[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3049":3,"related-3049":94},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":78,"slug":81,"view_count":82,"doi":8,"paper":83,"created_at":93},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。",null,"MDPI Agronomy 16(18):1853","2026-09-20T00:00:00Z","论文",10,false,68,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,9,1,"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"决策支持系统","精准农业","无人机遥感","智慧灌溉","土壤压实",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":44,"locked":13,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,55,60,66,73],{"code":32,"score":16,"supported":50,"title":51,"abstract":53,"tags":54},true,[52],"灌溉",[52],[29],{"code":56,"score":16,"supported":50,"title":57,"abstract":58,"tags":59},"I1",[27],[27],[27],{"code":39,"score":61,"supported":50,"title":62,"abstract":64,"tags":65},11,[63],"土壤",[63],[],{"code":67,"score":68,"supported":50,"title":69,"abstract":70,"tags":72},"I3",4,[],[71],"遥感",[28],{"code":74,"score":21,"supported":13,"title":75,"abstract":76,"tags":77},"I2",[],[],[26],[79,80],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",2,{"doi":8,"openalex_id":8,"authors":84,"venue":8,"cited_by_count":85,"oa_url":8,"card":86,"direction":90,"ingested_from":92},[],0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","智慧农业 \u002F 农业物联网","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,192,301,366,442,512],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":8,"source_name":103,"source_url":100,"published_at":104,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":105,"score_detail":106,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":116,"classification":118,"search_phrases":152,"slug":155,"view_count":82,"doi":156,"paper":157,"created_at":191},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。","Earth System Science Data Discussions","2026-09-23T00:00:00Z",80,{"impact":17,"substance":107,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":108},22,"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[110],{"name":103,"url":100},[112,27,113,114,29],"农业遥感","遥感监测","土壤墒情",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[117],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":119},{"candidates":120,"agricultural_evidence":50},[121,126,131,138,142,148],{"code":39,"score":122,"supported":50,"title":123,"abstract":124,"tags":125},15,[40],[63,40],[114],{"code":32,"score":12,"supported":50,"title":127,"abstract":128,"tags":130},[],[52,129,33],"水资源",[29],{"code":132,"score":95,"supported":50,"title":133,"abstract":134,"tags":137},"H7",[],[135,136],"干旱","drought",[],{"code":56,"score":68,"supported":50,"title":139,"abstract":140,"tags":141},[],[27],[27],{"code":74,"score":143,"supported":50,"title":144,"abstract":145,"tags":147},3,[],[146],"decision support",[],{"code":67,"score":82,"supported":13,"title":149,"abstract":150,"tags":151},[],[],[112,113],[153,154],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":156,"openalex_id":158,"authors":159,"venue":103,"cited_by_count":85,"oa_url":100,"card":183,"direction":189,"ingested_from":190},"W7214076150",[160,163,165,167,170,172,174,177,180],{"name":161,"orcid":162},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":164,"orcid":8},"Enas Abdelsamei",{"name":166,"orcid":8},"Viktoria Blanka-Vegi",{"name":168,"orcid":169},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":171,"orcid":8},"Ahmed M. Ali",{"name":173,"orcid":8},"Khaldoun Abualhin",{"name":175,"orcid":176},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":178,"orcid":179},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":181,"orcid":182},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",{"tldr":184,"method":185,"finding":186,"direction":187,"opportunity":188},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","农业遥感与作物表型","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","农业人工智能与决策模型","openalex","2026-09-24T23:30:36.864649Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":8,"source_name":198,"source_url":195,"published_at":199,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":201,"sources":204,"tags":206,"primary_topic":210,"secondary_topics":214,"classification":217,"search_phrases":272,"slug":275,"view_count":85,"doi":276,"paper":277,"created_at":300},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":19,"substance":17,"depth":122,"authority":16,"freshness":202,"relevant":21,"comment":203},8,"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[205],{"name":198,"url":195},[207,208,113,29,209],"气候变化","盐碱地治理","土壤盐渍化",{"code":132,"slug":211,"name":212,"path":213,"parent_code":36},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[215,216],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":218},{"candidates":219,"agricultural_evidence":50},[220,226,230,235,241,248,253,260,266],{"code":132,"score":17,"supported":50,"title":221,"abstract":223,"tags":225},[222],"climate change",[207,224,135,222],"气候适应",[207],{"code":39,"score":122,"supported":50,"title":227,"abstract":228,"tags":229},[40],[63,40],[208],{"code":32,"score":12,"supported":50,"title":231,"abstract":232,"tags":234},[],[52,129,33,233],"evapotranspiration",[29],{"code":67,"score":236,"supported":50,"title":237,"abstract":238,"tags":240},7,[],[71,239],"remote sensing",[113],{"code":242,"score":95,"supported":50,"title":243,"abstract":244,"tags":247},"A8",[],[245,246],"粮食安全","food security",[],{"code":56,"score":95,"supported":50,"title":249,"abstract":250,"tags":252},[],[27,251],"precision agriculture",[],{"code":254,"score":95,"supported":50,"title":255,"abstract":256,"tags":259},"I4",[],[257,258],"地理信息","geographic information",[],{"code":261,"score":143,"supported":50,"title":262,"abstract":263,"tags":265},"F3",[],[264],"养分管理",[],{"code":267,"score":143,"supported":50,"title":268,"abstract":269,"tags":271},"J1",[],[270],"农业政策",[],[273,274],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":276,"openalex_id":278,"authors":279,"venue":198,"cited_by_count":85,"oa_url":195,"card":295,"direction":187,"ingested_from":190},"W7222999158",[280,282,284,286,288,290,293],{"name":281,"orcid":8},"Asifa Naz",{"name":283,"orcid":8},"Muhammad Abu Baker Siddiq",{"name":285,"orcid":8},"Syed Asghar Hussain Shah",{"name":287,"orcid":8},"Shahrish Nawaz",{"name":289,"orcid":8},"Muhmmad Irfan",{"name":291,"orcid":292},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":294,"orcid":8},"Muhammad Nouman Khalid",{"tldr":296,"method":297,"finding":298,"direction":187,"opportunity":299},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":308,"score_detail":309,"sources":314,"tags":316,"primary_topic":320,"secondary_topics":321,"classification":323,"search_phrases":346,"slug":349,"view_count":82,"doi":350,"paper":351,"created_at":365},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。","Agronomy",78,{"impact":17,"substance":310,"depth":311,"authority":312,"freshness":202,"relevant":21,"comment":313},21,17,14,"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[315],{"name":307,"url":304},[317,112,318,319,30],"智慧农业","精准灌溉","土壤墒情监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[322],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":324},{"candidates":325,"agricultural_evidence":50},[326,330,334,338,342],{"code":32,"score":17,"supported":50,"title":327,"abstract":328,"tags":329},[33],[52,129,33],[318],{"code":39,"score":312,"supported":50,"title":331,"abstract":332,"tags":333},[40],[63,40],[],{"code":56,"score":20,"supported":50,"title":335,"abstract":336,"tags":337},[251],[],[317],{"code":67,"score":236,"supported":50,"title":339,"abstract":340,"tags":341},[],[71,239],[112],{"code":74,"score":143,"supported":50,"title":343,"abstract":344,"tags":345},[],[146],[],[347,348],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184","10.3390\u002Fagronomy16181853",{"doi":350,"openalex_id":352,"authors":353,"venue":307,"cited_by_count":85,"oa_url":304,"card":360,"direction":187,"ingested_from":190},"W7213866178",[354,357],{"name":355,"orcid":356},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":358,"orcid":359},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":361,"method":362,"finding":363,"direction":90,"opportunity":364},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","2026-09-22T23:30:25.358657Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":8,"source_name":372,"source_url":369,"published_at":373,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":374,"score_detail":375,"sources":378,"tags":380,"primary_topic":385,"secondary_topics":386,"classification":394,"search_phrases":419,"slug":422,"view_count":423,"doi":424,"paper":425,"created_at":441},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",75,{"impact":17,"substance":376,"depth":311,"authority":16,"freshness":202,"relevant":21,"comment":377},20,"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[379],{"name":372,"url":369},[317,381,382,383,27,384],"农业人工智能","物联网","智能灌溉","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[387,393],{"code":388,"slug":389,"name":390,"path":391,"parent_code":392},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":395},{"candidates":396,"agricultural_evidence":50},[397,401,406,410,414],{"code":32,"score":17,"supported":50,"title":398,"abstract":399,"tags":400},[33],[52,129,33],[383],{"code":388,"score":122,"supported":50,"title":402,"abstract":404,"tags":405},[403],"IoT",[382,403],[382],{"code":39,"score":312,"supported":50,"title":407,"abstract":408,"tags":409},[40],[63,40],[],{"code":56,"score":202,"supported":50,"title":411,"abstract":412,"tags":413},[],[27,251],[317,27],{"code":74,"score":68,"supported":50,"title":415,"abstract":416,"tags":418},[],[417],"artificial intelligence",[381],[420,421],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140",5,"10.22214\u002Fijraset.2026.84727",{"doi":424,"openalex_id":426,"authors":427,"venue":372,"cited_by_count":85,"oa_url":369,"card":436,"direction":90,"ingested_from":190},"W7212115545",[428,430,432,434],{"name":429,"orcid":8},"Gowri M.",{"name":431,"orcid":8},"Boomika M.",{"name":433,"orcid":8},"S. Rakshana",{"name":435,"orcid":8},"Rubali R.",{"tldr":437,"method":438,"finding":439,"direction":90,"opportunity":440},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":8,"source_name":448,"source_url":445,"published_at":449,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":460,"secondary_topics":461,"classification":463,"search_phrases":486,"slug":489,"view_count":85,"doi":490,"paper":491,"created_at":511},5541,"Year-round modeling of soil water-heat-salt transport and crop growth under film-mulched drip irrigation with freeze-thaw cycles","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105024","Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.","背景 季节性冻土区覆膜滴灌农田的土壤水热盐迁移与作物生长，受生育期内部分覆膜和滴灌以及休止期土壤冻融过程的影响。理解全年土壤水热盐动态及作物生长对于农业可持续用水至关重要。目的 然而，现有全年模型在生育期多采用一维（1D）方法，忽略了部分覆膜滴灌条件下的水平盐分迁移，导致对盐分淋洗效率的高估。这一局限可通过开发兼顾生育期与休止期不同动态特征的全年模型加以解决。方法 本研究将前期开发的基于HYDRUS-2D的作物与水分模型（CAWM-H2D）与考虑土壤冻融过程的SHAW模型相耦合，构建了全年耦合模型CAWM-S。CAWM-S的主要特点包括：详细刻画生育期二维土壤水热盐迁移、作物生长与产量形成以及覆膜土壤表面能量平衡，同时描述休止期冻融过程引起的土壤水盐再分布。模型采用南疆季节性冻土区覆膜滴灌棉田连续两个全年周期的监测数据，在不同灌溉水矿化度水平下进行率定与验证。该验证按处理进行，且仅限于同一站点和两年时段；仍需在其他站点、年份、作物及灌溉系统下开展独立验证。结果与结论 在两个不同矿化度水平的年份中，土壤水分、温度、盐分、作物生长指标及产量的模拟结果与实测数据总体吻合，处理平均R²为0.48~0.98，NSE为0.42~0.97。此外，分析了不同灌水量与矿化度组合对土壤脱盐率、蒸散及其组分、作物产量和水分生产率的影响，揭示了覆膜滴灌条件下最优灌水量与灌溉水矿化度之间的二次关系。验证结果表明，CAWM-S能够有效量化土壤水热盐耦合动态与作物生长，并预测","Agricultural Systems","2026-10-21T00:00:00Z",79,{"impact":18,"substance":107,"depth":17,"authority":312,"freshness":20,"relevant":21,"comment":452},"构建了覆盖冻融期的全年水热盐与作物生长耦合模型，方法有新意、验证数据扎实，对盐碱地滴灌节水有实用价值，但属单点两年验证的学术成果，产业影响有限。",[454],{"name":448,"url":445},[456,208,457,458,459],"棉花","节水灌溉","覆膜滴灌","土壤水盐运移",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[462],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":464},{"candidates":465,"agricultural_evidence":50},[466,470,474,480],{"code":32,"score":17,"supported":50,"title":467,"abstract":468,"tags":469},[33],[52,33,233],[457],{"code":39,"score":122,"supported":50,"title":471,"abstract":472,"tags":473},[40],[63,40],[208],{"code":475,"score":68,"supported":50,"title":476,"abstract":477,"tags":479},"A6",[],[478],"cotton",[456],{"code":481,"score":143,"supported":50,"title":482,"abstract":483,"tags":485},"H8",[],[484],"sustainable agriculture",[],[487,488],"南疆 覆膜滴灌 棉花","CAWM-S 模型 冻融","南疆覆膜滴灌棉花-5541","10.1016\u002Fj.agsy.2026.105024",{"doi":490,"openalex_id":492,"authors":493,"venue":448,"cited_by_count":85,"oa_url":445,"card":506,"direction":187,"ingested_from":190},"W7223120642",[494,497,499,502,504],{"name":495,"orcid":496},"Dong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-8433-9658",{"name":498,"orcid":8},"Tongkai Guo",{"name":500,"orcid":501},"Zunqiu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9393-3605",{"name":503,"orcid":8},"Wenping Bi",{"name":505,"orcid":8},"Xiaomin Mao",{"tldr":507,"method":508,"finding":509,"direction":187,"opportunity":510},"构建全年耦合模型CAWM-S，模拟覆膜滴灌棉田水热盐运移与作物生长。","将HYDRUS-2D的CAWM-H2D与冻融模型SHAW耦合，用南疆两年监测数据","模型精度较高（R²0.48-0.98），揭示最优灌溉量与灌水盐度呈二次关系。","可扩展至多站点、多作物与不同灌溉制度的独立验证，并耦合遥感数据实现区域尺度模拟。","2026-10-22T11:20:12.238655Z",{"id":513,"title":514,"url":515,"summary":516,"summary_zh":8,"content":8,"source_name":448,"source_url":515,"published_at":517,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":518,"score_detail":519,"sources":522,"tags":524,"primary_topic":527,"secondary_topics":528,"classification":530,"search_phrases":545,"slug":548,"view_count":85,"doi":549,"paper":550,"created_at":568},5229,"Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105017","Integrating HYDRUS-2D and AquaCrop for simulating soil water–salt dynamics and crop growth under film-mulched drip irrigation。Agricultural Systems","2026-10-17T00:00:00Z",82,{"impact":17,"substance":107,"depth":520,"authority":312,"freshness":20,"relevant":21,"comment":521},19,"耦合HYDRUS-2D与AquaCrop模拟覆膜滴灌下水盐与作物生长，方法有创新、结论可靠，属农业信息化与节水灌溉领域值得精选的论文。",[523],{"name":448,"url":515},[525,457,458,526],"作物模型","水盐调控",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[529],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":13,"version":45,"method":46,"evidence":531},{"candidates":532,"agricultural_evidence":50},[533,537,541],{"code":32,"score":16,"supported":50,"title":534,"abstract":535,"tags":536},[33],[33],[457],{"code":39,"score":61,"supported":50,"title":538,"abstract":539,"tags":540},[40],[40],[],{"code":74,"score":21,"supported":13,"title":542,"abstract":543,"tags":544},[],[],[525],[546,547],"HYDRUS-2D AquaCrop 覆膜滴灌","土壤水盐 作物生长 模拟","HYDRUS-2DAquaCrop覆膜滴灌-5229","10.1016\u002Fj.agsy.2026.105017",{"doi":549,"openalex_id":551,"authors":552,"venue":448,"cited_by_count":85,"oa_url":515,"card":8,"direction":8,"ingested_from":190},"W7222801463",[553,555,557,559,561,564,566],{"name":554,"orcid":8},"Yunfeng Li",{"name":556,"orcid":8},"Jun Wang",{"name":558,"orcid":8},"Hexiang Zheng",{"name":560,"orcid":8},"Delong Tian",{"name":562,"orcid":563},"Jie Ren","https:\u002F\u002Forcid.org\u002F0000-0001-8795-0372",{"name":565,"orcid":8},"Hao Liu",{"name":567,"orcid":8},"Jingsheng Sun","2026-10-18T23:30:04.248249Z"]