view_count":219,"doi":315,"paper":316,"created_at":342},2015,"Mitigating artificial intelligence-related animal welfare risks in animal farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112408","Mitigating artificial intelligence-related animal welfare risks in animal farming。Computers and Electronics in Agriculture","减轻畜牧业中与人工智能相关的动物福利风险。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-10T00:00:00Z",79,{"impact":17,"substance":120,"depth":286,"authority":19,"freshness":13,"relevant":21,"comment":287},17,"核心期刊新论文，聚焦AI在畜牧养殖中的动物福利风险与缓解路径，属智慧养殖前沿议题，具行业参考价值。",[289],{"name":282,"url":279},[27,291,292,66],"智慧养殖","风险治理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[295],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":297},{"candidates":298,"agricultural_evidence":51},[299,303,307],{"code":39,"score":196,"supported":51,"title":300,"abstract":301,"tags":302},[67],[66,67],[66],{"code":32,"score":58,"supported":51,"title":304,"abstract":305,"tags":306},[60],[60],[27],{"code":43,"score":219,"supported":51,"title":308,"abstract":309,"tags":310},[],[55],[],[312,313],"农业人工智能 动物福利 智慧养殖 风险治理","农业人工智能 动物福利","农业人工智能动物福利智慧养殖风险治理-2015","10.1016\u002Fj.compag.2026.112408",{"doi":315,"openalex_id":317,"authors":318,"venue":282,"cited_by_count":98,"oa_url":279,"card":337,"direction":170,"ingested_from":105},"W7211988489",[319,322,324,326,328,330,332,334],{"name":320,"orcid":321},"Joshua Jarvis‐Campbell","https:\u002F\u002Forcid.org\u002F0009-0008-7307-703X",{"name":323,"orcid":9},"Marina A.G. von Keyserlingk",{"name":325,"orcid":9},"Albert Didriksen",{"name":327,"orcid":9},"Maximus Rafla",{"name":329,"orcid":9},"Carmen Lopez",{"name":331,"orcid":9},"Jack Zhu",{"name":333,"orcid":9},"Alistair Bugg",{"name":335,"orcid":336},"Borbala Foris","https:\u002F\u002Forcid.org\u002F0000-0002-0901-3057",{"tldr":338,"method":339,"finding":340,"direction":170,"opportunity":341},"探讨如何缓解动物养殖中人工智能应用带来的动物福利风险。","综述分析AI在养殖中的福利风险与缓解策略。","需建立伦理框架与技术措施以降低AI对动物福利的负面影响。","可研究AI养殖中动物福利风险的量化评估与实时监测预警系统。","2026-09-10T23:30:01.332132Z",{"id":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":9,"source_name":349,"source_url":346,"published_at":350,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":355,"tags":357,"primary_topic":360,"secondary_topics":361,"classification":368,"search_phrases":404,"slug":407,"view_count":88,"doi":408,"paper":409,"created_at":433},3469,"ARTIFICIAL INTELLIGENCE FOR AGRICULTURE: A SYSTEMATIC REVIEW OF FARMERS' PERCEPTIONS, ACCEPTANCE, ADOPTION AND BARRIERS","https:\u002F\u002Fdoi.org\u002F10.64013\u002Fbbasrjlifess.v2026i1.70","Artificial intelligence (AI) is rapidly reshaping agriculture, enabling the application of data for efficient decision-making, precision farming, crop monitoring, pest management, disease detection, smart irrigation, yield prediction, and various aspects of farm automation. Yet, to successfully gain a foothold in farms, factors like farmers' perceptions, their acceptance, desire, and readiness to adopt, as well as the capability to surmount socioeconomic, technological, and institutional challenges, must also make a difference. This article systematically collates the available information on farmers' perspectives on, acceptance of, readiness to adopt, and the obstacles related to AI in the agricultural context. The PRISMA 2020 guideline has been followed to select studies that are related, evaluated for inclusion, and finally, per the criteria, combined into a systematic synthesis. The review mainly discusses the aspects that influence the use of AI, like perceived usefulness, ease of use, trust, digital literacy, affordability, farm size, socioeconomic characteristics, availability of digital infrastructure, and access to agricultural technical advisory services. Main challenges identified involve the high cost of implementing AI, poor connectivity, weak rural infrastructure, low levels of technological knowledge, unavailability of support services, concerns about data privacy, distrust of algorithmic bias, language barriers, and inequalities impacting smallholder farmers. Besides barriers, the review indicates the potential of leveraging AI through extension services, agricultural mobile apps, precision farming, climate-smart agriculture, early-warning systems, and tailored farm advisories. By drawing out technical, behavioral, socioeconomic, and institutional perspectives, the review pinpoints important research questions and presents a farmholder-oriented setup to explain the process of taking up AI in farming. The outcomes can be used by scientific experts, policymakers, extension personnel, and software developers to create low-cost, reliable, and accessible AI systems aimed at promoting sustainable agricultural development.","人工智能（AI）正迅速重塑农业，使数据得以应用于高效决策、精准农业、作物监测、病虫害管理、病害检测、智能灌溉、产量预测以及农场自动化的各个方面。然而，要在农场中成功立足，农民的认知、接受度、采用意愿和准备程度，以及克服社会经济、技术和制度挑战的能力，同样至关重要。本文系统梳理了现有关于农民对农业领域人工智能的看法、接受度、采用准备程度及相关障碍的信息。研究遵循PRISMA 2020指南，筛选相关研究，评估其纳入资格，并最终依据标准进行系统性综合。综述主要探讨了影响人工智能使用的因素，包括感知有用性、易用性、信任、数字素养、可负担性、农场规模、社会经济特征、数字基础设施的可用性以及农业技术咨询服务的获取。识别出的主要挑战包括人工智能实施成本高昂、网络连接不佳、农村基础设施薄弱、技术水平低下、支持服务缺乏、数据隐私担忧、对算法偏见的疑虑、语言障碍以及影响小农户的不平等问题。除障碍外，综述还指出了通过推广服务、农业移动应用、精准农业、气候智慧型农业、预警系统和定制化农场咨询来利用人工智能的潜力。通过梳理技术、行为、社会经济和制度层面的视角，本综述指出了重要的研究问题，并提出了一个以农场主为导向的框架，以解释在农业中采用人工智能的过程。研究结果可供科学专家、政策制定者、推广人员和软件开发者用于创建低成本、可靠且可及的人工智能系统，以促进可持续农业发展。","Journal of Life and Social Sciences","2026-09-24T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":353,"freshness":20,"relevant":21,"comment":354},13,"基于PRISMA的系统综述，系统梳理农户对AI的认知、接受度与采纳障碍，对智慧农业推广与政策设计有实质参考价值。",[356],{"name":349,"url":346},[26,27,28,358,359],"农户采纳","小农户",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[362],{"code":363,"slug":364,"name":365,"path":366,"parent_code":367},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":45,"locked":14,"version":46,"method":47,"evidence":369},{"candidates":370,"agricultural_evidence":51},[371,375,380,387,394,400],{"code":32,"score":58,"supported":51,"title":372,"abstract":373,"tags":374},[60],[60],[27],{"code":363,"score":121,"supported":51,"title":376,"abstract":377,"tags":379},[],[359,378],"smallholder",[358,359],{"code":75,"score":381,"supported":51,"title":382,"abstract":383,"tags":386},7,[],[384,385],"精准农业","smart agriculture",[26],{"code":388,"score":108,"supported":51,"title":389,"abstract":390,"tags":393},"H3",[],[391,392],"灌溉","irrigation",[],{"code":395,"score":219,"supported":51,"title":396,"abstract":397,"tags":399},"H8",[],[398],"可持续农业",[],{"code":80,"score":21,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[28],[405,406],"农民 AI 采纳 障碍","农业人工智能 系统综述","农民AI采纳障碍-3469","10.64013\u002Fbbasrjlifess.v2026i1.70",{"doi":408,"openalex_id":410,"authors":411,"venue":349,"cited_by_count":98,"oa_url":346,"card":428,"direction":170,"ingested_from":105},"W7214189410",[412,414,416,418,420,422,424,426],{"name":413,"orcid":9},"MM JAMEEL",{"name":415,"orcid":9},"M SAEED",{"name":417,"orcid":9},"SA SHER",{"name":419,"orcid":9},"Z ALI",{"name":421,"orcid":9},"Q HAYYAT",{"name":423,"orcid":9},"S KIRBAG",{"name":425,"orcid":9},"S KHAN",{"name":427,"orcid":9},"HN AHMAD",{"tldr":429,"method":430,"finding":431,"direction":103,"opportunity":432},"系统综述农民对农业AI的感知、接受度、采纳意愿及障碍。","遵循PRISMA 2020指南，系统筛选并综合相关文献。","成本、基础设施、数字素养与信任是主要障碍，小农户受影响最大。","可研究低成本、本地化AI采纳模型及小农户数字包容机制。","2026-09-25T23:30:09.670568Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":451,"search_phrases":476,"slug":479,"view_count":213,"doi":480,"paper":481,"created_at":510},2511,"Evaluation of hybrid models based on image segmentation and inference for pig weight estimation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1876396","Introduction Accurate weight estimation in pig production is essential for optimizing management, feeding, and commercialization decisions; however, traditional weighing methods are invasive, time-consuming, and prone to operational errors. This study proposes a non-invasive computer vision–based approach to estimate pig weight under real farm conditions in San Martín, Peru. Methods A dataset of 3,800 lateral images paired with their corresponding ground-truth weights was collected. A computational pipeline was implemented, including geometric standardization, instance segmentation using YOLOv8n-seg, and feature extraction through EfficientNet-B0. The resulting embeddings were used as input for supervised regression models (SVR, XGBoost, and CatBoost), evaluated using repeated stratified cross-validation and an independent test set, with MAE, RMSE, and R² as performance metrics. Statistical comparisons were conducted using the Friedman test followed by Wilcoxon post hoc analysis with Holm correction. Results The results demonstrated strong predictive performance, with the SVR model achieving the best results (RMSE = 2.68 kg, MAE = 1.81 kg, R 2 = 0.85), showing statistically significant differences compared to the other models. Discussion These findings indicate that combining computer vision techniques with models capable of capturing non-linear relationships effectively models the relationship between animal morphology and body weight, providing a low-cost, non-invasive solution applicable to real-world production systems and supporting the advancement of precision livestock farming.","引言 在生猪生产中，准确的体重估测对于优化管理、饲喂和商业化决策至关重要；然而，传统称重方法具有侵入性、耗时且易产生操作误差。本研究提出了一种基于非侵入式计算机视觉的方法，用于在秘鲁圣马丁的实际农场条件下估测生猪体重。方法 收集了3，800张侧向图像及其对应的真实体重数据集。实施了计算流程，包括几何标准化、使用YOLOv8n-seg进行实例分割，以及通过EfficientNet-B0进行特征提取。所得嵌入向量被用作监督回归模型（SVR、XGBoost和CatBoost）的输入，采用重复分层交叉验证和独立测试集进行评估，以MAE、RMSE和R²作为性能指标。统计比较采用Friedman检验，随后进行Wilcoxon事后分析并应用Holm校正。结果 结果表明预测性能良好，SVR模型取得了最佳结果（RMSE = 2.68 kg，MAE = 1.81 kg，R² = 0.85），与其他模型相比显示出统计学显著差异。讨论 这些发现表明，将计算机视觉技术与能够捕捉非线性关系的模型相结合，可以有效建模动物形态与体重之间的关系，提供一种适用于实际生产系统的低成本、非侵入式解决方案，并支持精准畜牧养殖的发展。","Frontiers in Artificial Intelligence","2026-09-14T00:00:00Z",73,{"impact":196,"substance":120,"depth":286,"authority":353,"freshness":121,"relevant":21,"comment":444},"基于YOLOv8分割与EfficientNet特征提取的生猪无接触称重研究，3800张图像、RMSE 2.68kg，方法组合与统计验证扎实，对精准畜牧有实用价值，但属细分技术进展，影响面有限。",[446],{"name":440,"url":437},[26,27,126,448,30],"生猪养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":452},{"candidates":453,"agricultural_evidence":51},[454,458,462,468,472],{"code":32,"score":121,"supported":51,"title":455,"abstract":456,"tags":457},[],[126,140],[27,126],{"code":43,"score":108,"supported":51,"title":459,"abstract":460,"tags":461},[],[55,53],[],{"code":463,"score":213,"supported":51,"title":464,"abstract":465,"tags":467},"C1",[],[466],"生猪",[448],{"code":39,"score":213,"supported":51,"title":469,"abstract":470,"tags":471},[],[30],[30],{"code":75,"score":21,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[26],[477,478],"农业人工智能 计算机视觉 智慧农业 生猪养殖","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生猪养殖-2511","10.3389\u002Ffrai.2026.1876396",{"doi":480,"openalex_id":482,"authors":483,"venue":440,"cited_by_count":98,"oa_url":504,"card":505,"direction":170,"ingested_from":105},"W7212747795",[484,487,490,492,495,497,499,502],{"name":485,"orcid":486},"Miguel Angel Valles-Coral","https:\u002F\u002Forcid.org\u002F0000-0002-8806-2892",{"name":488,"orcid":489},"Kelvin Lleins Rojas-Córdova","https:\u002F\u002Forcid.org\u002F0009-0001-4097-3961",{"name":491,"orcid":9},"Lloy Pinedo",{"name":493,"orcid":494},"Richard Injante","https:\u002F\u002Forcid.org\u002F0000-0002-2449-8937",{"name":496,"orcid":9},"Pierre Vidaurre-Rojas",{"name":498,"orcid":9},"Jorge Saavedra-Ramírez",{"name":500,"orcid":501},"Fernando Ruiz-Saavedra","https:\u002F\u002Forcid.org\u002F0000-0003-4664-4867",{"name":503,"orcid":9},"Williams Ramirez","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1876396\u002Fpdf",{"tldr":506,"method":507,"finding":508,"direction":273,"opportunity":509},"用YOLOv8n-seg分割猪体并提取EfficientNet特征，结合回归模型实现非侵入式猪体重估","3800张侧视图像，YOLOv8n-seg实例分割+EfficientNet-B","SVR表现最佳（RMSE=2.68kg，MAE=1.81kg，R²=0.85），显著优于其他模型。","可探索多视角、多品种及轻量化边缘部署，提升复杂农场环境下的泛化能力。","2026-09-15T23:30:08.535413Z",{"id":512,"title":513,"url":514,"summary":515,"summary_zh":516,"content":9,"source_name":282,"source_url":514,"published_at":517,"category":12,"cover_url":9,"hotness":13,"is_selected":51,"score":518,"score_detail":519,"sources":522,"tags":524,"primary_topic":526,"secondary_topics":528,"classification":534,"search_phrases":569,"slug":571,"view_count":219,"doi":572,"paper":573,"created_at":591},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。","2026-09-09T00:00:00Z",87,{"impact":18,"substance":520,"depth":134,"authority":19,"freshness":20,"relevant":21,"comment":521},23,"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。",[523],{"name":282,"url":514},[26,27,126,30,525],"智能养殖",{"code":206,"slug":210,"name":209,"path":527,"parent_code":43},"\u002Ftopic\u002Flivestock\u002Fpoultry",[529,530],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":218,"slug":531,"name":532,"path":533,"parent_code":43},"ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",{"status":45,"locked":14,"version":46,"method":47,"evidence":535},{"candidates":536,"agricultural_evidence":51},[537,542,546,551,555,559,565],{"code":32,"score":134,"supported":51,"title":538,"abstract":539,"tags":541},[140],[126,140,540],"decision support",[27,126],{"code":206,"score":19,"supported":51,"title":543,"abstract":544,"tags":545},[210],[209,210],[],{"code":43,"score":547,"supported":51,"title":548,"abstract":549,"tags":550},11,[53],[55],[],{"code":218,"score":547,"supported":51,"title":552,"abstract":553,"tags":554},[222],[222],[],{"code":39,"score":213,"supported":51,"title":556,"abstract":557,"tags":558},[],[30],[30],{"code":560,"score":219,"supported":51,"title":561,"abstract":562,"tags":564},"I7",[],[563],"文献计量",[],{"code":75,"score":21,"supported":14,"title":566,"abstract":567,"tags":568},[],[],[26],[570,478],"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智慧农业智能养殖-2017","10.1016\u002Fj.compag.2026.112419",{"doi":572,"openalex_id":574,"authors":575,"venue":282,"cited_by_count":98,"oa_url":514,"card":586,"direction":170,"ingested_from":105},"W7212062011",[576,578,581,583],{"name":577,"orcid":9},"Alexey Ruchay",{"name":579,"orcid":580},"Vladimir Kolpakov","https:\u002F\u002Forcid.org\u002F0000-0001-9658-7034",{"name":582,"orcid":9},"Hao Guo",{"name":584,"orcid":585},"Andrea Pezzuolo","https:\u002F\u002Forcid.org\u002F0000-0002-9955-9896",{"tldr":587,"method":588,"finding":589,"direction":170,"opportunity":590},"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"]