[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1319":3,"related-1319":92},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":21,"doi":67,"paper":68,"created_at":91},1319,"Influence of Effort Expectancy on Precision Farming Technology Acceptance among Pineapple Growers in Malaysia","https:\u002F\u002Fdoi.org\u002F10.17576\u002Febangi.2026.2303.26","The digital transformation of agriculture has accelerated the adoption of precision farming technologies (PFT) to enhance agricultural productivity, optimise resource utilisation, and support sustainable farming practices. Despite these potential benefits, the acceptance of PFT among Malaysian pineapple growers remains relatively limited, constraining the wider implementation of digital agriculture. This study examines the influence of effort expectancy on precision farming technology acceptance among pineapple growers in Malaysia. A quantitative research design was employed using a structured questionnaire administered to 184 pineapple growers. Data were analysed using descriptive statistics, Pearson correlation, and multiple regression analysis. The findings revealed that respondents reported a high level of effort expectancy (M = 3.16, SD = 0.76). Pearson correlation analysis indicated a strong positive relationship between effort expectancy and precision farming technology acceptance (r = 0.72, p \u003C 0.01). Multiple regression analysis further demonstrated that effort expectancy significantly influenced technology acceptance (β = 0.420, t = 4.916, p \u003C 0.001), indicating that growers who perceived precision farming technologies as easier to learn and use were more likely to accept them. This study extends the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) by providing empirical evidence on the role of effort expectancy in shaping acceptance of precision farming technology among Malaysian pineapple growers. The findings suggest that strengthening farmer training, digital extension services, and user-friendly technology design may accelerate precision farming technology acceptance among pineapple growers in Malaysia.","农业数字化转型加速了精准农业技术（PFT）的采用，以提高农业生产率、优化资源利用并支持可持续农业实践。尽管具有这些潜在益处，马来西亚菠萝种植者对精准农业技术的接受度仍然相对有限，制约了数字农业的更广泛实施。本研究考察了努力期望对马来西亚菠萝种植者精准农业技术接受度的影响。采用定量研究设计，通过结构化问卷对184名菠萝种植者进行调查。数据分析采用描述性统计、皮尔逊相关分析和多元回归分析。结果显示，受访者报告了较高水平的努力期望（M = 3.16，SD = 0.76）。皮尔逊相关分析表明，努力期望与精准农业技术接受度之间存在强正相关关系（r = 0.72，p \u003C 0.01）。多元回归分析进一步表明，努力期望显著影响技术接受度（β = 0.420，t = 4.916，p \u003C 0.001），表明认为精准农业技术更易学习和使用的种植者更可能接受这些技术。本研究通过提供努力期望在塑造马来西亚菠萝种植者精准农业技术接受度中作用的实证证据，扩展了技术接受与使用统一理论（UTAUT）的应用。研究结果表明，加强农民培训、数字化推广服务和用户友好的技术设计可能加速马来西亚菠萝种植者对精准农业技术的接受。",null,"Jurnal e-Bangi\u002Fe-Bangi","2026-08-31T00:00:00Z","论文",10,false,57,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,18,15,2,1,"研究马来西亚菠萝种植户对精准农业技术接受度，实证数据扎实，但地域局限且时效性低。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","精准农业","马来西亚","技术采纳",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46},[43,52,58],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},"I1",11,true,[],[27,49,50],"数字农业","digital agriculture",[26,27],{"code":31,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},3,[],[56],"可持续农业",[],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"G1",[],[],[29],[64,65],"技术采纳 智慧农业 精准农业 马来西亚","技术采纳 智慧农业","技术采纳智慧农业精准农业马来西亚-1319","10.17576\u002Febangi.2026.2303.26",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":82,"oa_url":9,"card":83,"direction":89,"ingested_from":90},"W7204750210",[71,73,75,77,79],{"name":72,"orcid":9},"Jasmin Arif Shah",{"name":74,"orcid":9},"Fatin Nabihah Rasib",{"name":76,"orcid":9},"Nur Atikah Ismail",{"name":78,"orcid":9},"Nur Bahiah Mohamed Haris",{"name":80,"orcid":81},"Norida Mazlan","https:\u002F\u002Forcid.org\u002F0000-0001-7124-7738",0,{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"研究马来西亚菠萝种植者感知易用性对精准农业技术接受度的影响。","问卷调查184名种植者，用描述统计、相关和回归分析。","努力期望与接受度强正相关，显著影响接受度。","农业人工智能与决策模型","可探索其他UTAUT因素（如社会影响）及不同作物\u002F地区，或设计易用性干预实验。","数字乡村与农业信息化","openalex","2026-09-01T23:30:24.062252Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,198,320,376,461,605],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":110,"tags":112,"primary_topic":116,"secondary_topics":122,"classification":129,"search_phrases":179,"slug":182,"view_count":21,"doi":183,"paper":184,"created_at":197},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z",80,{"impact":18,"substance":105,"depth":106,"authority":107,"freshness":108,"relevant":21,"comment":109},22,19,13,8,"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[111],{"name":101,"url":98},[26,113,27,114,115],"农业人工智能","无线传感器网络","土壤监测",{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[123,128],{"code":124,"slug":125,"name":126,"path":127,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":130},{"candidates":131,"agricultural_evidence":46},[132,138,144,149,154,161,168,174],{"code":117,"score":19,"supported":46,"title":133,"abstract":135,"tags":137},[134],"wireless sensor",[136,134],"无线传感",[114],{"code":124,"score":139,"supported":46,"title":140,"abstract":141,"tags":143},14,[125],[142,125],"土壤",[],{"code":31,"score":108,"supported":46,"title":145,"abstract":147,"tags":148},[146],"sustainable agriculture",[],[],{"code":44,"score":108,"supported":46,"title":150,"abstract":151,"tags":153},[],[27,152],"precision agriculture",[26,27],{"code":155,"score":93,"supported":46,"title":156,"abstract":157,"tags":160},"A3",[],[158,159],"玉米","corn",[],{"code":162,"score":93,"supported":46,"title":163,"abstract":164,"tags":167},"A4",[],[165,166],"大豆","soybean",[],{"code":169,"score":53,"supported":46,"title":170,"abstract":171,"tags":173},"F5",[],[172],"harvester",[],{"code":175,"score":21,"supported":14,"title":176,"abstract":177,"tags":178},"I2",[],[],[113],[180,181],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":183,"openalex_id":185,"authors":186,"venue":101,"cited_by_count":82,"oa_url":190,"card":191,"direction":195,"ingested_from":90},"W7214927134",[187],{"name":188,"orcid":189},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","智慧农业 \u002F 农业物联网","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","2026-10-03T23:30:17.793958Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":220,"classification":227,"search_phrases":304,"slug":307,"view_count":20,"doi":308,"paper":309,"created_at":319},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":18,"substance":208,"depth":209,"authority":107,"freshness":108,"relevant":21,"comment":210},20,17,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[212],{"name":204,"url":201},[26,113,214,215,27],"产量预测","可解释AI",{"code":175,"slug":217,"name":218,"path":219,"parent_code":121},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[221,226],{"code":222,"slug":223,"name":224,"path":225,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":228},{"candidates":229,"agricultural_evidence":46},[230,240,247,251,258,265,269,275,282,288,294,300],{"code":175,"score":45,"supported":46,"title":231,"abstract":232,"tags":239},[],[233,234,235,236,237,238],"目标检测","机器学习","计算机视觉","machine learning","artificial intelligence","computer vision",[113,215],{"code":222,"score":241,"supported":46,"title":242,"abstract":243,"tags":246},9,[],[244,245,223],"灌溉","水资源",[],{"code":31,"score":108,"supported":46,"title":248,"abstract":249,"tags":250},[146],[],[],{"code":252,"score":93,"supported":46,"title":253,"abstract":254,"tags":257},"A8",[],[255,256],"粮食安全","food security",[],{"code":259,"score":93,"supported":46,"title":260,"abstract":261,"tags":264},"F3",[],[262,263],"肥料","fertilizer",[],{"code":124,"score":93,"supported":46,"title":266,"abstract":267,"tags":268},[],[142,125],[],{"code":117,"score":93,"supported":46,"title":270,"abstract":271,"tags":274},[],[272,273],"物联网","internet of things",[],{"code":276,"score":277,"supported":46,"title":278,"abstract":279,"tags":281},"I3",4,[],[280],"遥感",[214],{"code":283,"score":53,"supported":46,"title":284,"abstract":285,"tags":287},"A7",[],[286],"间作",[],{"code":289,"score":53,"supported":46,"title":290,"abstract":291,"tags":293},"F4",[],[292],"除草",[],{"code":295,"score":53,"supported":46,"title":296,"abstract":297,"tags":299},"H7",[],[298],"气候变化",[],{"code":44,"score":20,"supported":14,"title":301,"abstract":302,"tags":303},[],[],[26,27],[305,306],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":308,"openalex_id":310,"authors":311,"venue":204,"cited_by_count":82,"oa_url":201,"card":314,"direction":195,"ingested_from":90},"W7212616413",[312],{"name":313,"orcid":9},"Manish Maan",{"tldr":315,"method":316,"finding":317,"direction":87,"opportunity":318},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":337,"secondary_topics":338,"classification":339,"search_phrases":358,"slug":361,"view_count":21,"doi":362,"paper":363,"created_at":375},1971,"Can industry 5.0’s human-centric approach fulfill industry 4.0’s unrealized promise to agriculture?","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102551","Agriculture sustains the world's growing population while facing a shrinking workforce increasingly reliant on temporary labor. Following the trajectory of industrial revolutions 1.0 through 3.0, agriculture has continuously adapted and improved its practices to meet rising food demand. Industry 4.0′s data-driven technologies, including artificial intelligence, smart sensors, robots, drones, and automated monitoring and decision-support systems, hold great promises for advancing sustainable agriculture and reducing dependency on human labor. However, the transition to Agriculture 4.0 has proven challenging, and the technologies have not been adopted as widely as expected. Adoption barriers stem from uncertainty about cost-effectiveness, driven by substantial initial investments in new technologies, and from uncertainty about how they integrate with existing workflows, alter operational processes, and affect efficiency, labor costs, and required skill sets, often exceeding farmers' knowledge bases and technical capacities. Recently, the Industry 5.0 framework emerged as an evolution, providing direction toward a human-centric, resilient, and sustainable industry. In this paper, I argue that Human Factors and Ergonomics (HF\u002FE), a scientific discipline concerned with human-system interactions and the application of evidence-based methods to inform human-centered design, has been largely absent from agricultural research and development discourse in Agriculture 4.0. Incorporating HF\u002FE methods into agricultural research and development has the potential to operationalize Industry 5.0′s human-centric vision, offering structured, evidence-based guidance on how to translate policy ambition into practical design and development, an approach that would benefit agricultural livelihoods and economic viability while helping agriculture meet global food demands.","农业支撑着全球不断增长的人口，却面临着日益缩减且愈发依赖临时劳动力的困境。沿循工业革命1.0至3.0的发展轨迹，农业不断调整和改进其生产方式，以满足日益增长的粮食需求。工业4.0时代的数据驱动技术，包括人工智能、智能传感器、机器人、无人机以及自动化监测和决策支持系统，为推进可持续农业、减少对人力的依赖带来了巨大希望。然而，向农业4.0的转型已被证明充满挑战，相关技术的普及程度远未达到预期。采用障碍源于对成本效益的不确定性——这由新技术所需的巨额初始投资驱动——以及对技术如何融入现有工作流程、改变操作过程、影响效率、劳动力成本和所需技能组合的不确定性，这些往往超出了农民的知识基础和技术能力。近期，工业5.0框架应运而生，作为一次演进，为以人为本、富有韧性和可持续的工业指明了方向。本文认为，人因学与工效学（Human Factors and Ergonomics, HF\u002FE）——一门关注人-系统交互、并应用循证方法为以人为本的设计提供依据的科学学科——在农业4.0的农业研究与发展讨论中基本缺席。将人因学与工效学方法纳入农业研究与发展，有望将工业5.0以人为本的愿景付诸实践，为如何将政策雄心转化为实际设计与发展提供结构化、循证的指导，这一路径将惠及农业生计和经济可行性，同时助力农业满足全球粮食需求。","Smart Agricultural Technology","2026-09-06T00:00:00Z",75,{"impact":18,"substance":105,"depth":18,"authority":107,"freshness":277,"relevant":21,"comment":330},"论文深入探讨农业4.0采纳障碍，提出以人因工程落实工业5.0人本理念，视角新颖，对智慧农业推广有参考价值。",[332],{"name":326,"url":323},[26,29,334,335,336],"人因工程","农业4.0","工业5.0",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346,350,354],{"code":31,"score":93,"supported":46,"title":343,"abstract":344,"tags":345},[],[56,146],[],{"code":175,"score":53,"supported":46,"title":347,"abstract":348,"tags":349},[],[237],[],{"code":59,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[29],{"code":44,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[26],[359,360],"人因工程 技术采纳 智慧农业 农业4.0","人因工程 技术采纳","人因工程技术采纳智慧农业农业4.0-1971","10.1016\u002Fj.atech.2026.102551",{"doi":362,"openalex_id":364,"authors":365,"venue":326,"cited_by_count":82,"oa_url":369,"card":370,"direction":87,"ingested_from":90},"W7210273202",[366],{"name":367,"orcid":368},"Yael Salzer","https:\u002F\u002Forcid.org\u002F0000-0001-9771-0452","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007768\u002Fpdf",{"tldr":371,"method":372,"finding":373,"direction":87,"opportunity":374},"论文主张将人因工程融入农业4.0，以实现工业5.0以人为本的愿景，促进技术采纳。","论述人因工程（HF\u002FE）在农业研发中的缺失，提出将其作为实现工业5.0以人为本的","农业4.0技术采纳受阻，人因工程可提供结构化指导，促进技术应用，提升农业可持续性。","研究人因工程在农业技术设计中的应用，如用户中心设计，可提高技术采纳率，填补农业信息化中人的因素研究空白。","2026-09-09T23:30:03.531577Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":390,"secondary_topics":391,"classification":394,"search_phrases":443,"slug":446,"view_count":20,"doi":447,"paper":448,"created_at":460},1305,"IoT-Based Soil Condition Monitoring System for Supporting Precision and Sustainable Agriculture in West Java","https:\u002F\u002Fdoi.org\u002F10.55981\u002Fjet.807","West Java Province is one of the largest horticultural producers in Indonesia. The horticulture subsector in West Java covers tens of thousands of hectares of farmland and contributes approximately 1.8% to the province’s total Gross Regional Domestic Product (GRDP). However, this sector faces serious challenges. Climate change has increased the frequency of droughts and floods, disrupting horticultural production. In addition, soil fertility has declined due to unsustainable farming practices; many agricultural lands in West Java contain less than 2% organic matter as a result of long-term chemical fertilizer use. These challenges have led to decreased productivity and threaten the sustainability of horticultural farming. Precision agriculture emerges as a solution to these issues. This concept leverages IoT technologies such as the ESP32 microcontroller with SPIFFS, soil sensors, and a web-based dashboard to monitor land conditions in real time and manage inputs more efficiently. The IoT-based soil monitoring system developed in this study was tested in a farmer’s yard in Pameungpeuk Subdistrict, Garut. The results demonstrated practical benefits, including improved irrigation efficiency (up to ~30% water savings) and reduced risk of crop losses through early detection of drought conditions. Moreover, the application of IoT in potato cultivation in West Java has been shown to double seed tuber yields from 15,000 to 30,000 tubers. These findings are consistent with regional policy directions, as reflected in the West Java Regional Medium-Term Development Plan (RPJMD) and the “Petani Milenial” (Millennial Farmers) program, which promote sustainable agricultural modernization through IoT-based technologies to enhance the productivity of young farmers.","西爪哇省是印度尼西亚最大的园艺生产地之一。该省的园艺分部门覆盖数万公顷农田，约占全省地区生产总值（GRDP）总额的1.8%。然而，该部门面临严峻挑战。气候变化增加了干旱和洪水的发生频率，扰乱了园艺生产。此外，由于不可持续的耕作方式，土壤肥力下降；长期使用化肥导致西爪哇许多农业用地的有机质含量低于2%。这些挑战导致生产力下降，并威胁到园艺农业的可持续性。精准农业应运而生，成为解决这些问题的方案。这一概念利用物联网（IoT）技术，如搭载SPIFFS的ESP32微控制器、土壤传感器和基于网页的仪表盘，实时监测土地状况并更高效地管理投入。本研究中开发的基于物联网的土壤监测系统在加鲁特县帕梅翁佩乌克分区的一位农民庭院中进行了测试。结果表明其具有实际效益，包括提高灌溉效率（节水约30%）以及通过早期检测干旱状况降低作物损失风险。此外，物联网在西爪哇马铃薯种植中的应用已被证明可使种薯产量翻倍，从每公顷15,000个增加到30,000个。这些发现与区域政策方向一致，正如西爪哇省中期发展规划（RPJMD）和“千禧农民”计划所反映的那样，这些政策通过基于物联网的技术促进可持续农业现代化，以提高年轻农民的生产力。","Jurnal Elektronika dan Telekomunikasi",61,{"impact":19,"substance":18,"depth":385,"authority":13,"freshness":20,"relevant":21,"comment":386},16,"论文展示IoT土壤监测系统在西爪哇的应用，节水30%并提升马铃薯产量，对区域精准农业有参考价值，但时效性较低。",[388],{"name":382,"url":379},[26,272,27,56,115],{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},[392,393],{"code":124,"slug":125,"name":126,"path":127,"parent_code":35},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":395},{"candidates":396,"agricultural_evidence":46},[397,402,406,410,417,421,428,432,439],{"code":117,"score":19,"supported":46,"title":398,"abstract":400,"tags":401},[399],"IoT",[272,399],[272],{"code":124,"score":139,"supported":46,"title":403,"abstract":404,"tags":405},[125],[142,125],[],{"code":31,"score":17,"supported":46,"title":407,"abstract":408,"tags":409},[146],[56],[56],{"code":295,"score":241,"supported":46,"title":411,"abstract":412,"tags":416},[],[298,413,414,415],"干旱","climate change","drought",[],{"code":44,"score":108,"supported":46,"title":418,"abstract":419,"tags":420},[],[27,152],[26,27],{"code":422,"score":93,"supported":46,"title":423,"abstract":424,"tags":427},"A5",[],[425,426],"马铃薯","potato",[],{"code":222,"score":93,"supported":46,"title":429,"abstract":430,"tags":431},[],[244,223],[],{"code":433,"score":93,"supported":46,"title":434,"abstract":435,"tags":438},"J2",[],[436,437],"农业现代化","agricultural modernization",[],{"code":259,"score":53,"supported":46,"title":440,"abstract":441,"tags":442},[],[263],[],[444,445],"可持续农业 土壤监测 智慧农业 精准农业","可持续农业 土壤监测","可持续农业土壤监测智慧农业精准农业-1305","10.55981\u002Fjet.807",{"doi":447,"openalex_id":449,"authors":450,"venue":382,"cited_by_count":82,"oa_url":379,"card":455,"direction":195,"ingested_from":90},"W7204810936",[451,453],{"name":452,"orcid":9},"Ayu Latifah",{"name":454,"orcid":9},"Dendi Ardimansah",{"tldr":456,"method":457,"finding":458,"direction":195,"opportunity":459},"开发基于IoT的土壤监测系统，支持西爪哇精准可持续农业，提高灌溉效率并增产。","使用ESP32微控制器、SPIFFS、土壤传感器和Web仪表盘构建实时监测系统。","系统节省约30%灌溉用水，马铃薯种薯产量翻倍至3万块，符合区域政策。","可扩展至更多作物和地区，结合机器学习预测土壤养分，优化施肥策略，提升系统普适性。","2026-09-01T23:30:12.797772Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":9,"source_name":467,"source_url":464,"published_at":468,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":469,"sources":471,"tags":473,"primary_topic":478,"secondary_topics":483,"classification":490,"search_phrases":544,"slug":547,"view_count":53,"doi":548,"paper":549,"created_at":604},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",{"impact":18,"substance":105,"depth":18,"authority":107,"freshness":241,"relevant":21,"comment":470},"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[472],{"name":467,"url":464},[26,474,475,476,477],"精准施肥","小麦","保护性耕作","水肥一体化",{"code":479,"slug":480,"name":475,"path":481,"parent_code":482},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[484,489],{"code":259,"slug":485,"name":486,"path":487,"parent_code":488},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":491},{"candidates":492,"agricultural_evidence":46},[493,499,503,508,513,517,521,525,530,536,540],{"code":259,"score":385,"supported":46,"title":494,"abstract":496,"tags":498},[495],"fertigation",[497,495],"施肥",[477,474],{"code":479,"score":19,"supported":46,"title":500,"abstract":501,"tags":502},[480],[475,480],[475],{"code":31,"score":17,"supported":46,"title":504,"abstract":506,"tags":507},[505],"conservation agriculture",[505],[476],{"code":155,"score":93,"supported":46,"title":509,"abstract":510,"tags":512},[],[158,511],"maize",[],{"code":252,"score":93,"supported":46,"title":514,"abstract":515,"tags":516},[],[255,256],[],{"code":124,"score":93,"supported":46,"title":518,"abstract":519,"tags":520},[],[142,125],[],{"code":222,"score":93,"supported":46,"title":522,"abstract":523,"tags":524},[],[244,223],[],{"code":283,"score":53,"supported":46,"title":526,"abstract":527,"tags":529},[],[528],"轮作",[],{"code":531,"score":53,"supported":46,"title":532,"abstract":533,"tags":535},"H5",[],[534],"秸秆还田",[],{"code":295,"score":53,"supported":46,"title":537,"abstract":538,"tags":539},[],[413],[],{"code":44,"score":21,"supported":14,"title":541,"abstract":542,"tags":543},[],[],[26],[545,546],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":548,"openalex_id":550,"authors":551,"venue":467,"cited_by_count":82,"oa_url":464,"card":599,"direction":195,"ingested_from":90},"W7214579192",[552,554,557,559,561,563,565,568,570,572,574,576,578,581,583,585,588,591,594,597],{"name":553,"orcid":9},"Kadagonda Nithinkumar",{"name":555,"orcid":556},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":558,"orcid":9},"Devika A. R.",{"name":560,"orcid":9},"Arjun Singh",{"name":562,"orcid":9},"Susama Sudhishri",{"name":564,"orcid":9},"Vinod Kumar Singh",{"name":566,"orcid":567},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":569,"orcid":9},"Sarvendra Kumar",{"name":571,"orcid":9},"Teekam Singh",{"name":573,"orcid":9},"Jitendra Rajput",{"name":575,"orcid":9},"Pravin Kumar Upadhyaya",{"name":577,"orcid":9},"R. N. Sahoo",{"name":579,"orcid":580},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":582,"orcid":9},"Aye Aye San",{"name":584,"orcid":9},"K. S. Sachin",{"name":586,"orcid":587},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":589,"orcid":590},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":592,"orcid":593},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":595,"orcid":596},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":598,"orcid":9},"Subhash Babu",{"tldr":600,"method":601,"finding":602,"direction":195,"opportunity":603},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","2026-09-29T23:30:06.859116Z",{"id":606,"title":607,"url":608,"summary":609,"summary_zh":610,"content":9,"source_name":611,"source_url":608,"published_at":612,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":613,"score_detail":614,"sources":616,"tags":618,"primary_topic":622,"secondary_topics":623,"classification":626,"search_phrases":649,"slug":652,"view_count":108,"doi":653,"paper":654,"created_at":674},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。","AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z",77,{"impact":18,"substance":208,"depth":18,"authority":107,"freshness":108,"relevant":21,"comment":615},"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[617],{"name":611,"url":608},[26,113,619,620,621],"智能灌溉","节水农业","成本效益分析",{"code":222,"slug":223,"name":224,"path":225,"parent_code":35},[624,625],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":175,"slug":217,"name":218,"path":219,"parent_code":121},{"status":38,"locked":14,"version":39,"method":40,"evidence":627},{"candidates":628,"agricultural_evidence":46},[629,633,637,641,645],{"code":222,"score":106,"supported":46,"title":630,"abstract":631,"tags":632},[223],[244,245,223],[619,620],{"code":31,"score":139,"supported":46,"title":634,"abstract":635,"tags":636},[146],[56,146],[],{"code":175,"score":13,"supported":46,"title":638,"abstract":639,"tags":640},[],[234,236,237],[113],{"code":124,"score":93,"supported":46,"title":642,"abstract":643,"tags":644},[],[142,125],[],{"code":44,"score":21,"supported":14,"title":646,"abstract":647,"tags":648},[],[],[26],[650,651],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":653,"openalex_id":655,"authors":656,"venue":611,"cited_by_count":82,"oa_url":608,"card":669,"direction":195,"ingested_from":90},"W7214115789",[657,659,661,663,665,667],{"name":658,"orcid":9},"Venkata Suman Jami",{"name":660,"orcid":9},"Purushotham Prasad Kalisetti",{"name":662,"orcid":9},"Sampath Dakshina Murthy A.",{"name":664,"orcid":9},"Gurunadha R.",{"name":666,"orcid":9},"Hema Mamidipaka",{"name":668,"orcid":9},"Gurrapu Omprakash",{"tldr":670,"method":671,"finding":672,"direction":195,"opportunity":673},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","2026-09-24T23:30:10.558578Z"]