[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3917":3,"related-3917":138},{"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":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":137},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",null,"Frontiers in Veterinary Science","2026-09-30T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","机器学习","畜牧兽医","动物福利","风险分级监管",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","C",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,62,70,76,83,89],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},15,true,[54],"animal welfare",[29,54],[29],{"code":39,"score":51,"supported":52,"title":58,"abstract":60,"tags":61},[59],"machine learning",[27,59],[27],{"code":63,"score":64,"supported":52,"title":65,"abstract":66,"tags":69},"C3",6,[],[67,68],"家禽","poultry",[],{"code":36,"score":71,"supported":52,"title":72,"abstract":73,"tags":75},4,[],[74],"livestock",[28],{"code":77,"score":78,"supported":52,"title":79,"abstract":80,"tags":82},"C2",3,[],[81],"cattle",[],{"code":84,"score":78,"supported":52,"title":85,"abstract":86,"tags":88},"C5",[],[87],"veterinary",[],{"code":90,"score":21,"supported":14,"title":91,"abstract":92,"tags":93},"I1",[],[],[26],[95,96],"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":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":129,"oa_url":6,"card":130,"direction":134,"ingested_from":136},"W7214951739",[103,105,107,109,111,114,116,118,120,123,126],{"name":104,"orcid":9},"Eva Obenaus",{"name":106,"orcid":9},"Sabrina Kuchling",{"name":108,"orcid":9},"Tadej Zerak",{"name":110,"orcid":9},"Corina Schleicher",{"name":112,"orcid":113},"Ian Kopacka","https:\u002F\u002Forcid.org\u002F0000-0003-4106-1742",{"name":115,"orcid":9},"Sven Budik",{"name":117,"orcid":9},"Sabine Grimme",{"name":119,"orcid":9},"Jörg Hiesel",{"name":121,"orcid":122},"Peter Eckhardt","https:\u002F\u002Forcid.org\u002F0009-0005-5015-4846",{"name":124,"orcid":125},"Johannes Baumgartner","https:\u002F\u002Forcid.org\u002F0000-0001-8555-1378",{"name":127,"orcid":128},"Clair L. Firth","https:\u002F\u002Forcid.org\u002F0000-0002-4919-7786",0,{"tldr":131,"method":132,"finding":133,"direction":134,"opportunity":135},"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。","openalex","2026-10-01T23:30:54.115236Z",{"total":64,"page":21,"page_size":64,"items":139},[140,208,276,343,438,526],{"id":141,"title":142,"url":143,"summary":144,"summary_zh":145,"content":9,"source_name":146,"source_url":143,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":148,"score_detail":149,"sources":154,"tags":156,"primary_topic":160,"secondary_topics":161,"classification":163,"search_phrases":186,"slug":189,"view_count":21,"doi":190,"paper":191,"created_at":207},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":150,"substance":151,"depth":17,"authority":19,"freshness":152,"relevant":21,"comment":153},12,20,8,"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。",[155],{"name":146,"url":143},[26,157,158,29,159],"农业人工智能","计算机视觉","精准畜牧",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[162],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":164},{"candidates":165,"agricultural_evidence":52},[166,171,177,182],{"code":32,"score":167,"supported":52,"title":168,"abstract":169,"tags":170},19,[54],[159,29,54],[29,159],{"code":39,"score":167,"supported":52,"title":172,"abstract":174,"tags":176},[173],"computer vision",[158,175,173],"artificial intelligence",[157,158],{"code":36,"score":64,"supported":52,"title":178,"abstract":179,"tags":181},[],[180,74],"畜牧",[],{"code":90,"score":21,"supported":14,"title":183,"abstract":184,"tags":185},[],[],[26],[187,188],"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":190,"openalex_id":192,"authors":193,"venue":146,"cited_by_count":129,"oa_url":200,"card":201,"direction":205,"ingested_from":136},"W7220695950",[194,197],{"name":195,"orcid":196},"Mariska Thalitha Bosschaert","https:\u002F\u002Forcid.org\u002F0000-0002-9160-7638",{"name":198,"orcid":199},"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":202,"method":203,"finding":204,"direction":205,"opportunity":206},"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":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":216,"sources":218,"tags":220,"primary_topic":223,"secondary_topics":224,"classification":226,"search_phrases":255,"slug":258,"view_count":259,"doi":260,"paper":261,"created_at":275},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","Agricultural Systems","2026-09-16T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":217},"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。",[219],{"name":214,"url":211},[26,157,221,222,159],"数字素养","农业补贴",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[225],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":227},{"candidates":228,"agricultural_evidence":52},[229,233,237,241,246,250],{"code":36,"score":19,"supported":52,"title":230,"abstract":231,"tags":232},[74],[180,74],[],{"code":39,"score":150,"supported":52,"title":234,"abstract":235,"tags":236},[175],[175],[157],{"code":32,"score":13,"supported":52,"title":238,"abstract":239,"tags":240},[],[159,29,54],[159],{"code":242,"score":21,"supported":14,"title":243,"abstract":244,"tags":245},"G3",[],[],[222],{"code":90,"score":21,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[26],{"code":251,"score":21,"supported":14,"title":252,"abstract":253,"tags":254},"I6",[],[],[221],[256,257],"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":260,"openalex_id":262,"authors":263,"venue":214,"cited_by_count":129,"oa_url":211,"card":269,"direction":273,"ingested_from":136},"W7213425399",[264,266],{"name":265,"orcid":9},"Carlos Parra-Lopez",{"name":267,"orcid":268},"Carmen Carmona‐Torres","https:\u002F\u002Forcid.org\u002F0000-0002-6982-1363",{"tldr":270,"method":271,"finding":272,"direction":273,"opportunity":274},"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。","2026-09-17T23:30:04.844104Z",{"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":78,"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":151,"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},[157,291,292,29],"智慧养殖","风险治理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[295],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":297},{"candidates":298,"agricultural_evidence":52},[299,303,307],{"code":32,"score":51,"supported":52,"title":300,"abstract":301,"tags":302},[54],[29,54],[29],{"code":39,"score":150,"supported":52,"title":304,"abstract":305,"tags":306},[175],[175],[157],{"code":36,"score":78,"supported":52,"title":308,"abstract":309,"tags":310},[],[180],[],[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":129,"oa_url":279,"card":337,"direction":205,"ingested_from":136},"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":205,"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":357,"tags":359,"primary_topic":363,"secondary_topics":369,"classification":377,"search_phrases":414,"slug":417,"view_count":129,"doi":418,"paper":419,"created_at":437},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":353,"substance":354,"depth":286,"authority":355,"freshness":13,"relevant":21,"comment":356},16,21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[358],{"name":349,"url":346},[26,27,360,361,362],"遥感监测","土壤墒情","冬小麦",{"code":364,"slug":365,"name":366,"path":367,"parent_code":368},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[370,376],{"code":371,"slug":372,"name":373,"path":374,"parent_code":375},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":378},{"candidates":379,"agricultural_evidence":52},[380,386,391,395,403,410],{"code":364,"score":381,"supported":52,"title":382,"abstract":384,"tags":385},26,[383,365],"winter wheat",[366,383,365],[362],{"code":371,"score":51,"supported":52,"title":387,"abstract":388,"tags":390},[372],[389,372],"土壤",[361],{"code":39,"score":51,"supported":52,"title":392,"abstract":393,"tags":394},[59],[27,59],[27],{"code":396,"score":13,"supported":52,"title":397,"abstract":398,"tags":402},"I3",[],[399,400,401],"遥感","remote sensing","multispectral",[360],{"code":404,"score":64,"supported":52,"title":405,"abstract":406,"tags":409},"H3",[],[407,408],"灌溉","irrigation",[],{"code":90,"score":71,"supported":52,"title":411,"abstract":412,"tags":413},[],[26],[26],[415,416],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":418,"openalex_id":420,"authors":421,"venue":349,"cited_by_count":129,"oa_url":346,"card":431,"direction":205,"ingested_from":136},"W7223156204",[422,425,427,429],{"name":423,"orcid":424},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":426,"orcid":9},"Yi Zhang",{"name":428,"orcid":9},"WEI Lei",{"name":430,"orcid":9},"Chengcai Zhang",{"tldr":432,"method":433,"finding":434,"direction":435,"opportunity":436},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":439,"title":440,"url":441,"summary":442,"summary_zh":443,"content":9,"source_name":444,"source_url":441,"published_at":445,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":455,"classification":462,"search_phrases":499,"slug":502,"view_count":129,"doi":503,"paper":504,"created_at":525},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":152,"relevant":21,"comment":448},"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[450],{"name":444,"url":441},[26,157,27,452,453],"食品安全","文献计量",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[456],{"code":457,"slug":458,"name":459,"path":460,"parent_code":461},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":45,"locked":14,"version":46,"method":47,"evidence":463},{"candidates":464,"agricultural_evidence":52},[465,471,476,482,489,495],{"code":39,"score":167,"supported":52,"title":466,"abstract":467,"tags":470},[175],[468,27,158,59,469,175,173],"深度学习","deep learning",[157,27],{"code":457,"score":51,"supported":52,"title":472,"abstract":474,"tags":475},[473],"food safety",[452,473],[452],{"code":477,"score":51,"supported":52,"title":478,"abstract":480,"tags":481},"I7",[479],"bibliometric",[453,479],[453],{"code":483,"score":64,"supported":52,"title":484,"abstract":485,"tags":488},"H7",[],[486,487],"气候变化","climate change",[],{"code":490,"score":78,"supported":52,"title":491,"abstract":492,"tags":494},"A8",[],[493],"food security",[],{"code":90,"score":21,"supported":14,"title":496,"abstract":497,"tags":498},[],[],[26],[500,501],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":503,"openalex_id":505,"authors":506,"venue":444,"cited_by_count":129,"oa_url":9,"card":520,"direction":134,"ingested_from":136},"W7222920742",[507,509,511,514,517],{"name":508,"orcid":9},"Harang Lee",{"name":510,"orcid":9},"지은서",{"name":512,"orcid":513},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":515,"orcid":516},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":518,"orcid":519},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":521,"method":522,"finding":523,"direction":134,"opportunity":524},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":532,"source_url":529,"published_at":533,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":534,"sources":536,"tags":538,"primary_topic":541,"secondary_topics":542,"classification":543,"search_phrases":559,"slug":562,"view_count":129,"doi":563,"paper":564,"created_at":601},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",{"impact":17,"substance":151,"depth":286,"authority":19,"freshness":152,"relevant":21,"comment":535},"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[537],{"name":532,"url":529},[26,157,27,539,540],"精准农业","农户认知",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":544},{"candidates":545,"agricultural_evidence":52},[546,551,555],{"code":39,"score":547,"supported":52,"title":548,"abstract":549,"tags":550},11,[],[27,59,175],[157,27],{"code":371,"score":64,"supported":52,"title":552,"abstract":553,"tags":554},[],[389,372],[],{"code":90,"score":98,"supported":14,"title":556,"abstract":557,"tags":558},[],[],[26,539],[560,561],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":563,"openalex_id":565,"authors":566,"venue":532,"cited_by_count":129,"oa_url":529,"card":596,"direction":134,"ingested_from":136},"W7222866367",[567,570,573,576,579,582,585,588,590,593],{"name":568,"orcid":569},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":571,"orcid":572},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":574,"orcid":575},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":577,"orcid":578},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":580,"orcid":581},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":583,"orcid":584},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":586,"orcid":587},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":589,"orcid":9},"M. Z. Walkup",{"name":591,"orcid":592},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":594,"orcid":595},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":597,"method":598,"finding":599,"direction":134,"opportunity":600},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z"]