[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3510":3,"related-3510":91},{"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":18,"tags":20,"primary_topic":26,"secondary_topics":32,"classification":33,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":90},3510,"Bridging the in situ data gap: a satellite- and model-based framework for operational monitoring of marine harmful algal blooms and temperature-related threats in emerging economies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmars.2026.1934010","Emerging and developing economies often face constrained financial, technical, and institutional capacities to establish and maintain the ocean-based in situ observational networks that underpin marine decision support applications. This limitation heightens their exposure and sensitivity to the cumulative impacts of marine environmental hazards, including harmful algal blooms and marine heatwaves. In this perspective, we present an example of an operational, co-developed ocean monitoring framework that is explicitly structured around user requirements, local scientific expertise, established software engineering practices, and openly available satellite and numerical model data and algorithms. The framework is instantiated through the South African National Oceans and Coastal Information Management System (OCIMS) Fisheries and Aquaculture Decision Support Tool, which is employed as a representative implementation case study and potential reference architecture for application in other regional contexts. We demonstrate that satellite-derived and model-based products can markedly accelerate the establishment of operational marine environmental monitoring capabilities in settings where dense, long-term in situ observing systems are absent or sparse, thereby enhancing societal preparedness and adaptive capacity in relation to intensifying, climate-driven marine risks. Furthermore, we discuss how such frameworks can underpin resilient, equitable, and evidence-based blue economy development in the Global South.","新兴和发展中经济体在建立和维持支撑海洋决策支持应用的海洋原位观测网络方面，往往面临财政、技术和制度能力的制约。这一局限加剧了它们对海洋环境危害累积影响的暴露度和敏感性，这些危害包括有害藻华和海洋热浪。在本视角文章中，我们展示了一个可业务化运行、共同开发的海洋监测框架实例，该框架明确围绕用户需求、地方科学专业知识、成熟的软件工程实践以及公开可用的卫星和数值模型数据与算法来构建。该框架通过南非国家海洋与海岸信息管理系统（OCIMS）渔业与水产养殖决策支持工具得以实例化，并作为代表性实施案例和潜在参考架构，供其他区域情境应用。我们证明，在缺乏密集长期原位观测系统或此类系统稀疏的环境中，卫星衍生和基于模型的产品能够显著加速业务化海洋环境监测能力的建立，从而增强社会对日益加剧的气候驱动海洋风险的防范和适应能力。此外，我们讨论了此类框架如何支撑全球南方具有韧性、公平且基于证据的蓝色经济发展。",null,"Frontiers in Marine Science","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为海洋有害藻华与热浪监测框架，属海洋环境与蓝色经济范畴，与三农、农业信息化、智慧农业无直接关联，相关性门槛未通过。",[19],{"name":10,"url":6},[21,22,23,24,25],"决策支持系统","有害藻华","蓝色经济","海洋遥感","海洋热浪",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"classified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42},[39,49],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":48},"D",9,true,[],[45,46,47],"水产","渔业","aquaculture",[],{"code":27,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},4,[],[53],"decision support",[21],[56,57],"OCIMS 渔业 水产 决策支持","南非 海洋监测 框架","OCIMS渔业水产决策支持-3510",2,"10.3389\u002Ffmars.2026.1934010",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":15,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7213986050",[64,66,69,71,74,76,78,80],{"name":65,"orcid":9},"Marié E. Smith",{"name":67,"orcid":68},"Stewart Bernard","https:\u002F\u002Forcid.org\u002F0000-0001-6537-3682",{"name":70,"orcid":9},"Giles Fearon",{"name":72,"orcid":73},"Marjolaine Krug","https:\u002F\u002Forcid.org\u002F0000-0002-5719-3240",{"name":75,"orcid":9},"Raymond Molapo",{"name":77,"orcid":9},"Jennifer Veitch",{"name":79,"orcid":9},"Lufuno Vhengani",{"name":81,"orcid":9},"L. A. Williams","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1934010\u002Fpdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"提出基于卫星与模型数据的海洋监测框架，弥补新兴经济体现场观测不足，监测有害藻华与热浪。","以南非OCIMS渔业决策工具为案例，结合用户需求、开源卫星与数值模型数据。","卫星与模型产品可快速建立业务化海洋环境监测能力，提升社会适应力。","农业遥感与作物表型","可迁移该框架至农业领域，利用遥感与模型弥补地面观测不足，监测作物病害与热胁迫。","openalex","2026-09-25T23:30:35.360231Z",{"total":92,"page":93,"page_size":92,"items":94},6,1,[95,169,227,285,357,423],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":153,"slug":156,"view_count":15,"doi":157,"paper":158,"created_at":168},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":105,"substance":106,"depth":107,"authority":92,"freshness":41,"relevant":93,"comment":108},8,14,12,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[110],{"name":101,"url":98},[112,113,21,114,115],"智慧农业","农业人工智能","病害识别","灌溉管理",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[],{"status":34,"locked":14,"version":35,"method":36,"evidence":119},{"candidates":120,"agricultural_evidence":42},[121,126,134,141,148],{"code":27,"score":122,"supported":42,"title":123,"abstract":124,"tags":125},11,[53],[],[113,21,114],{"code":127,"score":128,"supported":42,"title":129,"abstract":130,"tags":133},"H3",7,[],[131,132],"灌溉","irrigation",[115],{"code":135,"score":92,"supported":42,"title":136,"abstract":137,"tags":140},"H2",[],[138,139],"土壤","soil",[],{"code":142,"score":143,"supported":42,"title":144,"abstract":145,"tags":147},"F3",3,[],[146],"肥料",[],{"code":149,"score":93,"supported":14,"title":150,"abstract":151,"tags":152},"I1",[],[],[112],[154,155],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":157,"openalex_id":159,"authors":160,"venue":101,"cited_by_count":15,"oa_url":98,"card":161,"direction":167,"ingested_from":89},"W7222785888",[],{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","农业人工智能与决策模型","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","智慧农业 \u002F 农业物联网","2026-10-19T23:30:08.043132Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":176,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":184,"tags":186,"primary_topic":190,"secondary_topics":194,"classification":196,"search_phrases":208,"slug":211,"view_count":93,"doi":212,"paper":213,"created_at":226},4914,"Water Optimizer: A Tool for Decision Support and Policy Analysis","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413426","Water Optimizer is a tool for analyzing alternative water management strategies when the available water supply is limited. It can be used by producers to determine the profit maximizing crops to produce and the optimum amount of water to apply to each crop, given a particular water supply. It can also be used to evaluate the economic consequences of public policies which expand or reduce the amount of irrigation.","水资源优化器是一种在可用供水有限时分析替代水资源管理策略的工具。生产者可用其确定在特定供水量下能实现利润最大化的作物种类及每种作物的最优灌溉水量，也可用于评估扩大或减少灌溉面积的公共政策的经济后果。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43","2026-10-13T00:00:00Z",67,{"impact":107,"substance":180,"depth":181,"authority":182,"freshness":105,"relevant":93,"comment":183},18,16,13,"内布拉斯加大学开发的灌溉决策支持工具，方法实用但属细分领域工具类论文，公共影响有限。",[185],{"name":175,"url":176},[21,187,188,189],"水资源管理","灌溉决策","农业政策分析",{"code":127,"slug":132,"name":191,"path":192,"parent_code":193},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[195],{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},{"status":34,"locked":14,"version":35,"method":36,"evidence":197},{"candidates":198,"agricultural_evidence":42},[199,204],{"code":127,"score":122,"supported":42,"title":200,"abstract":201,"tags":203},[],[131,202,132],"水资源",[187,188],{"code":27,"score":41,"supported":42,"title":205,"abstract":206,"tags":207},[53],[],[21],[209,210],"Water Optimizer 灌溉决策","内布拉斯加大学 水资源管理","WaterOptimizer灌溉决策-4914","10.22004\u002Fag.econ.413426",{"doi":212,"openalex_id":214,"authors":215,"venue":175,"cited_by_count":15,"oa_url":220,"card":221,"direction":165,"ingested_from":89},"W38113320",[216,218],{"name":217,"orcid":9},"Derrel L. Martin",{"name":219,"orcid":9},"Ray Supalla","http:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43",{"tldr":222,"method":223,"finding":224,"direction":165,"opportunity":225},"开发了Water Optimizer工具，在有限供水下优化作物种植与灌溉量以最大化利润。","基于优化模型与决策支持工具，分析不同供水策略及政策的经济影响。","该工具能帮助生产者选择利润最大化作物和最优灌溉量，并评估灌溉政策的经济后果。","可结合实时气象与土壤数据，开发动态灌溉决策模型，并拓展至多目标（经济-环境）优化。","2026-10-14T23:30:55.832240Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":247,"classification":249,"search_phrases":267,"slug":270,"view_count":15,"doi":271,"paper":272,"created_at":284},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z",69,{"impact":107,"substance":237,"depth":181,"authority":182,"freshness":105,"relevant":93,"comment":238},20,"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[240],{"name":233,"url":230},[112,113,21,242,243],"土壤墒情","作物选择",{"code":135,"slug":139,"name":245,"path":246,"parent_code":193},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[248],{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},{"status":34,"locked":14,"version":35,"method":36,"evidence":250},{"candidates":251,"agricultural_evidence":42},[252,257,263],{"code":135,"score":253,"supported":42,"title":254,"abstract":255,"tags":256},15,[139],[138,139],[242],{"code":27,"score":122,"supported":42,"title":258,"abstract":259,"tags":262},[],[260,261,53],"机器学习","machine learning",[113,21],{"code":149,"score":93,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[112],[268,269],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":271,"openalex_id":273,"authors":274,"venue":233,"cited_by_count":15,"oa_url":230,"card":279,"direction":167,"ingested_from":89},"W7220991946",[275,277],{"name":276,"orcid":9},"C. SAI KUMAR",{"name":278,"orcid":9},"Prof. G SREENIVASULU",{"tldr":280,"method":281,"finding":282,"direction":165,"opportunity":283},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","2026-10-09T23:30:12.285129Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":291,"source_url":288,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":296,"tags":298,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":341,"slug":344,"view_count":92,"doi":345,"paper":346,"created_at":356},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":107,"substance":237,"depth":181,"authority":107,"freshness":105,"relevant":93,"comment":295},"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[297],{"name":291,"url":288},[112,113,299,260,21],"产量预测",{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},[],{"status":34,"locked":14,"version":35,"method":36,"evidence":303},{"candidates":304,"agricultural_evidence":42},[305,309,316,322,326,332,336],{"code":27,"score":237,"supported":42,"title":306,"abstract":307,"tags":308},[53],[260,261,53],[113,21,260],{"code":310,"score":92,"supported":42,"title":311,"abstract":312,"tags":315},"A8",[],[313,314],"粮食安全","food security",[],{"code":142,"score":92,"supported":42,"title":317,"abstract":318,"tags":321},[],[319,320],"施肥","fertilizer",[],{"code":135,"score":92,"supported":42,"title":323,"abstract":324,"tags":325},[],[138,139],[],{"code":327,"score":143,"supported":42,"title":328,"abstract":329,"tags":331},"J1",[],[330],"农业规划",[],{"code":149,"score":93,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[112],{"code":337,"score":93,"supported":14,"title":338,"abstract":339,"tags":340},"I3",[],[],[299],[342,343],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":345,"openalex_id":347,"authors":348,"venue":291,"cited_by_count":15,"oa_url":9,"card":351,"direction":165,"ingested_from":89},"W7213883348",[349],{"name":350,"orcid":9},"D.J.S. Sako",{"tldr":352,"method":353,"finding":354,"direction":165,"opportunity":355},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":369,"tags":371,"primary_topic":374,"secondary_topics":380,"classification":382,"search_phrases":407,"slug":410,"view_count":143,"doi":411,"paper":412,"created_at":422},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science","2026-09-16T00:00:00Z",74,{"impact":253,"substance":237,"depth":367,"authority":107,"freshness":13,"relevant":93,"comment":368},17,"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[370],{"name":363,"url":360},[112,113,372,21,373],"设施农业","病虫害预警",{"code":375,"slug":376,"name":377,"path":378,"parent_code":379},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[381],{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},{"status":34,"locked":14,"version":35,"method":36,"evidence":383},{"candidates":384,"agricultural_evidence":42},[385,390,396,403],{"code":27,"score":386,"supported":42,"title":387,"abstract":388,"tags":389},27,[261,53],[260,261,53],[113,21],{"code":375,"score":106,"supported":42,"title":391,"abstract":393,"tags":395},[392],"greenhouse",[394,392],"温室",[],{"code":397,"score":92,"supported":42,"title":398,"abstract":399,"tags":402},"F4",[],[400,401],"农药","pesticide",[],{"code":149,"score":93,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[112],[408,409],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":411,"openalex_id":413,"authors":414,"venue":363,"cited_by_count":15,"oa_url":360,"card":417,"direction":165,"ingested_from":89},"W7213235680",[415],{"name":416,"orcid":9},"S. Nasiketha",{"tldr":418,"method":419,"finding":420,"direction":165,"opportunity":421},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":441,"secondary_topics":442,"classification":444,"search_phrases":471,"slug":473,"view_count":93,"doi":474,"paper":475,"created_at":487},2052,"Sustainable soil governance pathways toward zero food gap","https:\u002F\u002Fdoi.org\u002F10.36922\u002Feer026320034","Achieving zero food gap has become a strategic global priority in response to increasing pressures from population growth, land degradation, climate change, and resource scarcity. Despite substantial advances in agricultural technologies, persistent disparities between food demand and production highlight the need for integrated approaches that strengthen the natural resource base while improving decision-making and policy implementation. Among these resources, healthy soils constitute the foundation of sustainable food systems, making sustainable soil governance the essential first step toward resilient food production. This review synthesizes recent advances in soil science, Earth observation, artificial intelligence, and decision support systems to develop an integrated conceptual framework—Sustainable Soil Governance Pathways Toward Zero Food Gap. Following the PRISMA 2020 framework, peer-reviewed literature published between 2015 and 2026 was systematically analyzed and organized into four interconnected domains: sustainable soil management, digital soil intelligence, sustainable soil governance, and enabling societal drivers. Rather than treating these domains independently, the review emphasizes their interactions in transforming scientific evidence into coordinated management and policy actions. The proposed framework identifies sustainable soil governance as the central coordinating mechanism that links healthy soils to digital innovation, evidence-based decision-making, and broader environmental, institutional, and socioeconomic conditions influencing food systems. This perspective recognizes that while soil governance is not the sole determinant of food security, it provides the foundation for integrating complementary interventions to advance the long-term vision of zero food gap. The framework offers a practical roadmap for supporting resilient, productive, and sustainable food systems.","实现零食物缺口已成为全球战略优先事项，以应对人口增长、土地退化、气候变化和资源稀缺带来的日益加剧的压力。尽管农业技术取得了实质性进展，但食物需求与生产之间持续存在的差距凸显出需要采取综合方法，在加强自然资源基础的同时改进决策和政策实施。在这些资源中，健康的土壤构成了可持续食物系统的基础，使可持续土壤治理成为迈向韧性食物生产的关键第一步。本综述综合了土壤科学、地球观测、人工智能和决策支持系统的最新进展，构建了一个整合性概念框架——面向零食物缺口的可持续土壤治理路径。遵循PRISMA 2020框架，对2015年至2026年间发表的同行评审文献进行了系统分析，并将其组织为四个相互关联的领域：可持续土壤管理、数字土壤智能、可持续土壤治理和赋能性社会驱动因素。本综述并未将这些领域独立对待，而是强调它们在将科学证据转化为协调管理和政策行动中的相互作用。所提出的框架将可持续土壤治理确定为核心协调机制，将健康土壤与数字创新、循证决策以及影响食物系统的更广泛环境、制度和社会经济条件联系起来。这一视角认识到，尽管土壤治理并非粮食安全的唯一决定因素，但它为整合互补性干预措施奠定了基础，以推进零食物缺口的长期愿景。该框架为支持韧性、高生产力和可持续的食物系统提供了实践路线图。","Explora Environment and Resource","2026-09-07T00:00:00Z",79,{"impact":433,"substance":237,"depth":180,"authority":182,"freshness":92,"relevant":93,"comment":434},22,"系统性综述提出土壤治理与数字技术融合框架，对农业信息化与粮食安全议题有参考价值，但属学术综述、时效一般，适合主题聚合而非头条精选。",[436],{"name":429,"url":426},[113,438,313,21,439,440],"农业遥感","土壤治理","数字土壤",{"code":135,"slug":139,"name":245,"path":246,"parent_code":193},[443],{"code":27,"slug":28,"name":29,"path":30,"parent_code":31},{"status":34,"locked":14,"version":35,"method":36,"evidence":445},{"candidates":446,"agricultural_evidence":42},[447,451,456,460,467],{"code":135,"score":106,"supported":42,"title":448,"abstract":449,"tags":450},[139],[138,139],[],{"code":27,"score":105,"supported":42,"title":452,"abstract":453,"tags":455},[],[454,53],"artificial intelligence",[113,21],{"code":310,"score":128,"supported":42,"title":457,"abstract":458,"tags":459},[],[313,314],[313],{"code":461,"score":92,"supported":42,"title":462,"abstract":463,"tags":466},"H7",[],[464,465],"气候变化","climate change",[],{"code":337,"score":93,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[438],[472,409],"农业人工智能 决策支持系统 农业遥感 土壤治理","农业人工智能决策支持系统农业遥感土壤治理-2052","10.36922\u002Feer026320034",{"doi":474,"openalex_id":476,"authors":477,"venue":429,"cited_by_count":15,"oa_url":481,"card":482,"direction":167,"ingested_from":89},"W7211977960",[478],{"name":479,"orcid":480},"R.R. Alí","https:\u002F\u002Forcid.org\u002F0000-0002-2483-2907","https:\u002F\u002Farticle.accscience.com\u002Fjournal\u002FEER\u002Fonline_first\u002Feer026320034\u002Farticle_eer0263200341_17c096.pdf",{"tldr":483,"method":484,"finding":485,"direction":87,"opportunity":486},"综述土壤科学、地球观测、AI与决策支持，提出可持续土壤治理实现零食物缺口的整合框架。","PRISMA 2020系统综述2015-2026年文献，构建四域整合概念框架。","可持续土壤治理是连接健康土壤、数字创新与循证决策的核心协调机制。","可实证检验数字土壤智能与治理政策的耦合机制，开发面向零食物缺口的决策支持系统。","2026-09-10T23:30:17.837941Z"]