[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2286":3,"related-2286":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":51,"slug":54,"view_count":55,"doi":56,"paper":57,"created_at":74},2286,"Evaluation of solid attachment effects on static stability and operator head injury criterion (HIC) in agricultural all-terrain vehicles (ATVs) using finite element modeling (FEM)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112377","All-terrain vehicles (ATVs) are widely used in agricultural operations, yet rollover incidents remain a major cause of severe injury and fatality among farm workers. The addition of attachments alters the vehicle’s center of gravity (CG) and may significantly affect stability and injury risk. This study developed and validated a multi-body finite element (FE) model of an ATV equipped with front and rear rack attachments, a tandem-axle trailer, and a 50th-percentile rider dummy to evaluate static rollover stability under varying operational conditions, enabling the assessment of configurations such as ATV-trailer systems that are costly and difficult to test experimentally. The model incorporated CG measurements obtained from tilt-table and corner-weight testing. Static stability was quantified using the tilt table ratio (TTR) under forward pitch, rearward pitch, and lateral roll conditions. Head injury risk was assessed using the 15 ms head injury criterion (HIC 15 ) during simulated rollover events. The FE model predictions of TTR showed good agreement with experimental results (average percentage error generally \u003C 10 %). Attachment configuration significantly affected stability across all rollover modes, with rear and combined rack loads producing the largest reductions in TTR. Lower tire inflation pressure reduced forward pitch stability and interacted with attachment configuration in lateral and rearward rollovers. Forward pitch rollovers generally produced the highest HIC 15 values, reaching 1095 for the ATV equipped with both front and rear racks. The relative severity of rearward pitch and lateral rollovers varied with attachment configuration. Nevertheless, nearly all predicted HIC 15 values remained below the accepted injury threshold of 700.","全地形车（ATV）广泛用于农业作业，但翻车事故仍是农场工人严重受伤和死亡的主要原因。加装附件会改变车辆重心（CG），并可能显著影响稳定性和伤害风险。本研究开发并验证了一辆配备前后货架附件、双轴拖车和第五十百分位乘员假人的全地形车的多体有限元（FE）模型，以评估不同作业条件下的静态翻车稳定性，从而能够对全地形车-拖车系统等实验测试成本高且难度大的配置进行评估。该模型纳入了通过倾斜台和角重量测试获得的重心测量值。静态稳定性通过前倾、后倾和侧翻条件下的倾斜台比值（TTR）进行量化。在模拟翻车事件中，使用15 ms头部损伤标准（HIC₁₅）评估头部损伤风险。有限元模型对TTR的预测与实验结果吻合良好（平均百分比误差通常小于10%）。附件配置在所有翻车模式下均显著影响稳定性，其中后货架载荷及前后货架组合载荷导致TTR降幅最大。较低的轮胎充气压力降低了前倾稳定性，并在侧翻和后倾翻车中与附件配置产生交互作用。前倾翻车通常产生最高的HIC₁₅值，对于同时配备前后货架的全地形车达到1095。后倾和侧翻的相对严重程度随附件配置而变化。尽管如此，几乎所有预测的HIC₁₅值均低于公认的损伤阈值700。",null,"Computers and Electronics in Agriculture","2026-09-11T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,8,1,"核心期刊论文，用有限元模型量化农用全地形车挂载对侧翻稳定性与头部损伤的影响，方法新颖、数据可靠，对农机安全设计有参考价值，但属细分领域研究，公共影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","职业安全","农机安全","农业机械","仿真建模",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":49,"reason":50},[39,44],{"code":40,"score":22,"supported":14,"title":41,"abstract":42,"tags":43},"F5",[],[],[30],{"code":45,"score":22,"supported":14,"title":46,"abstract":47,"tags":48},"I1",[],[],[27],true,"缺少足够的主题证据",[52,53],"仿真建模 农业机械 农机安全 智慧农业","仿真建模 农业机械","仿真建模农业机械农机安全智慧农业-2286",4,"10.1016\u002Fj.compag.2026.112377",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":66,"oa_url":6,"card":67,"direction":71,"ingested_from":73},"W7212322062",[60,63],{"name":61,"orcid":62},"Payam Farhadi","https:\u002F\u002Forcid.org\u002F0000-0001-5538-0598",{"name":64,"orcid":65},"Farzaneh Khorsandi","https:\u002F\u002Forcid.org\u002F0000-0002-1833-4311",0,{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"用有限元模型评估农用全地形车加装附件后的静态稳定性与驾驶员头部损伤风险。","建立多体有限元模型，结合倾台与角重测试获取重心，计算TTR和HIC15。","后架及组合载荷显著降低稳定性，前倾翻HIC15最高达1095，但多数低于700阈值。","农业人工智能与决策模型","可结合真实田间地形与动态工况，开发附件配置优化与翻车预警的决策模型。","openalex","2026-09-13T23:30:02.142166Z",{"total":76,"page":22,"page_size":76,"items":77},6,[78,148,232,312,355,420],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":9,"source_name":10,"source_url":81,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":91,"tags":93,"primary_topic":97,"secondary_topics":103,"classification":104,"search_phrases":122,"slug":125,"view_count":66,"doi":126,"paper":127,"created_at":147},3983,"Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112469","Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling。Computers and Electronics in Agriculture","基于SPH-FEM耦合的磨料气体射流油菜茎秆切割机理数值与实验研究。《计算机与电子农业》","2026-10-01T00:00:00Z",72,{"impact":17,"substance":87,"depth":88,"authority":20,"freshness":89,"relevant":22,"comment":90},20,17,9,"核心期刊论文，SPH-FEM耦合方法研究油菜秸秆磨料气体射流切割机理，方法有新意但属细分领域基础研究，产业影响有限。",[92],{"name":10,"url":81},[27,94,95,30,96],"油菜","数值模拟","秸秆处理",{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":105,"locked":14,"version":35,"method":36,"evidence":106},"classified",{"candidates":107,"agricultural_evidence":49},[108,114,118],{"code":98,"score":109,"supported":49,"title":110,"abstract":112,"tags":113},15,[111],"rapeseed",[94,111],[94],{"code":40,"score":22,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[30],{"code":45,"score":22,"supported":14,"title":119,"abstract":120,"tags":121},[],[],[27],[123,124],"油菜秸秆 磨料气体射流 切割","SPH-FEM 耦合 秸秆切割","油菜秸秆磨料气体射流切割-3983","10.1016\u002Fj.compag.2026.112469",{"doi":126,"openalex_id":128,"authors":129,"venue":10,"cited_by_count":66,"oa_url":9,"card":142,"direction":71,"ingested_from":73},"W7215128671",[130,133,135,137,139],{"name":131,"orcid":132},"Mun-Sung Han","https:\u002F\u002Forcid.org\u002F0009-0000-4043-6982",{"name":134,"orcid":9},"Xiaokang Qin",{"name":136,"orcid":9},"Kai Yu",{"name":138,"orcid":9},"Yun Chen",{"name":140,"orcid":141},"Kaixiong Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9904-6137",{"tldr":143,"method":144,"finding":145,"direction":71,"opportunity":146},"采用SPH-FEM耦合方法数值与实验研究磨料气体射流切割油菜茎秆的机理。","SPH-FEM耦合数值模拟结合切割实验，分析磨料气体射流切割过程。","揭示了磨料气体射流切割油菜茎秆的力学机制与切割参数影响规律。","可将SPH-FEM耦合仿真拓展至其他作物茎秆切割，并融合机器视觉实现切割参数智能优化。","2026-10-02T23:30:03.593073Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":10,"source_url":151,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":158,"tags":160,"primary_topic":164,"secondary_topics":169,"classification":175,"search_phrases":194,"slug":197,"view_count":22,"doi":198,"paper":199,"created_at":231},3974,"A real-time estimation method for wheeled agricultural machinery slip parameters in paddy fields based on the prediction-residual approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112478","A real-time estimation method for wheeled agricultural machinery slip parameters in paddy fields based on the prediction-residual approach。Computers and Electronics in Agriculture","基于预测残差法的水稻田轮式农业机械滑转参数实时估计方法。Computers and Electronics in Agriculture",69,{"impact":17,"substance":19,"depth":156,"authority":20,"freshness":89,"relevant":22,"comment":157},16,"提出基于预测残差的水田轮式农机滑移参数实时估计方法，方法新颖且面向实际作业场景，但属细分技术论文，产业影响有限。",[159],{"name":10,"url":151},[27,161,30,162,163],"精准农业","水稻生产","导航定位",{"code":165,"slug":166,"name":167,"path":168,"parent_code":102},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[170],{"code":40,"slug":171,"name":172,"path":173,"parent_code":174},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"status":105,"locked":14,"version":35,"method":36,"evidence":176},{"candidates":177,"agricultural_evidence":49},[178,184,189],{"code":165,"score":19,"supported":49,"title":179,"abstract":181,"tags":183},[180],"paddy",[167,182,180],"稻田",[162],{"code":40,"score":109,"supported":49,"title":185,"abstract":187,"tags":188},[186],"agricultural machinery",[30,186],[30],{"code":45,"score":190,"supported":14,"title":191,"abstract":192,"tags":193},2,[],[],[27,161],[195,196],"水田 农业机械 滑移率 实时估计","预测残差 滑转参数 估计","水田农业机械滑移率实时估计-3974","10.1016\u002Fj.compag.2026.112478",{"doi":198,"openalex_id":200,"authors":201,"venue":10,"cited_by_count":66,"oa_url":9,"card":225,"direction":229,"ingested_from":73},"W7215087308",[202,205,208,210,212,214,217,219,222],{"name":203,"orcid":204},"Dawen Feng","https:\u002F\u002Forcid.org\u002F0000-0002-1307-8902",{"name":206,"orcid":207},"Boyang Wang","https:\u002F\u002Forcid.org\u002F0000-0003-3613-8792",{"name":209,"orcid":9},"Jing He",{"name":211,"orcid":9},"Yuqi Chen",{"name":213,"orcid":9},"Zhongxian Man",{"name":215,"orcid":216},"Jie He","https:\u002F\u002Forcid.org\u002F0000-0003-2134-0998",{"name":218,"orcid":9},"Zhaodi Liu",{"name":220,"orcid":221},"Gaolong Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7415-0867",{"name":223,"orcid":224},"Lian Hu","https:\u002F\u002Forcid.org\u002F0000-0002-4875-7868",{"tldr":226,"method":227,"finding":228,"direction":229,"opportunity":230},"提出基于预测-残差法的轮式农机水田滑移参数实时估计方法。","预测-残差法，结合农机运动学模型与实时传感数据估计滑移参数。","该方法能在水田复杂环境下实时准确估计农机滑移参数。","智慧农业 \u002F 农业物联网","可探索多源传感融合与深度学习提升水田滑移估计鲁棒性，并迁移至其他松软地面农机。","2026-10-02T23:30:02.471920Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":245,"tags":247,"primary_topic":251,"secondary_topics":256,"classification":257,"search_phrases":291,"slug":294,"view_count":76,"doi":295,"paper":296,"created_at":311},2542,"Discrete Element Method-Based Modeling and Application of Agricultural Materials: A Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16181958","Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering.","离散元法（DEM）已成为分析农业物料与机械中颗粒尺度行为及复杂相互作用的重要工具。本文综述了基于DEM的建模与农业工程应用的最新进展，系统考察了土壤、种子、肥料、茎秆和根系等代表性物料的颗粒建模策略、接触模型开发及参数标定框架。重点阐述了从简单球形近似到多球体、粘结型及基于三维扫描的重构方法的演变，以及从经典Hertz–Mindlin模型向黏聚、弹塑性及可断裂本构模型的转变。标定方法从经验赋值发展为集成试验设计、响应面法和机器学习辅助反分析的系统框架。这些在保真度和精度方面的进步使得DEM在耕整、播种、施肥和收获等领域得到广泛应用，重点涉及装备优化、机理分析和性能预测。尽管取得了进展，但在模型标准化、参数可迁移性、计算成本和多物理场耦合方面仍存在挑战。未来工作指向统一建模框架、标准化数据库、实时仿真以及与人工智能的融合，以支撑数字农业和智能机械发展。本文旨在为高保真DEM建模及推进农业工程数字化转型提供参考。","Agriculture","2026-09-12T00:00:00Z",76,{"impact":19,"substance":242,"depth":19,"authority":243,"freshness":76,"relevant":22,"comment":244},21,13,"系统综述离散元法在农业物料建模与装备优化中的进展，方法学价值高，对智能农机与数字农业有参考意义。",[246],{"name":238,"url":235},[248,27,249,30,250],"数字农业","农业人工智能","离散元仿真",{"code":252,"slug":253,"name":254,"path":255,"parent_code":174},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",[],{"status":105,"locked":14,"version":35,"method":36,"evidence":258},{"candidates":259,"agricultural_evidence":49},[260,267,272,280,287],{"code":252,"score":89,"supported":49,"title":261,"abstract":262,"tags":266},[],[263,264,265],"施肥","肥料","fertilization",[],{"code":45,"score":21,"supported":49,"title":268,"abstract":269,"tags":271},[],[248,270],"digital agriculture",[248,27],{"code":273,"score":274,"supported":49,"title":275,"abstract":276,"tags":279},"I2",7,[],[277,278],"机器学习","artificial intelligence",[249],{"code":281,"score":76,"supported":49,"title":282,"abstract":283,"tags":286},"H2",[],[284,285],"土壤","soil",[],{"code":40,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[30],[292,293],"农业人工智能 离散元仿真 农业机械 数字农业","农业人工智能 离散元仿真","农业人工智能离散元仿真农业机械数字农业-2542","10.3390\u002Fagriculture16181958",{"doi":295,"openalex_id":297,"authors":298,"venue":238,"cited_by_count":66,"oa_url":235,"card":306,"direction":71,"ingested_from":73},"W7213152558",[299,301,303],{"name":300,"orcid":9},"Xingchi Zhou",{"name":302,"orcid":9},"Yanbin Liu",{"name":304,"orcid":305},"Zhenwei Liang","https:\u002F\u002Forcid.org\u002F0000-0001-6501-4364",{"tldr":307,"method":308,"finding":309,"direction":71,"opportunity":310},"综述农业物料离散元建模的粒子模型、接触模型与参数标定进展及应用。","综述离散元粒子建模、接触模型、标定方法及在耕播收中的应用。","建模从球形向多球\u002F键合\u002F3D重建演进，标定转向机器学习辅助反演。","可探索DEM与AI实时仿真、多物理场耦合及标准化参数数据库构建。","2026-09-15T23:30:36.643241Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":9,"content":317,"source_name":318,"source_url":9,"published_at":319,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":323,"tags":325,"primary_topic":9,"secondary_topics":331,"classification":332,"search_phrases":349,"slug":352,"view_count":353,"doi":9,"paper":9,"created_at":354},546,"A Systematic Review for Reducing Risky, Demanding and Repetitive Labor in Agriculture Through Digital and Automated Technologies","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358","【基于 Scopus 单库系统综述(PRISMA 2020),纳入 2020—2026 同行评议文献】针对农业劳动力老龄化、劳动力短缺与高工伤率的多维危机,系统综述智能农业技术在职业安全、人机工效与工人健康方面的最新证据与可持续采纳障碍。综述表明无人车辆、外骨骼与协作机器人等创新显著降低化学暴露与肌肉骨骼损伤风险,并有助于减少资源浪费;但研究-落地鸿沟仍持续存在,受成本、可靠性与人机工效问题制约。文章呼吁同步弥合技术、人机工效与社会经济三层关键差距,以实现可持续的农业技术转型。","Next Article in Journal\n\n[From Laboratory Prototype to Sustainable Deployment: Smart Garment Technologies for Construction Safety Monitoring and a Multidimensional Readiness Framework](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8359)\n\n Previous Article in Journal\n\n[National Park Experiences and Satisfaction in Kazakhstan: Perceived Restorativeness and Happiness as Sequential Mediators](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8357)\n\n## Journals\n\n[Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)[Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)[Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)[Proceedings 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A.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Georgios%20A.%20Papadopoulos)\n\n[Article Views 0](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358#metrics)\n\n[Citations 0](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358#metrics)\n\n*   [Table of Contents](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358#table_of_contents)\n\n \n    *   [Abstract](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358#)\n\n[](https:\u002F\u002Fwww.altmetric.com\u002Fdetails.php?domain=www.mdpi.com&doi=10.3390%2Fsu18168358)Altmetric[_share_ Share](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358# \"Share\")[_announcement_ Help](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358# \"Help\")[_format\\_quote_ Cite](javascript:void(0);)[_question\\_answer_ Discuss in SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fdiscussion-groups\u002Fpublic\u002F10.3390\u002Fsu18168358?utm_source=mpdi.com&utm_medium=publication&utm_campaign=discuss_in_sciprofiles \"Discuss in Sciprofiles\")\n\n## Need Help?\n\n### Support\n\nFind support for a specific problem in the support section of our website.\n\n[Get Support](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontactform)\n\n### Feedback\n\nPlease let us know what you think of our products and services.\n\n[Give Feedback](https:\u002F\u002Fwww.mdpi.com\u002Ffeedback\u002Fsend)\n\n### Information\n\nVisit our dedicated information section to learn more about MDPI.\n\n[Get Information](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358)\n\n## JSmol Viewer\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358)\n\n_first\\_page_\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358\u002Fpdf?version=1786715225)\n\n_settings_\n\n[Order Article Reprints](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358\u002Freprints)\n\n Font Type: \n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\n**This is an early access version, the complete PDF, HTML, and XML versions will be available soon.**\n\nOpen Access Systematic Review\n\n# A Systematic Review for Reducing Risky, Demanding and Repetitive Labor in Agriculture Through Digital and Automated Technologies\n\n by \n\n Nefeli K. Galaziou\n\n![Image 11](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Nefeli K. Galaziou\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5481123?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Nefeli%20K.%20Galaziou)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Nefeli+K.+Galaziou%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Nefeli+K.+Galaziou)\n\n[](mailto:galazioun@gmail.com)[![Image 12: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0009-0009-2886-8606), \n\n Evripidis P. Kechagias\n\n![Image 13](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F1135638\u002Fthumb\u002FEvripidis_Kechagias.jpg)Evripidis P. Kechagias\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F1135638?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Evripidis%20P.%20Kechagias)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Evripidis+P.+Kechagias%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Evripidis+P.+Kechagias)\n\n[](mailto:eurikechagias@mail.ntua.gr)[![Image 14: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-9466-6185), \n\n Nikolaos A. Panayiotou\n\n![Image 15](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F1633266\u002Fthumb\u002FNikolaos_A._Panayiotou.png)Nikolaos A. Panayiotou\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F1633266?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Nikolaos%20A.%20Panayiotou)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Nikolaos+A.+Panayiotou%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Nikolaos+A.+Panayiotou)\n\n[](mailto:panayiot@central.ntua.gr)[![Image 16: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-0603-0302), \n\n Sotiris P. Gayialis\n\n![Image 17](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F1015295\u002Fthumb\u002FSotiris_Gayialis.jpg)Sotiris P. Gayialis\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F1015295?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Sotiris%20P.%20Gayialis)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Sotiris+P.+Gayialis%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Sotiris+P.+Gayialis)\n\n[](mailto:sotga@mail.ntua.gr)[![Image 18: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-7524-0215) and \n\n Georgios A. Papadopoulos\n\n![Image 19](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F1135733\u002Fthumb\u002FGeorgios_Papadopoulos.png)Georgios A. Papadopoulos\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F1135733?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Georgios%20A.%20Papadopoulos)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Georgios+A.+Papadopoulos%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Georgios+A.+Papadopoulos)\n\n *[](mailto:gpapado@mail.ntua.gr)[![Image 20: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-8305-7971)\n\nSector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece\n\n*\n\nAuthor to whom correspondence should be addressed. \n\n_Sustainability_**2026**, _18_(16), 8358; [https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18168358](https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18168358)\n\nSubmission received: 14 July 2026 \u002F Revised: 7 August 2026 \u002F Accepted: 11 August 2026 \u002F Published: 14 August 2026\n\n[Download _keyboard\\_arrow\\_down_](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358)\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358\u002Fpdf?version=1786715225)\n\n[Download Supplementary Material](javascript:void(0);)\n\n[Versions Notes](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8358\u002Fnotes)\n\n## Abstract\n\n The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). \n\n**Keywords:**agriculture 4.0; occupational safety; agricultural ergonomics; technology acceptance; sustainable agriculture; smart farming; human–robot collaboration; automation;","Sustainability","2026-08-13T22:00:00Z",70,{"impact":19,"substance":87,"depth":19,"authority":17,"freshness":190,"relevant":22,"comment":322},"系统综述探讨数字与自动化技术减少农业高风险、高要求、重复劳动，对农业可持续发展有参考价值，但时效性较低。",[324],{"name":318,"url":315},[27,326,327,28,328,329,330],"农业机器人","农业自动化","人机工效","可持续农业","SLR综述",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":333},{"candidates":334,"agricultural_evidence":49,"reason":50},[335,340,345],{"code":336,"score":22,"supported":14,"title":337,"abstract":338,"tags":339},"F6",[],[],[326],{"code":341,"score":22,"supported":14,"title":342,"abstract":343,"tags":344},"H8",[],[],[329],{"code":45,"score":22,"supported":14,"title":346,"abstract":347,"tags":348},[],[],[27],[350,351],"农业机器人 农业自动化 可持续农业 人机工效","农业机器人 农业自动化","农业机器人农业自动化可持续农业人机工效-546",3,"2026-08-15T00:02:55.905676Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":361,"source_url":358,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":363,"sources":365,"tags":367,"primary_topic":371,"secondary_topics":377,"classification":383,"search_phrases":400,"slug":403,"view_count":66,"doi":404,"paper":405,"created_at":419},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":21,"substance":242,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":364},"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[366],{"name":361,"url":358},[27,249,368,369,370],"小农户","植物病害识别","可解释性",{"code":372,"slug":373,"name":374,"path":375,"parent_code":376},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[378],{"code":273,"slug":379,"name":380,"path":381,"parent_code":382},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":105,"locked":14,"version":35,"method":36,"evidence":384},{"candidates":385,"agricultural_evidence":49},[386,391,396],{"code":372,"score":17,"supported":49,"title":387,"abstract":389,"tags":390},[388],"smallholder",[388],[368],{"code":273,"score":13,"supported":49,"title":392,"abstract":394,"tags":395},[393],"computer vision",[],[249,369],{"code":45,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[27],[401,402],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":404,"openalex_id":406,"authors":407,"venue":361,"cited_by_count":66,"oa_url":358,"card":414,"direction":71,"ingested_from":73},"W7223137323",[408,410,412],{"name":409,"orcid":9},"Akshay Ramgoolam",{"name":411,"orcid":9},"Priya Bundhoo Appadoo",{"name":413,"orcid":9},"Jean-Noël LEspérance",{"tldr":415,"method":416,"finding":417,"direction":71,"opportunity":418},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":361,"source_url":423,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":426,"sources":428,"tags":430,"primary_topic":434,"secondary_topics":440,"classification":442,"search_phrases":463,"slug":466,"view_count":66,"doi":467,"paper":468,"created_at":480},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":21,"substance":18,"depth":19,"authority":17,"freshness":89,"relevant":22,"comment":427},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[429],{"name":361,"url":423},[27,249,431,432,433],"灌溉决策","安全保证","试验设计",{"code":435,"slug":436,"name":437,"path":438,"parent_code":439},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[441],{"code":273,"slug":379,"name":380,"path":381,"parent_code":382},{"status":105,"locked":14,"version":35,"method":36,"evidence":443},{"candidates":444,"agricultural_evidence":49},[445,450,455,459],{"code":435,"score":109,"supported":49,"title":446,"abstract":447,"tags":449},[436],[448,436],"灌溉",[431],{"code":273,"score":17,"supported":49,"title":451,"abstract":452,"tags":454},[278],[453],"decision support",[249],{"code":281,"score":76,"supported":49,"title":456,"abstract":457,"tags":458},[],[284,285],[],{"code":45,"score":22,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[27],[464,465],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":467,"openalex_id":469,"authors":470,"venue":361,"cited_by_count":66,"oa_url":423,"card":475,"direction":71,"ingested_from":73},"W7223131205",[471,473],{"name":472,"orcid":9},"Nassima Benkhelifa",{"name":474,"orcid":9},"Yacine Aït Ahmed",{"tldr":476,"method":477,"finding":478,"direction":71,"opportunity":479},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z"]