[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3326":3,"related-3326":119},{"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":38,"search_phrases":103,"slug":106,"view_count":107,"doi":8,"paper":108,"created_at":118},3326,"Research on the Application of Agricultural Big Data in Plant Growth Prediction——基于多源数据同化与混合智能（MDA-HI）框架","https:\u002F\u002Fwww.icck.org\u002Ffilebob\u002Fuploads\u002Fstorage\u002FDIA_ANrbHOHS4uBBpnVQ6.pdf","《Digital Intelligence in Agriculture》2026年第2卷第2期。Wei Yongqiang等提出Multi-source Data Assimilation and Hybrid Intelligence（MDA-HI）框架，将基于过程的作物模型与集成机器学习算法（包括基于Transformer的架构和物理信息神经网络）相结合。在2023—2025年中国多生态区主要作物（水稻、小麦、玉米、番茄）的实证验证中：MDA-HI模型实现了产量预测RMSE平均减少42.7%、关键物候期预测减少38.1%。水稻-小麦轮作系统大规模案例研究显示数据驱动处方可将氮肥使用减少22.5%、灌溉水减少18.3%，同时产量增加5.1%。",null,"《Digital Intelligence in Agriculture》2026; 2(2):54-67","2026-09-17T00:00:00Z","论文",10,false,85,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,24,19,14,6,1,"多源数据同化与混合智能框架在四大作物上验证，减肥节水增产数据扎实，方法新颖且具产业推广价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施肥","农业大数据","作物生长预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,64,70,76,82,88,93,98],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},4,true,[],[49],"机器学习",[27],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"A1",3,[],[56],"水稻",[],{"code":59,"score":53,"supported":46,"title":60,"abstract":61,"tags":63},"A2",[],[62],"小麦",[],{"code":65,"score":53,"supported":46,"title":66,"abstract":67,"tags":69},"A3",[],[68],"玉米",[],{"code":71,"score":53,"supported":46,"title":72,"abstract":73,"tags":75},"A7",[],[74],"轮作",[],{"code":77,"score":53,"supported":46,"title":78,"abstract":79,"tags":81},"B1",[],[80],"番茄",[],{"code":83,"score":53,"supported":46,"title":84,"abstract":85,"tags":87},"H3",[],[86],"灌溉",[],{"code":89,"score":21,"supported":13,"title":90,"abstract":91,"tags":92},"F3",[],[],[28],{"code":94,"score":21,"supported":13,"title":95,"abstract":96,"tags":97},"I1",[],[],[26],{"code":99,"score":21,"supported":13,"title":100,"abstract":101,"tags":102},"I6",[],[],[29],[104,105],"MDA-HI 多源数据同化 作物模型","水稻小麦轮作 氮肥减量 产量预测","MDA-HI多源数据同化作物模型-3326",8,{"doi":8,"openalex_id":8,"authors":109,"venue":8,"cited_by_count":110,"oa_url":8,"card":111,"direction":115,"ingested_from":117},[],0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"提出MDA-HI框架，融合过程模型与混合智能，用于作物生长与产量预测。","多源数据同化结合Transformer与物理信息神经网络，在中国多生态区验证。","产量预测RMSE降42.7%，氮肥减22.5%、灌溉水减18.3%，产量增5.1%。","农业人工智能与决策模型","可探索轻量化MDA-HI在边缘设备部署及跨区域迁移能力，降低小农户应用门槛。","agent","2026-09-24T00:04:02.947080Z",{"total":20,"page":21,"page_size":20,"items":120},[121,217,292,401,481,555],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":8,"source_name":127,"source_url":124,"published_at":128,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":129,"score_detail":130,"sources":136,"tags":138,"primary_topic":141,"secondary_topics":142,"classification":143,"search_phrases":191,"slug":194,"view_count":110,"doi":195,"paper":196,"created_at":216},5559,"Smart AI-Based Agriculture Decision Support System (AgriSens)","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85162","Traditional farming relies heavily on empirical guesswork and legacy ancestral knowledge, frequently leaving crops vulnerable to uncalibrated chemical fertilizer application, unpredictable micro-climates, and delayed foliar disease detection. This paper introduces AgriSens, a comprehensive, full-stack smart farming platform developed to empower farmers with datadriven precision agriculture. The system integrates a multi-parameter crop recommendation engine using a mathematical Gaussian-decay environmental fitness model alongside a multimodal leaf disease diagnostic system powered by Google Gemini 3.7 Flash computer vision to identify foliar pathogens from smartphone imagery. Furthermore, the architecture deploys a soiltexture-aware fertilizer optimization engine that calculates precise N-P-K (Nitrogen, Phosphorus, Potassium) nutrient deficits, a real-time agro-meteorological forecasting stream driven by the Open-Meteo API for chemical spray safety alerts, and a closedloop automated IoT irrigation telemetry simulator. To eliminate structural literacy barriers within rural farming communities, the platform features native voice synthesis and a 7-language localized translation module. Simulation benchmarks demonstrate that the platform achieves rapid visual diagnostic latencies under 2.5 seconds and optimizes water conservation by 25% to 35% compared to baseline scheduled flooding methods","传统农业严重依赖经验性猜测和祖辈传承的知识，作物常常面临化肥施用不当、微气候难以预测以及叶部病害发现滞后等问题。本文介绍AgriSens——一个综合性全栈智慧农业平台，旨在以数据驱动的精准农业赋能农民。该系统集成了基于高斯衰减环境适应度数学模型的多元参数作物推荐引擎，以及由Google Gemini 3.7 Flash计算机视觉驱动的多模态叶片病害诊断系统，可从智能手机图像中识别叶部病原体。此外，该架构还部署了土壤质地感知的肥料优化引擎，用于计算精确的氮磷钾（N-P-K）养分亏缺；由Open-Meteo API驱动的实时农业气象预报流，用于提供施药安全预警；以及闭环自动化物联网灌溉遥测模拟器。为消除农村农业社区中的结构性识字障碍，该平台具备原生语音合成功能和7种语言的本地化翻译模块。仿真基准测试表明，该平台实现了低于2.5秒的快速视觉诊断延迟，并且与基准定时漫灌方法相比，将节水效率优化了25%至35%。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":131,"substance":132,"depth":133,"authority":134,"freshness":12,"relevant":21,"comment":135},12,18,16,9,"论文提出集作物推荐、病害诊断、施肥优化与灌溉模拟于一体的全栈平台，方法有创新但尚属仿真验证阶段，具备一定参考价值。",[137],{"name":127,"url":124},[26,27,28,139,140],"智能灌溉","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":144},{"candidates":145,"agricultural_evidence":46},[146,154,160,167,172,179,185],{"code":32,"score":147,"supported":46,"title":148,"abstract":150,"tags":153},15,[149],"decision support",[151,152],"计算机视觉","computer vision",[27],{"code":94,"score":12,"supported":46,"title":155,"abstract":156,"tags":159},[],[26,157,158],"精准农业","precision agriculture",[26],{"code":89,"score":161,"supported":46,"title":162,"abstract":163,"tags":166},7,[],[164,165],"肥料","fertilizer",[28],{"code":83,"score":161,"supported":46,"title":168,"abstract":169,"tags":171},[],[86,170],"irrigation",[139],{"code":173,"score":20,"supported":46,"title":174,"abstract":175,"tags":178},"I5",[],[176,177],"物联网","IoT",[],{"code":180,"score":53,"supported":46,"title":181,"abstract":182,"tags":184},"H2",[],[183],"土壤",[],{"code":186,"score":53,"supported":46,"title":187,"abstract":188,"tags":190},"H7",[],[189],"农业气象",[],[192,193],"AgriSens 智慧农业","Gemini 叶片病害诊断","AgriSens智慧农业-5559","10.22214\u002Fijraset.2026.85162",{"doi":195,"openalex_id":197,"authors":198,"venue":127,"cited_by_count":110,"oa_url":124,"card":209,"direction":214,"ingested_from":215},"W7223158459",[199,201,203,205,207],{"name":200,"orcid":8},"Harshini G. K.",{"name":202,"orcid":8},"Monisha B. P.",{"name":204,"orcid":8},"Manoj G.",{"name":206,"orcid":8},"Chandana K. M.",{"name":208,"orcid":8},"Prof. Sushma S.",{"tldr":210,"method":211,"finding":212,"direction":115,"opportunity":213},"构建AgriSens全栈智慧农业决策支持平台，集成作物推荐、病害诊断、施肥优化与灌溉模拟。","高斯衰减环境适应度模型、Gemini视觉诊断、Open-Meteo API与Io","视觉诊断延迟低于2.5秒，节水25%–35%，并支持7语言语音以降低识字门槛。","可验证多模态病害诊断在真实田间的准确率，并研究语音多语言交互对农户采纳行为的影响。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.985947Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":8,"source_name":223,"source_url":220,"published_at":224,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":225,"score_detail":226,"sources":228,"tags":230,"primary_topic":233,"secondary_topics":238,"classification":244,"search_phrases":271,"slug":274,"view_count":110,"doi":8,"paper":275,"created_at":291},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":131,"substance":133,"depth":147,"authority":131,"freshness":20,"relevant":21,"comment":227},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[229],{"name":223,"url":220},[26,27,28,231,232],"农业物联网","智能农机",{"code":89,"slug":234,"name":235,"path":236,"parent_code":237},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[239,240],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":173,"slug":241,"name":242,"path":243,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":13,"version":40,"method":41,"evidence":245},{"candidates":246,"agricultural_evidence":46},[247,251,257,262,266],{"code":89,"score":147,"supported":46,"title":248,"abstract":249,"tags":250},[165],[164,165],[28],{"code":32,"score":12,"supported":46,"title":252,"abstract":253,"tags":256},[],[49,254,255],"machine learning","artificial intelligence",[27],{"code":173,"score":12,"supported":46,"title":258,"abstract":259,"tags":261},[],[176,260,177],"internet of things",[231],{"code":94,"score":161,"supported":46,"title":263,"abstract":264,"tags":265},[],[157,158],[26],{"code":267,"score":21,"supported":13,"title":268,"abstract":269,"tags":270},"F5",[],[],[232],[272,273],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":8,"openalex_id":276,"authors":277,"venue":223,"cited_by_count":110,"oa_url":220,"card":286,"direction":214,"ingested_from":215},"W7221394563",[278,280,282,284],{"name":279,"orcid":8},"Anjali Manoj Kumbhar",{"name":281,"orcid":8},"Shruti Prashant Kulkarni",{"name":283,"orcid":8},"Snehal Santosh Khune",{"name":285,"orcid":8},"B. B. Godbole",{"tldr":287,"method":288,"finding":289,"direction":214,"opportunity":290},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":8,"source_name":298,"source_url":295,"published_at":299,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":300,"score_detail":301,"sources":306,"tags":308,"primary_topic":310,"secondary_topics":311,"classification":318,"search_phrases":377,"slug":380,"view_count":110,"doi":381,"paper":382,"created_at":400},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":132,"substance":302,"depth":303,"authority":304,"freshness":107,"relevant":21,"comment":305},20,17,13,"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[307],{"name":298,"url":295},[26,27,29,157,309],"遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[312,317],{"code":313,"slug":314,"name":315,"path":316,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":173,"slug":241,"name":242,"path":243,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":319},{"candidates":320,"agricultural_evidence":46},[321,325,332,336,340,347,354,359,363,367,372],{"code":32,"score":12,"supported":46,"title":322,"abstract":323,"tags":324},[],[49,254,255],[27],{"code":313,"score":134,"supported":46,"title":326,"abstract":327,"tags":331},[],[328,329,330],"地理信息","GIS","geographic information",[],{"code":173,"score":134,"supported":46,"title":333,"abstract":334,"tags":335},[],[176,260,177],[],{"code":94,"score":107,"supported":46,"title":337,"abstract":338,"tags":339},[],[157,158],[26,157],{"code":341,"score":161,"supported":46,"title":342,"abstract":343,"tags":346},"I3",[],[344,345],"遥感","remote sensing",[309],{"code":348,"score":20,"supported":46,"title":349,"abstract":350,"tags":353},"A8",[],[351,352],"粮食安全","food security",[],{"code":180,"score":20,"supported":46,"title":355,"abstract":356,"tags":358},[],[183,357],"soil",[],{"code":83,"score":20,"supported":46,"title":360,"abstract":361,"tags":362},[],[86,170],[],{"code":99,"score":45,"supported":46,"title":364,"abstract":365,"tags":366},[],[29],[29],{"code":89,"score":53,"supported":46,"title":368,"abstract":369,"tags":371},[],[370],"养分管理",[],{"code":267,"score":53,"supported":46,"title":373,"abstract":374,"tags":376},[],[375],"农业机械",[],[378,379],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":381,"openalex_id":383,"authors":384,"venue":298,"cited_by_count":110,"oa_url":295,"card":395,"direction":214,"ingested_from":215},"W7220374836",[385,387,389,391,393],{"name":386,"orcid":8},"Kodavali Aswini",{"name":388,"orcid":8},"Raju Marimuthu",{"name":390,"orcid":8},"Guru Gurusamy",{"name":392,"orcid":8},"Kannan Pandian",{"name":394,"orcid":8},"Anandham Rangasamy",{"tldr":396,"method":397,"finding":398,"direction":115,"opportunity":399},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":8,"source_name":407,"source_url":404,"published_at":408,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":421,"classification":423,"search_phrases":450,"slug":453,"view_count":454,"doi":455,"paper":456,"created_at":480},3805,"Machine Learning Prediction of Soil Nutrients and pH in Tibetan Alpine Croplands: A Comparison of Nine Algorithms","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18199875","Soils underpin terrestrial ecosystem functioning and exert strong control over farmland productivity, nutrient turnover, environmental quality, and agricultural sustainability. Reliable prediction of soil nutrients and pH is therefore important for precision fertilization, soil-quality assessment, and the sustainability of farmland management, particularly in fragile alpine agroecosystems. However, the comparative performance of candidate algorithms for estimating soil properties in alpine croplands of the Qinghai-Tibetan Plateau remains poorly characterized. Here, we assessed nine approaches, namely random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive partitioning regression tree (RRT), for predicting soil carbon, nitrogen, phosphorus, potassium, and pH from annual mean temperature, annual precipitation, annual solar radiation, and maximum normalized difference vegetation index (NDVImax). Field observations were obtained from 103 sampling plots, with four individual soil samples collected per plot and depth, resulting in 412 observations for each depth layer. Clear contrasts in model performance emerged under alpine agricultural conditions. Across all soil variables and depths, eXGB and RF showed the best overall performance, with the lowest mean relative bias values of 1.35% and 1.55%, respectively, and the lowest mean RMSE values of 0.43 and 0.47. These results indicate that nonlinear ensemble algorithms better captured the heterogeneous relationships between environmental covariates and soil properties than linear models or single-tree approaches. Overall, RF and eXGB provide a practical methodological basis for precision fertilization, soil-quality assessment, and long-term sustainability in fragile alpine agroecosystems.","土壤是陆地生态系统功能的基础，并对农田生产力、养分周转、环境质量和农业可持续性具有强烈控制作用。因此，可靠预测土壤养分和pH对于精准施肥、土壤质量评估以及农田管理的可持续性十分重要，尤其是在脆弱的高寒农业生态系统中。然而，在青藏高原高寒农田中，用于估算土壤属性的候选算法的比较性能仍缺乏系统表征。本研究评估了九种方法，即随机森林（RF）、广义提升回归（GBR）、多元线性回归（MLR）、人工神经网络（ANN）、广义线性回归（GLR）、条件推断树（CIT）、极端梯度提升（eXGB）、支持向量机（SVM）和递归分割回归树（RRT），利用年平均气温、年降水量、年太阳辐射和最大归一化差异植被指数（NDVImax）预测土壤碳、氮、磷、钾和pH。野外观测来自103个采样样地，每个样地和每个深度采集4个独立土壤样品，每个深度层共获得412个观测值。在高寒农业条件下，模型性能表现出明显差异。在所有土壤变量和深度上，eXGB和RF总体表现最佳，平均相对偏差值最低，分别为1.35%和1.55%，平均RMSE值最低，分别为0.43和0.47。这些结果表明，与线性模型或单树方法相比，非线性集成算法更好地捕捉了环境协变量与土壤属性之间的异质性关系。总体而言，RF和eXGB为脆弱高寒农业生态系统中的精准施肥、土壤质量评估和长期可持续性提供了实用的方法学基础。","Sustainability","2026-09-27T00:00:00Z",73,{"impact":131,"substance":16,"depth":132,"authority":304,"freshness":107,"relevant":21,"comment":411},"基于103个样点412份观测对比九种算法预测高原农田土壤养分与pH，方法扎实、结论明确，对精准施肥有直接参考价值，但属区域性研究，产业影响有限。",[413],{"name":407,"url":404},[26,27,28,415,416],"土壤养分","青藏高原",{"code":180,"slug":357,"name":418,"path":419,"parent_code":420},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[422],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":424},{"candidates":425,"agricultural_evidence":46},[426,430,434,440,446],{"code":180,"score":147,"supported":46,"title":427,"abstract":428,"tags":429},[357],[183,357],[415],{"code":32,"score":134,"supported":46,"title":431,"abstract":432,"tags":433},[254],[],[27],{"code":89,"score":161,"supported":46,"title":435,"abstract":436,"tags":439},[],[437,438],"施肥","fertilization",[28],{"code":441,"score":53,"supported":46,"title":442,"abstract":443,"tags":445},"H6",[],[444],"农业生态",[],{"code":94,"score":21,"supported":13,"title":447,"abstract":448,"tags":449},[],[],[26],[451,452],"青藏高原 农田 土壤养分 机器学习","土壤pH 预测 随机森林 XGBoost","青藏高原农田土壤养分机器学习-3805",2,"10.3390\u002Fsu18199875",{"doi":455,"openalex_id":457,"authors":458,"venue":407,"cited_by_count":110,"oa_url":404,"card":475,"direction":115,"ingested_from":215},"W7214680092",[459,461,463,466,468,471,473],{"name":460,"orcid":8},"Chenjun Zhao",{"name":462,"orcid":8},"Shaowei Li",{"name":464,"orcid":465},"Yuan Tian","https:\u002F\u002Forcid.org\u002F0000-0002-8758-1826",{"name":467,"orcid":8},"ChengQun Yu",{"name":469,"orcid":470},"Gang Fu","https:\u002F\u002Forcid.org\u002F0000-0002-6055-8730",{"name":472,"orcid":8},"Zhiming Zhong",{"name":474,"orcid":8},"Wei Sun",{"tldr":476,"method":477,"finding":478,"direction":115,"opportunity":479},"比较九种机器学习算法预测青藏高原高寒农田土壤养分与pH，发现集成算法最优。","基于103个样点412个观测，用气候与NDVI变量比较RF、eXGB等九种算法。","eXGB和RF表现最佳，平均相对偏差最低（1.35%和1.55%），优于线性与单树模型。","可引入多时相遥感与深度学习方法，提升高寒区土壤属性制图精度与泛化能力。","2026-09-29T23:30:57.794777Z",{"id":482,"title":483,"url":484,"summary":485,"summary_zh":486,"content":8,"source_name":487,"source_url":484,"published_at":488,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":489,"score_detail":490,"sources":492,"tags":494,"primary_topic":497,"secondary_topics":498,"classification":499,"search_phrases":527,"slug":530,"view_count":45,"doi":531,"paper":532,"created_at":554},3621,"Beyond Precision Agriculture: AI and Data-driven Information Systems as Catalysts for Sustainable Agribusiness Transformation","https:\u002F\u002Fdoi.org\u002F10.56557\u002Fjgembr\u002F2026\u002Fv18i311156","The dominant narrative in digital agriculture centres on discrete technologies, including sensors, unmanned aerial vehicles, global positioning systems, remote sensing, and machine learning, evaluated primarily for their capacity to increase yields and improve input-use efficiency. This narrows the analytical lens to the farm gate and understates a more consequential shift: individual technologies generate value for agribusiness only when information systems capture, integrate, and route the data they produce into real operational and strategic decisions. This review critically examines the AI- and data-driven agribusiness transformation literature through a single organising pathway: data, analytics, artificial intelligence, information systems, decisions, sustainable agribusiness outcomes; it asks whether the field's technical achievements have matched comparable progress in systems integration and governance. A transparent narrative search and appraisal protocol synthesised evidence across ten thematic domains: agricultural big-data integration, predictive and prescriptive analytics, farm management information systems (FMIS), AI-enabled supply-chain analytics, climate-risk adaptation, sustainability and ESG analytics, data governance and cybersecurity, digital infrastructure and adoption, circular agriculture, and decision-support systems. The evidence shows that sensing and algorithmic capabilities have matured faster than the organisational and governance capacity required to convert those capabilities into adopted agribusiness decisions. Data-governance barriers, rather than technological ones, now dominate constraints on information-system integration in the literature published since 2023. Research, investment, and policy attention should therefore shift further toward the interoperability, governance, and organisational design layers of digital agriculture, rather than continuing to concentrate primarily on sensor accuracy or model performance.","数字农业的主流叙事聚焦于各类离散技术，包括传感器、无人机、全球定位系统、遥感和机器学习，并主要依据其提高产量和改善投入品利用效率的能力加以评价。这种视角将分析视野收窄至农场大门，低估了一个更具深远意义的转变：只有当信息系统能够捕获、整合单个技术所产生的数据，并将其导入实际运营和战略决策时，这些技术才能为农业企业创造价值。本综述通过一条单一的组织路径——数据、分析、人工智能、信息系统、决策、可持续农业企业成果——批判性地审视人工智能和数据驱动的农业企业转型文献，并追问该领域的技术成就是否与系统集成和治理方面的相应进展相匹配。一项透明的叙述性检索与评价方案综合了十个主题领域的证据：农业大数据集成、预测性与规范性分析、农场管理信息系统(FMIS)、人工智能赋能的供应链分析、气候风险适应、可持续性与ESG分析、数据治理与网络安全、数字基础设施与采用、循环农业以及决策支持系统。证据表明，感知和算法能力的成熟速度快于将这些能力转化为已被采用的农业企业决策所需的组织和治理能力。在2023年以来发表的文献中，数据治理障碍而非技术障碍，已成为信息系统集成的主要制约因素。因此，研究、投资和政策关注应进一步转向数字农业的互操作性、治理和组织设计层面，而非继续主要集中于传感器精度或模型性能。","Journal of Global Economics Management and Business Research","2026-09-26T00:00:00Z",80,{"impact":132,"substance":16,"depth":132,"authority":304,"freshness":134,"relevant":21,"comment":491},"该综述指出数据治理与系统集成而非传感技术本身才是数字农业落地的关键瓶颈，观点有实质增量，但属学术综述、产业影响偏间接。",[493],{"name":487,"url":484},[26,27,29,495,496],"数据治理","农业信息体系",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":500},{"candidates":501,"agricultural_evidence":46},[502,508,512,516,521],{"code":94,"score":147,"supported":46,"title":503,"abstract":504,"tags":507},[158],[505,506],"数字农业","digital agriculture",[26],{"code":32,"score":12,"supported":46,"title":509,"abstract":510,"tags":511},[],[49,254,255],[27],{"code":341,"score":20,"supported":46,"title":513,"abstract":514,"tags":515},[],[344,345],[],{"code":99,"score":517,"supported":46,"title":518,"abstract":519,"tags":520},5,[],[29],[29,495],{"code":522,"score":53,"supported":46,"title":523,"abstract":524,"tags":526},"H8",[],[525],"可持续农业",[],[528,529],"农业信息体系 数据治理","农场管理信息系统 FMIS","农业信息体系数据治理-3621","10.56557\u002Fjgembr\u002F2026\u002Fv18i311156",{"doi":531,"openalex_id":533,"authors":534,"venue":487,"cited_by_count":110,"oa_url":484,"card":547,"direction":553,"ingested_from":215},"W7214471201",[535,537,539,541,543,545],{"name":536,"orcid":8},"Timilehin Fawibe",{"name":538,"orcid":8},"Gbenga Samuel Ernest",{"name":540,"orcid":8},"Joshua Emmanuel",{"name":542,"orcid":8},"Simon Jacob",{"name":544,"orcid":8},"Olawale Akinleye",{"name":546,"orcid":8},"Tosin Olorunwo",{"tldr":548,"method":549,"finding":550,"direction":551,"opportunity":552},"综述指出农业数字化瓶颈已从技术转向数据治理与系统集成，呼吁研究重心转向互操作与组织设计。","采用透明叙事检索与评价协议，综合十大主题领域的AI与数据驱动农业文献。","2023年后数据治理障碍而非技术障碍，成为农业信息系统集成的主要制约。","数字乡村与农业信息化","可研究农业数据互操作标准、治理机制与组织采纳路径如何影响可持续农业商业决策。","农业遥感与作物表型","2026-09-27T23:30:46.092194Z",{"id":556,"title":557,"url":558,"summary":559,"summary_zh":560,"content":8,"source_name":561,"source_url":558,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":562,"score_detail":563,"sources":566,"tags":568,"primary_topic":571,"secondary_topics":576,"classification":582,"search_phrases":607,"slug":610,"view_count":454,"doi":611,"paper":612,"created_at":632},2805,"Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71908-1","Abstract Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicum L., Royal variety). A total of 300 hyperspectral samples were collected from plants subjected to three nitrogen treatments (N-30%, N-60%, and N-90%) under controlled conditions. Reflectance data (400–1000 nm) were calibrated and preprocessed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Savitzky–Golay (SG) filtering. Both unsupervised and supervised algorithms were systematically evaluated. Clustering results demonstrated that preprocessing substantially influenced class separability. The MSC + GMM combination yielded the best unsupervised results, with the lowest Davies–Bouldin index (0.443) and the highest silhouette coefficient (0.975), indicating improved cluster compactness and separation. In supervised learning, Neural Networks (NN) consistently outperformed the other models, achieving 100% accuracy and an ROC–AUC of 1.00 under SNV, MSC, and SG preprocessing. Gradient Boosting (GB) and Support Vector Machine (SVM) also demonstrated strong predictive capability, whereas conventional models, including LDA, LR, KNN, and NB, showed moderate improvements following preprocessing. Statistical analysis confirmed the significant effect of spectral preprocessing on both clustering and classification outcomes ( p \u003C 0.05). Overall, integrating hyperspectral imaging with appropriate preprocessing and nonlinear machine learning models provided strong classification performance under the controlled experimental conditions. However, the exceptionally high performance observed for some models should be interpreted cautiously because of the relatively small dataset and experimentally controlled nitrogen treatments. Moreover, the absence of an independent external validation dataset limits the assessment of generalizability across growing conditions, cultivars, and field environments. Therefore, independent validation using larger and more diverse datasets is required before broader application of the proposed framework in practical precision agriculture.","摘要 准确估算叶片氮含量对于优化施肥管理和提高作物生产力至关重要。本研究提出了一种无损高光谱成像框架，结合先进的机器学习算法对番茄叶片（Solanum lycopersicum L.，Royal品种）的氮水平进行分类。在受控条件下，从接受三种氮处理（N-30%、N-60%和N-90%）的植株中共采集了300个高光谱样本。反射率数据（400–1000 nm）经标准正态变量变换（SNV）、多元散射校正（MSC）和Savitzky–Golay（SG）滤波进行校准和预处理。系统评估了无监督和有监督算法。聚类结果表明，预处理对类别可分性产生了显著影响。MSC + GMM组合取得了最佳无监督结果，具有最低的Davies–Bouldin指数（0.443）和最高的轮廓系数（0.975），表明聚类紧密度和分离度得到改善。在有监督学习中，神经网络（NN）始终优于其他模型，在SNV、MSC和SG预处理下均达到100%的准确率和1.00的ROC–AUC。梯度提升（GB）和支持向量机（SVM）也表现出较强的预测能力，而传统模型（包括LDA、LR、KNN和NB）在预处理后表现出中等程度的改善。统计分析证实了光谱预处理对聚类和分类结果均有显著影响（p \u003C 0.05）。总体而言，将高光谱成像与适当的预处理及非线性机器学习模型相结合，在受控实验条件下提供了较强的分类性能。然而，由于数据集相对较小且氮处理为实验受控条件，某些模型所表现出的极高性能应谨慎解读。此外，缺乏独立的外部验证数据集限制了对跨生长条件、品种和田间环境泛化能力的评估。因此，在将该框架广泛应用于实际精准农业之前，需要使用更大规模、更多样化的数据集进行独立验证。","Scientific Reports",77,{"impact":147,"substance":564,"depth":303,"authority":19,"freshness":12,"relevant":21,"comment":565},21,"高光谱结合机器学习实现番茄叶片氮素无损估测，方法系统、结论明确，但样本量小且缺乏外部验证，属细分领域方法学进展。",[567],{"name":561,"url":558},[26,27,28,569,570],"番茄种植","高光谱遥感",{"code":77,"slug":572,"name":573,"path":574,"parent_code":575},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[577,578],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":341,"slug":579,"name":580,"path":581,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":13,"version":40,"method":41,"evidence":583},{"candidates":584,"agricultural_evidence":46},[585,590,594,599,603],{"code":77,"score":147,"supported":46,"title":586,"abstract":588,"tags":589},[587],"tomato",[80,587],[569],{"code":32,"score":147,"supported":46,"title":591,"abstract":592,"tags":593},[254],[49,254],[27],{"code":341,"score":131,"supported":46,"title":595,"abstract":597,"tags":598},[596],"hyperspectral",[596],[570],{"code":89,"score":161,"supported":46,"title":600,"abstract":601,"tags":602},[],[437,438],[28],{"code":94,"score":161,"supported":46,"title":604,"abstract":605,"tags":606},[],[157,158],[26],[608,609],"农业人工智能 高光谱遥感 智慧农业 番茄种植","农业人工智能 高光谱遥感","农业人工智能高光谱遥感智慧农业番茄种植-2805","10.1038\u002Fs41598-026-71908-1",{"doi":611,"openalex_id":613,"authors":614,"venue":561,"cited_by_count":110,"oa_url":558,"card":627,"direction":115,"ingested_from":215},"W7213472002",[615,618,621,624],{"name":616,"orcid":617},"Mohammad Vahedi Torshizi","https:\u002F\u002Forcid.org\u002F0000-0003-3648-1515",{"name":619,"orcid":620},"Sajad Sabzi","https:\u002F\u002Forcid.org\u002F0000-0003-2439-5329",{"name":622,"orcid":623},"Mohsen Azadbakht","https:\u002F\u002Forcid.org\u002F0000-0002-5726-9321",{"name":625,"orcid":626},"Razieh Pourdarbani","https:\u002F\u002Forcid.org\u002F0000-0003-0766-8305",{"tldr":628,"method":629,"finding":630,"direction":553,"opportunity":631},"用高光谱成像结合预处理与机器学习，对番茄叶片氮素水平进行分类。","300个高光谱样本，SNV、MSC、SG预处理，聚类与多种监督分类算法对比。","MSC+GMM聚类最优，神经网络在三种预处理下均达100%准确率。","样本少且无外部验证，可扩展多品种、多环境田间数据并做独立验证以提升泛化性。","2026-09-17T23:30:59.361085Z"]