[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1329":3,"related-1329":82},{"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":29,"secondary_topics":35,"classification":36,"search_phrases":59,"slug":62,"view_count":21,"doi":63,"paper":64,"created_at":81},1329,"Sistem Pendukung Keputusan Distribusi Bantuan Pertanian dengan Metode Simple Additive Weighting (SAW) Berbasis Web","https:\u002F\u002Fdoi.org\u002F10.55606\u002Fjubpi.v4i3.4520","This study aims to design and implement a web-based Decision Support System (DSS) for the distribution of agricultural aid using the Simple Additive Weighting (SAW) method. The background of this research is the frequent occurrence of inaccurate targeting and lack of transparency in the selection process of aid recipients, which is still conducted manually and tends to be subjective. As a result, deserving farmers often do not receive assistance, while less eligible ones may benefit from it. The research adopts a descriptive qualitative approach combined with software engineering using the waterfall development model. Data were collected through observation, interviews, documentation, and literature studies to understand the existing system and define system requirements. The developed system processes multiple criteria, including land ownership status, business scale, income level, group membership, and participation level in farmer groups. The SAW method is applied to normalize data, assign weights to each criterion, and generate a ranking of eligible recipients. The results show that the system improves efficiency by reducing processing time, enhances objectivity by minimizing human bias, and increases transparency by providing traceable decision-making processes. The implication of this research is that the developed system can be used as a practical tool for government agencies to support fair, efficient, and accountable distribution of agricultural aid.","本研究旨在设计并实现一个基于网络的决策支持系统（DSS），用于农业援助分配，采用简单加性加权法（SAW）。研究背景是援助对象选择过程中频繁出现目标不准确和缺乏透明度的问题，该过程目前仍以人工方式进行，且往往带有主观性。因此，应得援助的农民常常无法获得帮助，而资格较差的个体却可能从中受益。本研究采用描述性定性方法，结合瀑布开发模型的软件工程实践。通过观察、访谈、文献记录及文献研究收集数据，以了解现有系统并确定系统需求。所开发的系统处理多项标准，包括土地所有权状况、经营规模、收入水平、团体成员资格以及农民团体参与程度。应用SAW方法对数据进行归一化处理，为每项标准分配权重，并生成合格受助者的排名。结果表明，该系统通过缩短处理时间提高了效率，通过减少人为偏见增强了客观性，并通过提供可追溯的决策过程增加了透明度。本研究的启示在于，所开发的系统可作为政府机构支持公平、高效且负责任的农业援助分配的实际工具。",null,"Jurnal Bintang Pendidikan Indonesia","2026-08-31T00:00:00Z","论文",10,false,40,{"impact":17,"substance":18,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,12,6,2,1,"面向农业援助分配的Web决策支持系统，采用SAW方法提升精准性与透明度，但影响范围有限，时效性较低。",[24],{"name":10,"url":6},[26,27,28],"智慧农业","决策支持系统","农业补贴",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},4,true,[],[47],"decision support",[27],{"code":50,"score":21,"supported":14,"title":51,"abstract":52,"tags":53},"G3",[],[],[28],{"code":55,"score":21,"supported":14,"title":56,"abstract":57,"tags":58},"I1",[],[],[26],[60,61],"决策支持系统 农业补贴 智慧农业","决策支持系统 农业补贴","决策支持系统农业补贴智慧农业-1329","10.55606\u002Fjubpi.v4i3.4520",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":73,"oa_url":6,"card":74,"direction":78,"ingested_from":80},"W7204770221",[67,69,71],{"name":68,"orcid":9},"Konradus Wolo Sukun",{"name":70,"orcid":9},"Bernadete Deta",{"name":72,"orcid":9},"Alfian Nara Weking",0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"开发基于Web的农业援助分配决策支持系统，采用SAW方法提高精准性和透明度。","使用SAW方法，结合瀑布模型开发Web系统，多准则加权排序。","系统减少处理时间，提高客观性，增强决策透明度。","农业人工智能与决策模型","可扩展至其他农业资源分配，或引入模糊逻辑处理不确定信息，提升决策鲁棒性。","openalex","2026-09-01T23:30:41.287312Z",{"total":19,"page":21,"page_size":19,"items":83},[84,154,215,287,367,432],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":139,"slug":142,"view_count":21,"doi":143,"paper":144,"created_at":153},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":17,"substance":94,"depth":18,"authority":19,"freshness":95,"relevant":21,"comment":96},14,9,"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。",[98],{"name":90,"url":87},[26,100,27,101,102],"农业人工智能","病害识别","灌溉管理",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":106},{"candidates":107,"agricultural_evidence":44},[108,113,121,128,135],{"code":30,"score":109,"supported":44,"title":110,"abstract":111,"tags":112},11,[47],[],[100,27,101],{"code":114,"score":115,"supported":44,"title":116,"abstract":117,"tags":120},"H3",7,[],[118,119],"灌溉","irrigation",[102],{"code":122,"score":19,"supported":44,"title":123,"abstract":124,"tags":127},"H2",[],[125,126],"土壤","soil",[],{"code":129,"score":130,"supported":44,"title":131,"abstract":132,"tags":134},"F3",3,[],[133],"肥料",[],{"code":55,"score":21,"supported":14,"title":136,"abstract":137,"tags":138},[],[],[26],[140,141],"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":143,"openalex_id":145,"authors":146,"venue":90,"cited_by_count":73,"oa_url":87,"card":147,"direction":152,"ingested_from":80},"W7222785888",[],{"tldr":148,"method":149,"finding":150,"direction":78,"opportunity":151},"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。","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":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":168,"tags":170,"primary_topic":173,"secondary_topics":177,"classification":179,"search_phrases":197,"slug":200,"view_count":73,"doi":201,"paper":202,"created_at":214},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":18,"substance":164,"depth":165,"authority":166,"freshness":17,"relevant":21,"comment":167},20,16,13,"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。",[169],{"name":160,"url":157},[26,100,27,171,172],"土壤墒情","作物选择",{"code":122,"slug":126,"name":174,"path":175,"parent_code":176},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[178],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":180},{"candidates":181,"agricultural_evidence":44},[182,187,193],{"code":122,"score":183,"supported":44,"title":184,"abstract":185,"tags":186},15,[126],[125,126],[171],{"code":30,"score":109,"supported":44,"title":188,"abstract":189,"tags":192},[],[190,191,47],"机器学习","machine learning",[100,27],{"code":55,"score":21,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[26],[198,199],"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":201,"openalex_id":203,"authors":204,"venue":160,"cited_by_count":73,"oa_url":157,"card":209,"direction":152,"ingested_from":80},"W7220991946",[205,207],{"name":206,"orcid":9},"C. SAI KUMAR",{"name":208,"orcid":9},"Prof. G SREENIVASULU",{"tldr":210,"method":211,"finding":212,"direction":78,"opportunity":213},"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":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":226,"tags":228,"primary_topic":230,"secondary_topics":231,"classification":232,"search_phrases":271,"slug":274,"view_count":19,"doi":275,"paper":276,"created_at":286},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":18,"substance":164,"depth":165,"authority":18,"freshness":17,"relevant":21,"comment":225},"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。",[227],{"name":221,"url":218},[26,100,229,190,27],"产量预测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":233},{"candidates":234,"agricultural_evidence":44},[235,239,246,252,256,262,266],{"code":30,"score":164,"supported":44,"title":236,"abstract":237,"tags":238},[47],[190,191,47],[100,27,190],{"code":240,"score":19,"supported":44,"title":241,"abstract":242,"tags":245},"A8",[],[243,244],"粮食安全","food security",[],{"code":129,"score":19,"supported":44,"title":247,"abstract":248,"tags":251},[],[249,250],"施肥","fertilizer",[],{"code":122,"score":19,"supported":44,"title":253,"abstract":254,"tags":255},[],[125,126],[],{"code":257,"score":130,"supported":44,"title":258,"abstract":259,"tags":261},"J1",[],[260],"农业规划",[],{"code":55,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[26],{"code":267,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},"I3",[],[],[229],[272,273],"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":275,"openalex_id":277,"authors":278,"venue":221,"cited_by_count":73,"oa_url":9,"card":281,"direction":78,"ingested_from":80},"W7213883348",[279],{"name":280,"orcid":9},"D.J.S. Sako",{"tldr":282,"method":283,"finding":284,"direction":78,"opportunity":285},"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":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":300,"tags":302,"primary_topic":305,"secondary_topics":306,"classification":313,"search_phrases":346,"slug":349,"view_count":350,"doi":351,"paper":352,"created_at":366},2746,"Fostering artificial intelligence in the livestock sector: Adoption factors and policy insights from the case of Spain","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104956","CONTEXT AND PURPOSE The integration of Artificial Intelligence (AI) and Precision Livestock Farming (PLF) offers significant opportunities to improve the productivity, animal welfare and sustainability of the livestock sector. However, the adoption of these technologies remains highly uneven. Moving beyond descriptive statistics, this study investigates the complex, interacting factors associated with AI adoption in the Spanish livestock sector. METHODOLOGY Utilising a survey-based sample of 666 Spanish livestock holdings, drawn from a national survey whose sampling was designed to be proportional to the 2020 Agricultural Census by subsector and region, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was employed to evaluate a comprehensive adoption framework across different farming systems and operational scales. AI adoption was modelled as a formative composite of five self-reported AI system categories, and the framework was tested through mediation, moderation, subgroup and out-of-sample predictive analyses with a 10,000-resample bootstrap. MAIN FINDINGS The empirical results indicate that AI adoption is most strongly associated with a holding's baseline technological infrastructure (β = 0.6464, p \u003C 0.001); advanced predictive tools appear to presuppose a pre-existing ecosystem of digital farm books, automated feeding systems and interoperable sensors. A ‘perception-capability gap’ is formally supported: perceived benefits are associated with adoption only indirectly, through the infrastructure baseline (indirect-only mediation), and their association strengthens as infrastructure increases (positive interaction). Recognition of AI's theoretical benefits or awareness of public subsidies is thus not independently associated with adoption in the absence of foundational digital capability. Furthermore, the study identifies a stark structural divide: adoption reaches 56.3% in intensive and semi-intensive holdings but only 24.4% in extensive ones (χ2 = 57.61, p \u003C 0.001), as traditional extensive systems face severe physical and connectivity barriers that leave them systematically disadvantaged. Results are robust across reflective, count, reduced-indicator and logistic specifications, and the model shows substantial out-of-sample predictive power (Q 2 predict ≈ 0.53). CONCLUSIONS To facilitate a more inclusive digital transformation, the findings suggest that policymakers should adopt a ‘readiness-conditional’ model, ensuring farms meet baseline digital prerequisites and have access to robust extension services before subsidising advanced AI tools.","背景与目的 人工智能（Artificial Intelligence, AI）与精准畜牧业（Precision Livestock Farming, PLF）的融合为提升畜牧部门的生产力、动物福利和可持续性提供了重要机遇。然而，这些技术的采用仍然极不均衡。本研究超越描述性统计，探讨了西班牙畜牧部门中与AI采用相关的复杂交互因素。方法 基于一项全国性调查中抽取的666个西班牙畜牧养殖场的样本（该调查的抽样设计按2020年农业普查的子部门和地区比例进行），采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modelling, PLS-SEM）方法，评估了一个跨不同养殖系统和经营规模的综合采用框架。AI采用被建模为五个自我报告的AI系统类别的形成性复合变量，并通过中介、调节、子组和样本外预测分析（10,000次重抽样自助法）对框架进行了检验。主要发现 实证结果表明，AI采用与养殖场的基线技术基础设施关联最为密切（β = 0.6464，p \u003C 0.001）；先进的预测工具似乎以已有的数字牧场记录、自动化饲喂系统和可互操作传感器生态系统为前提。“感知—能力差距”得到正式支持：感知收益仅通过基础设施基线间接与采用相关（仅间接中介），且其关联随基础设施的增加而增强（正向交互）。因此，在缺乏基础数字能力的情况下，对AI理论效益的认可或对公共补贴的知晓与采用并无独立关联。此外，研究识别出一条鲜明的结构性分界线：集约化和半集约化养殖场的采用率达到56.3%，而粗放型养殖场仅为24.4%（χ2 = 57.61，p \u003C 0.001），因为传统粗放型系统面临严重的物理和连接障碍，使其处于系统性劣势。结果在反映性、计数、简化指标和逻辑斯谛设定下均稳健，且模型显示出显著的样本外预测能力（Q 2 predict ≈ 0.53）。结论 为促进更具包容性的数字化转型，研究结果表明政策制定者应采取“就绪条件”模型，确保养殖场","Agricultural Systems","2026-09-16T00:00:00Z",81,{"impact":297,"substance":298,"depth":297,"authority":94,"freshness":95,"relevant":21,"comment":299},18,22,"基于666个西班牙牧场样本的实证研究，揭示AI采纳受数字基础设施制约的“感知—能力鸿沟”，并提出“就绪条件式”补贴政策思路，对智慧畜牧推广与数字乡村政策设计有参考价值。",[301],{"name":293,"url":290},[26,100,303,28,304],"数字素养","精准畜牧",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[307],{"code":308,"slug":309,"name":310,"path":311,"parent_code":312},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","C",{"status":37,"locked":14,"version":38,"method":39,"evidence":314},{"candidates":315,"agricultural_evidence":44},[316,322,327,333,337,341],{"code":312,"score":94,"supported":44,"title":317,"abstract":319,"tags":321},[318],"livestock",[320,318],"畜牧",[],{"code":30,"score":18,"supported":44,"title":323,"abstract":325,"tags":326},[324],"artificial intelligence",[324],[100],{"code":308,"score":13,"supported":44,"title":328,"abstract":329,"tags":332},[],[304,330,331],"动物福利","animal welfare",[304],{"code":50,"score":21,"supported":14,"title":334,"abstract":335,"tags":336},[],[],[28],{"code":55,"score":21,"supported":14,"title":338,"abstract":339,"tags":340},[],[],[26],{"code":342,"score":21,"supported":14,"title":343,"abstract":344,"tags":345},"I6",[],[],[303],[347,348],"农业人工智能 农业补贴 数字素养 智慧农业","农业人工智能 农业补贴","农业人工智能农业补贴数字素养智慧农业-2746",5,"10.1016\u002Fj.agsy.2026.104956",{"doi":351,"openalex_id":353,"authors":354,"venue":293,"cited_by_count":73,"oa_url":290,"card":360,"direction":364,"ingested_from":80},"W7213425399",[355,357],{"name":356,"orcid":9},"Carlos Parra-Lopez",{"name":358,"orcid":359},"Carmen Carmona‐Torres","https:\u002F\u002Forcid.org\u002F0000-0002-6982-1363",{"tldr":361,"method":362,"finding":363,"direction":364,"opportunity":365},"基于西班牙666家养殖场调查，用PLS-SEM分析AI采纳的影响因素与政策启示。","666份养殖场问卷，PLS-SEM建模，含中介、调节与分组分析。","AI采纳最依赖既有数字基础设施；感知收益仅间接起作用，集约场采纳率远高于粗放场。","数字乡村与农业信息化","可研究粗放养殖场数字基础设施与连接性短板，设计‘就绪度条件’式推广与补贴机制。","2026-09-17T23:30:04.844104Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":373,"source_url":370,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":374,"score_detail":375,"sources":378,"tags":380,"primary_topic":383,"secondary_topics":389,"classification":391,"search_phrases":416,"slug":419,"view_count":130,"doi":420,"paper":421,"created_at":431},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",74,{"impact":183,"substance":164,"depth":376,"authority":18,"freshness":13,"relevant":21,"comment":377},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。",[379],{"name":373,"url":370},[26,100,381,27,382],"设施农业","病虫害预警",{"code":384,"slug":385,"name":386,"path":387,"parent_code":388},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[390],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":392},{"candidates":393,"agricultural_evidence":44},[394,399,405,412],{"code":30,"score":395,"supported":44,"title":396,"abstract":397,"tags":398},27,[191,47],[190,191,47],[100,27],{"code":384,"score":94,"supported":44,"title":400,"abstract":402,"tags":404},[401],"greenhouse",[403,401],"温室",[],{"code":406,"score":19,"supported":44,"title":407,"abstract":408,"tags":411},"F4",[],[409,410],"农药","pesticide",[],{"code":55,"score":21,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[26],[417,418],"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":420,"openalex_id":422,"authors":423,"venue":373,"cited_by_count":73,"oa_url":370,"card":426,"direction":78,"ingested_from":80},"W7213235680",[424],{"name":425,"orcid":9},"S. Nasiketha",{"tldr":427,"method":428,"finding":429,"direction":78,"opportunity":430},"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":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":443,"tags":445,"primary_topic":448,"secondary_topics":449,"classification":450,"search_phrases":486,"slug":488,"view_count":20,"doi":489,"paper":490,"created_at":503},2039,"An Adaptive AI-DSS Framework for Real-Time Image Analysis and Decision-Making in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.14445\u002F22315381\u002Fijett-v74i8p136","Environmental variability, pest infestation, and resource inefficiency have become increasingly problematic to agriculture, and on-line decision making using intelligent, adaptive technologies has been called for. This research is suggesting a novel Integrated Artificial Intelligence Decision Support System (AI-DSS) in precision agriculture so as to attain proper context-aware analysis and recommendation across dynamic field conditions. The framework is based on lightweight deep learning frameworks (MobileNetV2, Efficient Net-lite) and a newly designed Adaptive Feature Optimization (AFO) engine, which dynamically reweights convolutional features based on the temporal stability and environment consistency. Mathematically, the AFO mechanism is obtained via a weighted pooling of the instantaneous features of CNN with the temporal averaged prototypes using adaptive attention weights, which filter out transient noises due to illumination variations, occlusion, and sensor noises. The optimized features are fused with data from environmental and soil sensors in a hybrid Decision Support System (DSS) based on rule-based reasoning, Bayesian inference, and temporal tracking to construct explainable and region-specific recommendations to farmers. Experimental evaluations using the experimental data PlantVillage, DeepWeeds, and Fieldstream Sim demonstrate the superiority of the proposed AFO enhanced framework over the baseline CNN classifier in terms of classification accuracy (.95), macro F1 score (.95), and robustness to distortions (.15% improvement). Additionally, when deployed on edge devices like Raspberry Pi 4 and Nvidia Jetson Nano, the system achieves real-time inference latency (\u003C200 ms) and low energy consumption (~520 mJ\u002Fframe), which validates the scalability of the system in low-resource settings. In addition, the expert agreement was enhanced with the DSS module integration to 91.4% with 57% less false alarms. The obtained results validate the proposed AI-DSS framework with AFO as a promising solution to cover the distance between accuracy in a controlled lab environment and reliability in the field, providing a strong, explainable, and resource-efficient solution to the digital sustainable agriculture problem. This solution is further being expanded into proactive farm intelligence and climate resilience through multimodal sensing, satellite assisted crops monitoring, and adaptive decision-making-led solutions.","环境变异、病虫害侵袭和资源低效问题对农业的影响日益严重，亟需利用智能自适应技术进行在线决策。本研究提出了一种用于精准农业的新型集成人工智能决策支持系统（AI-DSS），旨在实现动态田间条件下的情境感知分析与推荐。该框架基于轻量级深度学习框架（MobileNetV2、EfficientNet-lite）和新设计的自适应特征优化（AFO）引擎，后者根据时间稳定性和环境一致性对卷积特征进行动态重加权。在数学上，AFO机制通过自适应注意力权重将CNN的瞬时特征与时间平均原型进行加权池化，从而滤除由光照变化、遮挡和传感器噪声引起的瞬态噪声。优化后的特征与环境和土壤传感器数据在混合决策支持系统（DSS）中融合，该系统基于规则推理、贝叶斯推断和时间追踪，为农民构建可解释的、区域特定的推荐方案。使用PlantVillage、DeepWeeds和Fieldstream Sim实验数据的评估表明，所提出的AFO增强框架在分类准确率（.95）、宏F1分数（.95）和抗失真鲁棒性（提升.15%）方面均优于基线CNN分类器。此外，在Raspberry Pi 4和Nvidia Jetson Nano等边缘设备上部署时，系统实现了实时推理延迟（\u003C200 ms）和低能耗（约520 mJ\u002F帧），验证了该系统在低资源环境下的可扩展性。同时，集成DSS模块后专家一致率提升至91.4%，误报率降低57%。所得结果验证了所提出的带有AFO的AI-DSS框架是一种有前景的解决方案，能够弥合受控实验室环境中的准确性与田间可靠性之间的差距，为数字可持续农业问题提供了稳健、可解释且资源高效的解决方案。该方案正进一步通过多模态传感、卫星辅助作物监测和自适应决策驱动的解决方案，扩展至主动式农场智能和气候韧性领域。","International Journal of Engineering Trends and Technology","2026-09-09T00:00:00Z",79,{"impact":297,"substance":298,"depth":297,"authority":18,"freshness":95,"relevant":21,"comment":442},"提出融合自适应特征优化与多源传感器推理的轻量级AI决策支持框架，在多个公开数据集与边缘设备上验证了精度与实时性，方法新颖、结论可靠，对智慧农业落地有参考价值。",[444],{"name":438,"url":435},[26,100,446,27,447],"边缘计算","精准农业",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":451},{"candidates":452,"agricultural_evidence":44},[453,458,464,468,475,481],{"code":55,"score":165,"supported":44,"title":454,"abstract":456,"tags":457},[455],"precision agriculture",[447,455],[26,447],{"code":30,"score":109,"supported":44,"title":459,"abstract":460,"tags":463},[],[461,462,324,47],"深度学习","deep learning",[100,27],{"code":122,"score":19,"supported":44,"title":465,"abstract":466,"tags":467},[],[125,126],[],{"code":469,"score":19,"supported":44,"title":470,"abstract":471,"tags":474},"H8",[],[472,473],"可持续农业","sustainable agriculture",[],{"code":476,"score":130,"supported":44,"title":477,"abstract":478,"tags":480},"H7",[],[479],"climate resilience",[],{"code":482,"score":21,"supported":14,"title":483,"abstract":484,"tags":485},"I5",[],[],[446],[487,418],"农业人工智能 决策支持系统 智慧农业 精准农业","农业人工智能决策支持系统智慧农业精准农业-2039","10.14445\u002F22315381\u002Fijett-v74i8p136",{"doi":489,"openalex_id":491,"authors":492,"venue":438,"cited_by_count":73,"oa_url":497,"card":498,"direction":152,"ingested_from":80},"W7212021859",[493,495],{"name":494,"orcid":9},"Yebhushi Prashanth",{"name":496,"orcid":9},"Dr.Manna SheelaRani Chetty","https:\u002F\u002Fijettjournal.org\u002FVolume-74\u002FIssue-8\u002FIJETT-V74I8P136.pdf",{"tldr":499,"method":500,"finding":501,"direction":78,"opportunity":502},"提出自适应AI决策支持系统，用轻量网络与特征优化实现精准农业实时图像分析与决策。","MobileNetV2\u002FEfficientNet-lite加自适应特征优化引擎，","分类准确率与宏F1达0.95，边缘设备延迟低于200毫秒，专家一致率91.4%。","可探索多模态传感与卫星遥感融合，提升田间跨区域泛化与气候韧性决策能力。","2026-09-10T23:30:09.239423Z"]