[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4110":3,"related-4110":86},{"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":24,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":66,"slug":69,"view_count":22,"doi":70,"paper":71,"created_at":85},4110,"Functional Time Series Forecasting of Air Temperature Using a Functional Autoregressive Model with Electricity Demand as an Exogenous Variable","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23100794","Short-term air temperature (AT) forecasting is crucial for climate monitoring, integration of renewable energy, agricultural management, and environmental decision-making. The inherent complexity of the temporal structure of AT (nonlinear dynamics, high diurnal periodicity, the interactions between various meteorological variables) makes the conventional statistical and machine learning models (which typically view AT observations as discrete time series and do not make effective use of the continuous functional structure of AT) less effective in predicting AT. This study proposes a functional autoregressive model with exogenous variable (FARX(1)) for predicting daily AT curves in a functional time series (FTS) context, overcoming these limitations. The proposed model is an extension of the classical functional autoregressive model of order one (FAR(1)), which maintains the time dependence and smooth functional structure of the daily temperature measurements; while additionally incorporating electricity demand as the exogenous variable, given its established association with diurnal temperature dynamics. The hourly AT data is approximated by smooth functional curves that enable efficient modeling of the diurnal temperature dynamics and observational noise in the data, and is obtained by an approximation of the data with Fourier basis functions. The ability of the proposed framework to forecast the performance is evaluated by comparing it with five widely used benchmark models: ARIMA, VAR, ANN, ARNN and FAR(1) over extensive overall forecasting, hourly forecasting, weekly forecasting and monthly forecasting scenarios, employing the Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (sMAPE) and Root Mean Square Error (RMSE). The experimental results demonstrate the effectiveness of the proposed FARX(1) model in minimizing the conventional FAR(1)'s forecasting error for all the evaluation scenarios, highlighting the significance of using the exogenous variable of electricity demand in the model for forecasting accuracy. The results also highlight the potential of representing AT as functional observations to obtain a flexible and statistically sound representation of complex temporal variations, which enhances the reliability and robustness of short-term AT forecasts. The concept is proposed as a solution for the meteorology community in operational forecasting to the weather community and as climate monitoring and decision support to weather dependent applications such as renewable energy management.","短期气温（AT）预报对于气候监测、可再生能源整合、农业管理以及环境决策至关重要。气温时间结构的内在复杂性（非线性动力学、显著的日周期变化、多种气象变量之间的相互作用）使得传统的统计模型和机器学习模型（通常将气温观测视为离散时间序列，未能有效利用气温的连续函数结构）在气温预测中效果欠佳。本研究提出了一种带外生变量的函数自回归模型（FARX(1)），用于在函数型时间序列（FTS）框架下预测日气温曲线，从而克服上述局限。该模型是经典一阶函数自回归模型（FAR(1)）的扩展，既保留了日气温观测的时间依赖性和平滑函数结构，又额外将电力需求作为外生变量纳入模型，因为电力需求与日气温动态变化之间存在已知的关联。逐时气温数据通过傅里叶基函数逼近，以平滑函数曲线进行近似表示，从而能够有效建模日气温动态变化和数据中的观测噪声。为评估所提框架的预测性能，将其与五种广泛使用的基准模型进行比较：ARIMA、VAR、ANN、ARNN和FAR(1)，在总体预测、逐时预测、周预测和月预测等多种场景下，采用平均绝对标度误差（MASE）、对称平均绝对百分比误差（sMAPE）和均方根误差（RMSE）进行评价。实验结果表明，所提出的FARX(1)模型在所有评估场景中均能有效降低传统FAR(1)的预测误差，凸显了将电力需求作为外生变量纳入模型对提升预测精度的重要意义。结果还表明，将气温表示为函数型观测数据，能够灵活且统计上合理地刻画复杂的时间变化，从而增强短期气温预报的可靠性和稳健性。该概念可作为气象学界在业务预报中的解决方案，服务于天气领域，并作为气候监测和决策支持的手段。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-02T00:00:00Z","论文",25,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,12,9,1,"方法新颖、对比实验扎实的气温预测论文，对农业气象与能源调度有参考价值，但属学术预印本、应用尚远。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23100793",[29,30,31,32,33],"智慧农业","农业人工智能","农业气象","可再生能源","气温预测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,56,61],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":55},10,true,[],[52,53,54],"机器学习","machine learning","decision support",[30],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"H7",[],[],[31],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[29],[67,68],"FARX 气温预测 电力需求","功能时间序列 气温预测","FARX气温预测电力需求-4110","10.5281\u002Fzenodo.23100794",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":77,"oa_url":6,"card":78,"direction":82,"ingested_from":84},"W7215826363",[74],{"name":75,"orcid":76},"Faheem Jan","https:\u002F\u002Forcid.org\u002F0000-0002-7788-206X",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"提出FARX(1)函数自回归模型，以电力需求为外生变量预测日气温曲线。","用傅里叶基函数将小时气温拟合成函数曲线，构建含外生变量的FARX(1)模型。","FARX(1)在所有预测场景下均优于ARIMA、VAR、ANN等基准，电力需求外生变量显著提升精度。","农业人工智能与决策模型","可将电力需求等非气象外生变量引入农业气象预测，探索多源数据融合的函数型时序建模。","openalex","2026-10-03T23:30:57.612088Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,167,235,324,386,451],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":97,"score_detail":98,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":113,"classification":115,"search_phrases":135,"slug":138,"view_count":22,"doi":139,"paper":140,"created_at":166},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":20,"substance":18,"depth":99,"authority":100,"freshness":17,"relevant":22,"comment":101},18,14,"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[103],{"name":95,"url":92},[29,30,105,31,106],"可解释AI","水稻产量预测",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[114],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":116},{"candidates":117,"agricultural_evidence":49},[118,123,127,131],{"code":35,"score":119,"supported":49,"title":120,"abstract":121,"tags":122},19,[53],[52,53,54],[30,105],{"code":108,"score":100,"supported":49,"title":124,"abstract":125,"tags":126},[109],[110,109],[],{"code":57,"score":22,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[31],{"code":62,"score":22,"supported":14,"title":132,"abstract":133,"tags":134},[],[],[29],[136,137],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":139,"openalex_id":141,"authors":142,"venue":95,"cited_by_count":77,"oa_url":159,"card":160,"direction":165,"ingested_from":84},"W7221657053",[143,145,147,149,151,153,156],{"name":144,"orcid":9},"Noh-Hyun Lee",{"name":146,"orcid":9},"Wonjae Jeong",{"name":148,"orcid":9},"Hyeon-Ji Yang",{"name":150,"orcid":9},"Hana Cho",{"name":152,"orcid":9},"Hyeon-Yeong Ju",{"name":154,"orcid":155},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":157,"orcid":158},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":161,"method":162,"finding":163,"direction":82,"opportunity":164},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:10.483881Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":174,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":175,"score_detail":176,"sources":178,"tags":180,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":211,"slug":214,"view_count":215,"doi":216,"paper":217,"created_at":234},4990,"Smart Crop Advisory System","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i10.081","The Smart Crop Advisory System is a web-based agricultural decision-support platform that uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to assist farmers in making informed farming decisions. Traditional agriculture faces several challenges, including unsuitable crop selection, unpredictable weather conditions, crop diseases, and fluctuations in market prices. The proposed system integrates essential agricultural services, including crop recommendation, weather forecasting, crop disease detection, agricultural market price information, and an AI-based multilingual voice assistant. It also provides farmer profile management, crop price trend analysis, and rainfall trend visualization through an interactive dashboard. The crop recommendation module helps farmers identify suitable crops based on relevant agricultural parameters, while the disease detection module supports the identification of potential crop diseases. Weather and market information help farmers plan agricultural activities and make informed selling decisions. The multilingual voice assistant aims to improve accessibility by enabling farmers to interact with the system through voice-based communication. Overall, the Smart Crop Advisory System aims to improve access to agricultural information, support effective decision-making, promote efficient resource utilization, and encourage sustainable farming practices. Keywords: Smart Agriculture, Crop Recommendation, Machine Learning, Crop Disease Detection, Weather Forecasting, Market Price Analysis, Voice Assistant, Sustainable Agriculture.","智能作物咨询系统是一个基于网络的农业决策支持平台，利用人工智能（AI）和机器学习（ML）技术帮助农民做出明智的农作决策。传统农业面临诸多挑战，包括作物选择不当、天气条件不可预测、作物病害以及市场价格波动。所提出的系统整合了基本农业服务，包括作物推荐、天气预报、作物病害检测、农产品市场价格信息以及基于AI的多语言语音助手。它还通过交互式仪表板提供农民档案管理、作物价格趋势分析和降雨趋势可视化。作物推荐模块帮助农民根据相关农业参数识别适宜作物，而病害检测模块支持识别潜在作物病害。天气和市场信息帮助农民规划农业活动并做出明智的销售决策。多语言语音助手旨在通过使农民能够以语音方式与系统交互来提高可及性。总体而言，智能作物咨询系统旨在改善农业信息的获取，支持有效决策，促进资源高效利用，并鼓励可持续农业实践。关键词：智慧农业、作物推荐、机器学习、作物病害检测、天气预报、市场价格分析、语音助手、可持续农业。","International Journal of Creative and Open Research in Engineering and Management","2026-10-14T00:00:00Z",48,{"impact":17,"substance":100,"depth":20,"authority":87,"freshness":17,"relevant":22,"comment":177},"面向农户的AI农业决策支持系统论文，功能集成度较高但方法新颖性与数据规模有限，属细分领域进展，可作为智慧农业主题聚合素材。",[179],{"name":173,"url":170},[29,30,181,31,182],"病虫害识别","作物推荐",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":186},{"candidates":187,"agricultural_evidence":49},[188,194,200,207],{"code":35,"score":189,"supported":49,"title":190,"abstract":191,"tags":193},11,[],[52,53,192],"artificial intelligence",[30,181],{"code":62,"score":195,"supported":49,"title":196,"abstract":197,"tags":199},7,[],[29,198],"smart agriculture",[29],{"code":201,"score":87,"supported":49,"title":202,"abstract":203,"tags":206},"H8",[],[204,205],"可持续农业","sustainable agriculture",[],{"code":57,"score":22,"supported":14,"title":208,"abstract":209,"tags":210},[],[],[31],[212,213],"AI 作物推荐 语音助手","农业人工智能 病虫害识别 作物推荐 农业气象","AI作物推荐语音助手-4990",2,"10.55041\u002Fijcope.v2i10.081",{"doi":216,"openalex_id":218,"authors":219,"venue":173,"cited_by_count":77,"oa_url":228,"card":229,"direction":165,"ingested_from":84},"W7221803029",[220,222,224,226],{"name":221,"orcid":9},"Bhuvana H P",{"name":223,"orcid":9},"Kavya C M",{"name":225,"orcid":9},"Manjula S",{"name":227,"orcid":9},"Priyanka A","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002FSmart-Crop-Advisory-System.pdf",{"tldr":230,"method":231,"finding":232,"direction":82,"opportunity":233},"构建基于AI\u002FML的Web农业决策支持平台，为农民提供作物推荐、病害检测、天气与市场价格等服务。","集成机器学习模型、多语言语音助手与交互式仪表盘，整合天气、市场及农业参数数据。","系统可提升农业信息获取与决策效率，促进资源高效利用和可持续耕作。","可探索多源数据融合的轻量化模型与本地化语音交互，提升小农户采纳率与实时决策精度。","2026-10-15T23:30:09.048921Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":243,"score_detail":244,"sources":247,"tags":249,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":294,"slug":297,"view_count":276,"doi":298,"paper":299,"created_at":323},4040,"SPATIAL AND CLIMATE DATA ANALYSIS FOR MACHINE LEARNING-BASED CROP YIELD PREDICTION IN NORTHERN KAZAKHSTAN","https:\u002F\u002Fdoi.org\u002F10.32014\u002F2026.2518-1726.462","Crop yield prediction is important for food security, agricultural planning, and effective resource management, particularly in Northern Kazakhstan, where agricultural production is strongly influenced by climate variability. Changes in temperature and precipitation can affect crop productivity and cause considerable differences in yield between years. The aim of this study is to develop, compare, and evaluate machine learning regression models for wheat yield prediction in the North Kazakhstan Region using climatic data. A dataset covering 1990–2025 was prepared using official crop yield statistics from the Bureau of National Statistics of the Republic of Kazakhstan and climatic data from the NASA POWER database. Daily climatic data were collected from five representative geographical points across the North Kazakhstan Region. The climatic variables included average, minimum, and maximum air temperature and precipitation. Daily temperature values were aggregated into annual mean values, while precipitation was summed to obtain annual totals. The annual values from the five geographical points were then averaged to obtain representative regional indicators. Several regression algorithms were compared, including Linear Regression, Random Forest, Gradient Boosting, Bagging Regressor, Decision Tree, and XGBoost. A hybrid model combining XGBoost and Gradient Boosting was also evaluated. Wheat yield was used as the target variable, while climatic variables were used as predictors. To preserve the temporal structure of the data and avoid information leakage, walk-forward validation was applied for 2020–2025. Model performance was evaluated using R², RMSE, and MAE. XGBoost demonstrated the best predictive performance, achieving R² = 0.811, RMSE = 1.436 c\u002Fha, and MAE = 1.160 c\u002Fha. The results demonstrate the potential of machine learning and climatic data for wheat yield prediction and agricultural decision support in Northern Kazakhstan.","作物产量预测对粮食安全、农业规划和有效资源管理具有重要意义，尤其是在哈萨克斯坦北部，该地区农业生产受气候变异性影响显著。气温和降水的变化会影响作物生产力，并导致年际间产量差异较大。本研究旨在利用气候数据，开发、比较和评估用于哈萨克斯坦北部地区小麦产量预测的机器学习回归模型。研究使用哈萨克斯坦共和国国家统计局提供的官方作物产量统计数据和NASA POWER数据库的气候数据，构建了覆盖1990—2025年的数据集。每日气候数据采集自哈萨克斯坦北部地区五个具有代表性的地理点位。气候变量包括平均气温、最低气温、最高气温和降水量。每日气温值被聚合为年均值，降水量则累加得到年总量。随后，对五个地理点位的年度值取平均，以获得具有代表性的区域指标。研究比较了多种回归算法，包括线性回归、随机森林、梯度提升、Bagging回归器、决策树和XGBoost，并评估了XGBoost与梯度提升相结合的混合模型。小麦产量作为目标变量，气候变量作为预测因子。为保持数据的时间结构并避免信息泄漏，研究对2020—2025年采用了前向滚动验证。模型性能采用R²、RMSE和MAE进行评估。XGBoost表现出最佳的预测性能，达到R² = 0.811、RMSE = 1.436 c\u002Fha、MAE = 1.160 c\u002Fha。结果表明，机器学习和气候数据在哈萨克斯坦北部小麦产量预测和农业决策支持方面具有潜力。","NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN","2026-10-01T00:00:00Z",70,{"impact":20,"substance":18,"depth":19,"authority":245,"freshness":17,"relevant":22,"comment":246},13,"基于1990—2025年气候数据与多种机器学习模型的小麦产量预测研究，方法规范、结论可靠，但区域局限于哈萨克斯坦北部，公共影响有限。",[248],{"name":241,"url":238},[29,30,250,251,31],"产量预测","小麦",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":255},{"candidates":256,"agricultural_evidence":49},[257,261,267,274,281,285,289],{"code":35,"score":99,"supported":49,"title":258,"abstract":259,"tags":260},[53],[52,53,54],[30],{"code":262,"score":195,"supported":49,"title":263,"abstract":264,"tags":266},"A2",[],[251,265],"wheat",[251],{"code":268,"score":87,"supported":49,"title":269,"abstract":270,"tags":273},"A8",[],[271,272],"粮食安全","food security",[],{"code":275,"score":276,"supported":49,"title":277,"abstract":278,"tags":280},"J1",3,[],[279],"农业规划",[],{"code":57,"score":22,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[31],{"code":62,"score":22,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[29],{"code":290,"score":22,"supported":14,"title":291,"abstract":292,"tags":293},"I3",[],[],[250],[295,296],"哈萨克斯坦 小麦 产量预测","NASA POWER 气候数据 机器学习","哈萨克斯坦小麦产量预测-4040","10.32014\u002F2026.2518-1726.462",{"doi":298,"openalex_id":300,"authors":301,"venue":241,"cited_by_count":77,"oa_url":317,"card":318,"direction":82,"ingested_from":84},"W7215105964",[302,305,308,311,314],{"name":303,"orcid":304},"Aigul Mimenbayeva","https:\u002F\u002Forcid.org\u002F0000-0003-4652-470X",{"name":306,"orcid":307},"A A Naizagarayeva","https:\u002F\u002Forcid.org\u002F0000-0001-6888-1092",{"name":309,"orcid":310},"Tleugaisha Ospanova","https:\u002F\u002Forcid.org\u002F0000-0002-1729-1321",{"name":312,"orcid":313},"D. K. Koikelova","https:\u002F\u002Forcid.org\u002F0009-0003-6644-9345",{"name":315,"orcid":316},"Raikhan Kudabayeva","https:\u002F\u002Forcid.org\u002F0009-0002-0827-9395","https:\u002F\u002Fjournals.nauka-nanrk.kz\u002Fphysics-mathematics\u002Farticle\u002Fdownload\u002F8926\u002F5764",{"tldr":319,"method":320,"finding":321,"direction":82,"opportunity":322},"用1990–2025年气候数据与机器学习模型预测哈萨克斯坦北部小麦产量。","NASA POWER气候数据+官方产量统计，比较XGBoost等回归模型并采用前","XGBoost预测效果最佳，R²=0.811，RMSE=1.436 c\u002Fha，MAE=1.160 c","可引入遥感植被指数与土壤数据，构建多源融合模型并迁移至其他气候敏感区。","2026-10-02T23:30:45.440623Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":10,"source_url":327,"published_at":330,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":331,"score_detail":332,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":347,"classification":349,"search_phrases":366,"slug":369,"view_count":77,"doi":370,"paper":371,"created_at":385},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","2026-10-21T00:00:00Z",69,{"impact":17,"substance":333,"depth":99,"authority":20,"freshness":48,"relevant":22,"comment":334},21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[336],{"name":10,"url":327},[29,30,338,339,340],"小农户","植物病害识别","可解释性",{"code":342,"slug":343,"name":344,"path":345,"parent_code":346},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[348],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":350},{"candidates":351,"agricultural_evidence":49},[352,357,362],{"code":342,"score":20,"supported":49,"title":353,"abstract":355,"tags":356},[354],"smallholder",[354],[338],{"code":35,"score":48,"supported":49,"title":358,"abstract":360,"tags":361},[359],"computer vision",[],[30,339],{"code":62,"score":22,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[29],[367,368],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":370,"openalex_id":372,"authors":373,"venue":10,"cited_by_count":77,"oa_url":327,"card":380,"direction":82,"ingested_from":84},"W7223137323",[374,376,378],{"name":375,"orcid":9},"Akshay Ramgoolam",{"name":377,"orcid":9},"Priya Bundhoo Appadoo",{"name":379,"orcid":9},"Jean-Noël LEspérance",{"tldr":381,"method":382,"finding":383,"direction":82,"opportunity":384},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":10,"source_url":389,"published_at":330,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":331,"score_detail":392,"sources":395,"tags":397,"primary_topic":401,"secondary_topics":407,"classification":409,"search_phrases":433,"slug":436,"view_count":77,"doi":437,"paper":438,"created_at":450},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":17,"substance":393,"depth":99,"authority":20,"freshness":21,"relevant":22,"comment":394},22,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[396],{"name":10,"url":389},[29,30,398,399,400],"灌溉决策","安全保证","试验设计",{"code":402,"slug":403,"name":404,"path":405,"parent_code":406},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[408],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":410},{"candidates":411,"agricultural_evidence":49},[412,418,422,429],{"code":402,"score":413,"supported":49,"title":414,"abstract":415,"tags":417},15,[403],[416,403],"灌溉",[398],{"code":35,"score":20,"supported":49,"title":419,"abstract":420,"tags":421},[192],[54],[30],{"code":423,"score":87,"supported":49,"title":424,"abstract":425,"tags":428},"H2",[],[426,427],"土壤","soil",[],{"code":62,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[29],[434,435],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":437,"openalex_id":439,"authors":440,"venue":10,"cited_by_count":77,"oa_url":389,"card":445,"direction":82,"ingested_from":84},"W7223131205",[441,443],{"name":442,"orcid":9},"Nassima Benkhelifa",{"name":444,"orcid":9},"Yacine Aït Ahmed",{"tldr":446,"method":447,"finding":448,"direction":82,"opportunity":449},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":452,"title":453,"url":454,"summary":455,"summary_zh":456,"content":9,"source_name":10,"source_url":454,"published_at":330,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":243,"score_detail":457,"sources":459,"tags":461,"primary_topic":464,"secondary_topics":465,"classification":467,"search_phrases":482,"slug":485,"view_count":77,"doi":486,"paper":487,"created_at":501},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",{"impact":17,"substance":393,"depth":99,"authority":20,"freshness":48,"relevant":22,"comment":458},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[460],{"name":10,"url":454},[29,30,338,462,463],"作物病害识别","模型审计",{"code":342,"slug":343,"name":344,"path":345,"parent_code":346},[466],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":468},{"candidates":469,"agricultural_evidence":49},[470,474,478],{"code":342,"score":413,"supported":49,"title":471,"abstract":472,"tags":473},[354],[338,354],[338],{"code":35,"score":48,"supported":49,"title":475,"abstract":476,"tags":477},[359],[],[462,30],{"code":62,"score":22,"supported":14,"title":479,"abstract":480,"tags":481},[],[],[29],[483,484],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":486,"openalex_id":488,"authors":489,"venue":10,"cited_by_count":77,"oa_url":454,"card":496,"direction":82,"ingested_from":84},"W7223088030",[490,492,494],{"name":491,"orcid":9},"Abdi-Basid Idriss Farah",{"name":493,"orcid":9},"Amina Abdillahi Guedi",{"name":495,"orcid":9},"Moussa Omar Youssouf",{"tldr":497,"method":498,"finding":499,"direction":82,"opportunity":500},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z"]