[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5559":3,"related-5559":124},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":96,"slug":99,"view_count":100,"doi":101,"paper":102,"created_at":123},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%。",null,"International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},12,18,16,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施肥","智能灌溉","病害诊断",{"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":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,60,68,75,83,90],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":52},15,true,[48],"decision support",[50,51],"计算机视觉","computer vision",[27],{"code":54,"score":13,"supported":46,"title":55,"abstract":56,"tags":59},"I1",[],[26,57,58],"精准农业","precision agriculture",[26],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":67},"F3",7,[],[65,66],"肥料","fertilizer",[28],{"code":69,"score":62,"supported":46,"title":70,"abstract":71,"tags":74},"H3",[],[72,73],"灌溉","irrigation",[29],{"code":76,"score":77,"supported":46,"title":78,"abstract":79,"tags":82},"I5",6,[],[80,81],"物联网","IoT",[],{"code":84,"score":85,"supported":46,"title":86,"abstract":87,"tags":89},"H2",3,[],[88],"土壤",[],{"code":91,"score":85,"supported":46,"title":92,"abstract":93,"tags":95},"H7",[],[94],"农业气象",[],[97,98],"AgriSens 智慧农业","Gemini 叶片病害诊断","AgriSens智慧农业-5559",0,"10.22214\u002Fijraset.2026.85162",{"doi":101,"openalex_id":103,"authors":104,"venue":10,"cited_by_count":100,"oa_url":6,"card":115,"direction":121,"ingested_from":122},"W7223158459",[105,107,109,111,113],{"name":106,"orcid":9},"Harshini G. K.",{"name":108,"orcid":9},"Monisha B. P.",{"name":110,"orcid":9},"Manoj G.",{"name":112,"orcid":9},"Chandana K. M.",{"name":114,"orcid":9},"Prof. Sushma S.",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"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。","农业人工智能与决策模型","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",{"total":77,"page":21,"page_size":77,"items":125},[126,202,278,359,433,518],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":137,"tags":139,"primary_topic":142,"secondary_topics":147,"classification":153,"search_phrases":181,"slug":184,"view_count":100,"doi":9,"paper":185,"created_at":201},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":17,"substance":19,"depth":45,"authority":17,"freshness":77,"relevant":21,"comment":136},"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。",[138],{"name":132,"url":129},[26,27,28,140,141],"农业物联网","智能农机",{"code":61,"slug":143,"name":144,"path":145,"parent_code":146},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[148,149],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":76,"slug":150,"name":151,"path":152,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":154},{"candidates":155,"agricultural_evidence":46},[156,160,167,172,176],{"code":61,"score":45,"supported":46,"title":157,"abstract":158,"tags":159},[66],[65,66],[28],{"code":32,"score":13,"supported":46,"title":161,"abstract":162,"tags":166},[],[163,164,165],"机器学习","machine learning","artificial intelligence",[27],{"code":76,"score":13,"supported":46,"title":168,"abstract":169,"tags":171},[],[80,170,81],"internet of things",[140],{"code":54,"score":62,"supported":46,"title":173,"abstract":174,"tags":175},[],[57,58],[26],{"code":177,"score":21,"supported":14,"title":178,"abstract":179,"tags":180},"F5",[],[],[141],[182,183],"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":9,"openalex_id":186,"authors":187,"venue":132,"cited_by_count":100,"oa_url":129,"card":196,"direction":121,"ingested_from":122},"W7221394563",[188,190,192,194],{"name":189,"orcid":9},"Anjali Manoj Kumbhar",{"name":191,"orcid":9},"Shruti Prashant Kulkarni",{"name":193,"orcid":9},"Snehal Santosh Khune",{"name":195,"orcid":9},"B. B. Godbole",{"tldr":197,"method":198,"finding":199,"direction":121,"opportunity":200},"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":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":208,"source_url":205,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":215,"tags":217,"primary_topic":219,"secondary_topics":223,"classification":225,"search_phrases":255,"slug":258,"view_count":77,"doi":259,"paper":260,"created_at":277},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。","Scientific Reports",77,{"impact":19,"substance":211,"depth":18,"authority":212,"freshness":213,"relevant":21,"comment":214},21,14,8,"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[216],{"name":208,"url":205},[26,27,140,29,218],"节水农业",{"code":69,"slug":73,"name":220,"path":221,"parent_code":222},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[224],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":226},{"candidates":227,"agricultural_evidence":46},[228,235,241,245,250],{"code":69,"score":229,"supported":46,"title":230,"abstract":231,"tags":234},19,[73],[72,232,73,233],"水资源","evapotranspiration",[29,218],{"code":32,"score":45,"supported":46,"title":236,"abstract":238,"tags":240},[237],"deep learning",[239,237],"深度学习",[27],{"code":76,"score":62,"supported":46,"title":242,"abstract":243,"tags":244},[],[80,170],[140],{"code":84,"score":77,"supported":46,"title":246,"abstract":247,"tags":249},[],[88,248],"soil",[],{"code":54,"score":251,"supported":46,"title":252,"abstract":253,"tags":254},4,[],[26],[26],[256,257],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":259,"openalex_id":261,"authors":262,"venue":208,"cited_by_count":100,"oa_url":271,"card":272,"direction":121,"ingested_from":122},"W7221040139",[263,265,267,269],{"name":264,"orcid":9},"Ze Luo",{"name":266,"orcid":9},"Zhuo Yang",{"name":268,"orcid":9},"Yi Hu",{"name":270,"orcid":9},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":273,"method":274,"finding":275,"direction":121,"opportunity":276},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":291,"tags":293,"primary_topic":296,"secondary_topics":299,"classification":301,"search_phrases":328,"slug":331,"view_count":332,"doi":333,"paper":334,"created_at":358},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":17,"substance":288,"depth":18,"authority":289,"freshness":213,"relevant":21,"comment":290},22,13,"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。",[292],{"name":284,"url":281},[26,27,28,294,295],"土壤养分","青藏高原",{"code":84,"slug":248,"name":297,"path":298,"parent_code":222},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[300],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":302},{"candidates":303,"agricultural_evidence":46},[304,308,312,318,324],{"code":84,"score":45,"supported":46,"title":305,"abstract":306,"tags":307},[248],[88,248],[294],{"code":32,"score":20,"supported":46,"title":309,"abstract":310,"tags":311},[164],[],[27],{"code":61,"score":62,"supported":46,"title":313,"abstract":314,"tags":317},[],[315,316],"施肥","fertilization",[28],{"code":319,"score":85,"supported":46,"title":320,"abstract":321,"tags":323},"H6",[],[322],"农业生态",[],{"code":54,"score":21,"supported":14,"title":325,"abstract":326,"tags":327},[],[],[26],[329,330],"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":333,"openalex_id":335,"authors":336,"venue":284,"cited_by_count":100,"oa_url":281,"card":353,"direction":119,"ingested_from":122},"W7214680092",[337,339,341,344,346,349,351],{"name":338,"orcid":9},"Chenjun Zhao",{"name":340,"orcid":9},"Shaowei Li",{"name":342,"orcid":343},"Yuan Tian","https:\u002F\u002Forcid.org\u002F0000-0002-8758-1826",{"name":345,"orcid":9},"ChengQun Yu",{"name":347,"orcid":348},"Gang Fu","https:\u002F\u002Forcid.org\u002F0000-0002-6055-8730",{"name":350,"orcid":9},"Zhiming Zhong",{"name":352,"orcid":9},"Wei Sun",{"tldr":354,"method":355,"finding":356,"direction":119,"opportunity":357},"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":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":367,"sources":370,"tags":372,"primary_topic":374,"secondary_topics":375,"classification":382,"search_phrases":407,"slug":410,"view_count":213,"doi":411,"paper":412,"created_at":432},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",{"impact":18,"substance":368,"depth":18,"authority":289,"freshness":213,"relevant":21,"comment":369},20,"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。",[371],{"name":365,"url":362},[26,27,29,218,373],"成本效益分析",{"code":69,"slug":73,"name":220,"path":221,"parent_code":222},[376,381],{"code":377,"slug":378,"name":379,"path":380,"parent_code":222},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":383},{"candidates":384,"agricultural_evidence":46},[385,389,395,399,403],{"code":69,"score":229,"supported":46,"title":386,"abstract":387,"tags":388},[73],[72,232,73],[29,218],{"code":377,"score":212,"supported":46,"title":390,"abstract":392,"tags":394},[391],"sustainable agriculture",[393,391],"可持续农业",[],{"code":32,"score":13,"supported":46,"title":396,"abstract":397,"tags":398},[],[163,164,165],[27],{"code":84,"score":77,"supported":46,"title":400,"abstract":401,"tags":402},[],[88,248],[],{"code":54,"score":21,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[26],[408,409],"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":411,"openalex_id":413,"authors":414,"venue":365,"cited_by_count":100,"oa_url":362,"card":427,"direction":121,"ingested_from":122},"W7214115789",[415,417,419,421,423,425],{"name":416,"orcid":9},"Venkata Suman Jami",{"name":418,"orcid":9},"Purushotham Prasad Kalisetti",{"name":420,"orcid":9},"Sampath Dakshina Murthy A.",{"name":422,"orcid":9},"Gurunadha R.",{"name":424,"orcid":9},"Hema Mamidipaka",{"name":426,"orcid":9},"Gurrapu Omprakash",{"tldr":428,"method":429,"finding":430,"direction":121,"opportunity":431},"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",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":9,"content":9,"source_name":438,"source_url":9,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":444,"tags":446,"primary_topic":449,"secondary_topics":450,"classification":451,"search_phrases":505,"slug":508,"view_count":213,"doi":9,"paper":509,"created_at":517},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%。","《Digital Intelligence in Agriculture》2026; 2(2):54-67","2026-09-17T00:00:00Z",85,{"impact":288,"substance":442,"depth":229,"authority":212,"freshness":77,"relevant":21,"comment":443},24,"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。",[445],{"name":438,"url":436},[26,27,28,447,448],"农业大数据","作物生长预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":452},{"candidates":453,"agricultural_evidence":46},[454,458,464,470,476,482,488,492,496,500],{"code":32,"score":251,"supported":46,"title":455,"abstract":456,"tags":457},[],[163],[27],{"code":459,"score":85,"supported":46,"title":460,"abstract":461,"tags":463},"A1",[],[462],"水稻",[],{"code":465,"score":85,"supported":46,"title":466,"abstract":467,"tags":469},"A2",[],[468],"小麦",[],{"code":471,"score":85,"supported":46,"title":472,"abstract":473,"tags":475},"A3",[],[474],"玉米",[],{"code":477,"score":85,"supported":46,"title":478,"abstract":479,"tags":481},"A7",[],[480],"轮作",[],{"code":483,"score":85,"supported":46,"title":484,"abstract":485,"tags":487},"B1",[],[486],"番茄",[],{"code":69,"score":85,"supported":46,"title":489,"abstract":490,"tags":491},[],[72],[],{"code":61,"score":21,"supported":14,"title":493,"abstract":494,"tags":495},[],[],[28],{"code":54,"score":21,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[26],{"code":501,"score":21,"supported":14,"title":502,"abstract":503,"tags":504},"I6",[],[],[447],[506,507],"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",{"doi":9,"openalex_id":9,"authors":510,"venue":9,"cited_by_count":100,"oa_url":9,"card":511,"direction":119,"ingested_from":516},[],{"tldr":512,"method":513,"finding":514,"direction":119,"opportunity":515},"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%。","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",{"id":519,"title":520,"url":521,"summary":522,"summary_zh":523,"content":9,"source_name":524,"source_url":521,"published_at":525,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":526,"score_detail":527,"sources":530,"tags":532,"primary_topic":533,"secondary_topics":534,"classification":537,"search_phrases":569,"slug":572,"view_count":332,"doi":573,"paper":574,"created_at":591},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":17,"substance":211,"depth":528,"authority":289,"freshness":20,"relevant":21,"comment":529},17,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[531],{"name":524,"url":521},[26,27,163,80,29],{"code":69,"slug":73,"name":220,"path":221,"parent_code":222},[535,536],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":76,"slug":150,"name":151,"path":152,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":538},{"candidates":539,"agricultural_evidence":46},[540,544,548,552,556,561,565],{"code":69,"score":18,"supported":46,"title":541,"abstract":542,"tags":543},[73],[72,232,73],[29],{"code":32,"score":289,"supported":46,"title":545,"abstract":546,"tags":547},[165],[163],[27,163],{"code":76,"score":20,"supported":46,"title":549,"abstract":550,"tags":551},[81],[],[80],{"code":84,"score":77,"supported":46,"title":553,"abstract":554,"tags":555},[],[88,248],[],{"code":91,"score":85,"supported":46,"title":557,"abstract":558,"tags":560},[],[559],"干旱",[],{"code":377,"score":85,"supported":46,"title":562,"abstract":563,"tags":564},[],[393],[],{"code":54,"score":21,"supported":14,"title":566,"abstract":567,"tags":568},[],[],[26],[570,571],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":573,"openalex_id":575,"authors":576,"venue":524,"cited_by_count":100,"oa_url":521,"card":586,"direction":121,"ingested_from":122},"W7213546497",[577,580,583],{"name":578,"orcid":579},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":581,"orcid":582},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":584,"orcid":585},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":587,"method":588,"finding":589,"direction":121,"opportunity":590},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z"]