[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2948":3,"related-2948":135},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":116,"slug":119,"view_count":98,"doi":120,"paper":121,"created_at":134},2948,"Machine learning and remote sensing for smallholder precision agriculture in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04538-2","While machine learning (ML) and remote sensing (RS) are frequently heralded as the definitive solutions for agricultural resilience in Sub-Saharan Africa, a profound ‘implementation gap’ persists between laboratory-validated computational maturity and field-level utility for smallholder farmers. This systematic review, conducted under preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, critically analyzes why sophisticated models optimized for large-scale monocultures fail within the fragmented, intercropped landscapes of Ethiopia. By synthesizing empirical evidence across four domains—in-season crop yield forecasting, digital soil mapping, real-time biotic stress detection, and agro-meteorological modeling—the review uncover a fundamental scale mismatch between coarse-resolution satellite observations and sub-hectare micro-plots. The critique identifies localized data scarcity, hardware constraints, and the ‘last-mile’ connectivity divide as the primary friction points obstructing the transition from macro-level pixels to actionable, site-specific agricultural intelligence. Moving beyond simple summary, the study propose a strategic roadmap centered on decentralized edge computing, tinyML optimizations, and a restructuring of extension services to integrate digital intelligence into daily smallholder decision-making. These structural shifts are essential to bridge the digital divide and secure Ethiopia’s national food security against escalating climate variability. This review foregrounds the significance of digital agriculture within the context of the sustainable development goals (SDGs), specifically addressing SDG 2 (zero hunger) and SDG 13 (climate action) by enhancing crop productivity and building resilience in smallholder systems.","尽管机器学习（ML）与遥感（RS）常被标榜为撒哈拉以南非洲农业韧性的终极解决方案，但实验室验证的计算成熟度与小农户田间实用性之间仍存在深刻的“实施鸿沟”。本系统综述依据系统综述和荟萃分析首选报告条目（PRISMA）2020指南开展，批判性地分析了为何针对大规模单一种植优化的复杂模型在埃塞俄比亚碎片化、间作化的景观中失效。通过综合四个领域的实证证据——季内作物产量预测、数字土壤制图、实时生物胁迫检测和农业气象建模——本综述揭示了粗分辨率卫星观测与亚公顷微地块之间的根本性尺度错配。该批判性分析将局部数据稀缺、硬件约束和“最后一公里”连接鸿沟确定为阻碍从宏观像元向可操作、因地制宜的农业智能转化的主要摩擦点。本研究超越简单的总结，提出了一条以去中心化边缘计算、tinyML优化和推广服务体系重构为核心的战略路线图，旨在将数字智能融入小农户的日常决策。这些结构性转变对于弥合数字鸿沟、保障埃塞俄比亚在日益加剧的气候变率下的国家粮食安全至关重要。本综述凸显了数字农业在可持续发展目标（SDGs）背景下的重要意义，特别是通过提升作物生产力和增强小农系统韧性来回应SDG 2（零饥饿）和SDG 13（气候行动）。",null,"Discover Sustainability","2026-09-18T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,9,1,"系统综述揭示机器学习与遥感在小农场景的落地鸿沟，并提出边缘计算与tinyML路线图，对数字农业与SDG研究有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","小农户","数字鸿沟","遥感",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":115},[38,46,54,61,67,74,82,89,96,103,109],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"G2",15,true,[43],"smallholder",[28,43],[28],{"code":47,"score":40,"supported":41,"title":48,"abstract":50,"tags":53},"I1",[49],"precision agriculture",[51,52],"数字农业","digital agriculture",[26],{"code":55,"score":40,"supported":41,"title":56,"abstract":58,"tags":60},"I2",[57],"machine learning",[59,57],"机器学习",[27],{"code":62,"score":40,"supported":41,"title":63,"abstract":65,"tags":66},"I3",[64],"remote sensing",[30,64],[30],{"code":68,"score":69,"supported":41,"title":70,"abstract":71,"tags":73},"I6",7,[],[29,72],"digital divide",[29],{"code":75,"score":76,"supported":41,"title":77,"abstract":78,"tags":81},"A8",6,[],[79,80],"粮食安全","food security",[],{"code":83,"score":76,"supported":41,"title":84,"abstract":85,"tags":88},"H2",[],[86,87],"土壤","soil",[],{"code":90,"score":76,"supported":41,"title":91,"abstract":92,"tags":95},"I5",[],[93,94],"边缘计算","edge computing",[],{"code":97,"score":98,"supported":41,"title":99,"abstract":100,"tags":102},"A7",3,[],[101],"间作",[],{"code":104,"score":98,"supported":41,"title":105,"abstract":106,"tags":108},"H7",[],[107],"农业气象",[],{"code":110,"score":98,"supported":41,"title":111,"abstract":112,"tags":114},"J3",[],[113],"非洲农业",[],"多个产业证据相当，需要确认主归属",[117,118],"埃塞俄比亚 小农户 精准农业","机器学习 遥感 小农","埃塞俄比亚小农户精准农业-2948","10.1007\u002Fs43621-026-04538-2",{"doi":120,"openalex_id":122,"authors":123,"venue":10,"cited_by_count":126,"oa_url":6,"card":127,"direction":131,"ingested_from":133},"W7213562005",[124],{"name":125,"orcid":9},"Abrha Asefa",0,{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。","PRISMA 2020 系统综述，综合四领域实证证据。","粗分辨率卫星与亚公顷微地块尺度不匹配，数据稀缺和连接鸿沟阻碍落地。","农业遥感与作物表型","面向碎片化间作小农的 tinyML 边缘计算与本地化数据采集，是填补落地鸿沟的关键方向。","openalex","2026-09-19T23:30:33.334404Z",{"total":76,"page":21,"page_size":76,"items":136},[137,204,257,313,366,478],{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":9,"source_name":143,"source_url":140,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":145,"score_detail":146,"sources":150,"tags":152,"primary_topic":155,"secondary_topics":160,"classification":166,"search_phrases":183,"slug":186,"view_count":126,"doi":187,"paper":188,"created_at":203},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":147,"substance":18,"depth":17,"authority":148,"freshness":13,"relevant":21,"comment":149},8,12,"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。",[151],{"name":143,"url":140},[26,27,28,153,154],"植物病害识别","可解释性",{"code":39,"slug":156,"name":157,"path":158,"parent_code":159},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[161],{"code":55,"slug":162,"name":163,"path":164,"parent_code":165},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":167,"locked":14,"version":34,"method":35,"evidence":168},"classified",{"candidates":169,"agricultural_evidence":41},[170,174,179],{"code":39,"score":148,"supported":41,"title":171,"abstract":172,"tags":173},[43],[43],[28],{"code":55,"score":13,"supported":41,"title":175,"abstract":177,"tags":178},[176],"computer vision",[],[27,153],{"code":47,"score":21,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[26],[184,185],"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":187,"openalex_id":189,"authors":190,"venue":143,"cited_by_count":126,"oa_url":140,"card":197,"direction":201,"ingested_from":133},"W7223137323",[191,193,195],{"name":192,"orcid":9},"Akshay Ramgoolam",{"name":194,"orcid":9},"Priya Bundhoo Appadoo",{"name":196,"orcid":9},"Jean-Noël LEspérance",{"tldr":198,"method":199,"finding":200,"direction":201,"opportunity":202},"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。","农业人工智能与决策模型","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":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":143,"source_url":207,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":214,"tags":216,"primary_topic":219,"secondary_topics":220,"classification":222,"search_phrases":237,"slug":240,"view_count":126,"doi":241,"paper":242,"created_at":256},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。",70,{"impact":147,"substance":212,"depth":17,"authority":148,"freshness":13,"relevant":21,"comment":213},22,"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。",[215],{"name":143,"url":207},[26,27,28,217,218],"作物病害识别","模型审计",{"code":39,"slug":156,"name":157,"path":158,"parent_code":159},[221],{"code":55,"slug":162,"name":163,"path":164,"parent_code":165},{"status":167,"locked":14,"version":34,"method":35,"evidence":223},{"candidates":224,"agricultural_evidence":41},[225,229,233],{"code":39,"score":40,"supported":41,"title":226,"abstract":227,"tags":228},[43],[28,43],[28],{"code":55,"score":13,"supported":41,"title":230,"abstract":231,"tags":232},[176],[],[217,27],{"code":47,"score":21,"supported":14,"title":234,"abstract":235,"tags":236},[],[],[26],[238,239],"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":241,"openalex_id":243,"authors":244,"venue":143,"cited_by_count":126,"oa_url":207,"card":251,"direction":201,"ingested_from":133},"W7223088030",[245,247,249],{"name":246,"orcid":9},"Abdi-Basid Idriss Farah",{"name":248,"orcid":9},"Amina Abdillahi Guedi",{"name":250,"orcid":9},"Moussa Omar Youssouf",{"tldr":252,"method":253,"finding":254,"direction":201,"opportunity":255},"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",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":143,"source_url":260,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":297,"slug":300,"view_count":126,"doi":301,"paper":302,"created_at":312},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。",74,{"impact":148,"substance":212,"depth":17,"authority":148,"freshness":13,"relevant":21,"comment":265},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[267],{"name":143,"url":260},[26,27,79,30,269],"气候分析",{"code":55,"slug":162,"name":163,"path":164,"parent_code":165},[],{"status":167,"locked":14,"version":34,"method":35,"evidence":273},{"candidates":274,"agricultural_evidence":41},[275,280,285,289,293],{"code":55,"score":20,"supported":41,"title":276,"abstract":278,"tags":279},[277],"artificial intelligence",[],[27],{"code":90,"score":98,"supported":41,"title":281,"abstract":282,"tags":284},[],[283],"农业传感",[],{"code":75,"score":21,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[79],{"code":47,"score":21,"supported":14,"title":290,"abstract":291,"tags":292},[],[],[26],{"code":62,"score":21,"supported":14,"title":294,"abstract":295,"tags":296},[],[],[30],[298,299],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":301,"openalex_id":303,"authors":304,"venue":143,"cited_by_count":126,"oa_url":260,"card":307,"direction":201,"ingested_from":133},"W7223111766",[305],{"name":306,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":308,"method":309,"finding":310,"direction":131,"opportunity":311},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":9,"source_name":143,"source_url":316,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":324,"tags":326,"primary_topic":328,"secondary_topics":329,"classification":334,"search_phrases":350,"slug":353,"view_count":126,"doi":354,"paper":355,"created_at":365},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":147,"substance":321,"depth":322,"authority":148,"freshness":13,"relevant":21,"comment":323},20,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[325],{"name":143,"url":316},[26,27,327,30,154],"产量预测",{"code":55,"slug":162,"name":163,"path":164,"parent_code":165},[330],{"code":62,"slug":331,"name":332,"path":333,"parent_code":165},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":167,"locked":14,"version":34,"method":35,"evidence":335},{"candidates":336,"agricultural_evidence":41},[337,342,346],{"code":55,"score":148,"supported":41,"title":338,"abstract":339,"tags":341},[176],[340],"计算机视觉",[27],{"code":62,"score":147,"supported":41,"title":343,"abstract":344,"tags":345},[],[30,64],[327,30],{"code":47,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[26],[351,352],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":354,"openalex_id":356,"authors":357,"venue":143,"cited_by_count":126,"oa_url":316,"card":360,"direction":131,"ingested_from":133},"W7223114002",[358],{"name":359,"orcid":9},"Kadiatou Barry",{"tldr":361,"method":362,"finding":363,"direction":131,"opportunity":364},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":9,"source_name":372,"source_url":369,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":373,"sources":375,"tags":377,"primary_topic":380,"secondary_topics":386,"classification":392,"search_phrases":448,"slug":451,"view_count":126,"doi":452,"paper":453,"created_at":477},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",{"impact":17,"substance":212,"depth":17,"authority":148,"freshness":20,"relevant":21,"comment":374},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[376],{"name":372,"url":369},[26,27,30,378,379],"作物水分胁迫","精准灌溉",{"code":381,"slug":382,"name":383,"path":384,"parent_code":385},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[387,388],{"code":55,"slug":162,"name":163,"path":164,"parent_code":165},{"code":90,"slug":389,"name":390,"path":391,"parent_code":165},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":167,"locked":14,"version":34,"method":35,"evidence":393},{"candidates":394,"agricultural_evidence":41},[395,400,404,411,417,421,428,435,442],{"code":381,"score":40,"supported":41,"title":396,"abstract":397,"tags":399},[382],[398,382],"灌溉",[379],{"code":55,"score":13,"supported":41,"title":401,"abstract":402,"tags":403},[],[59,57,277],[27],{"code":90,"score":20,"supported":41,"title":405,"abstract":406,"tags":410},[],[407,408,409],"物联网","internet of things","IoT",[],{"code":47,"score":69,"supported":41,"title":412,"abstract":413,"tags":416},[],[414,415],"数字孪生","digital twin",[26],{"code":62,"score":69,"supported":41,"title":418,"abstract":419,"tags":420},[],[30,64],[30],{"code":422,"score":76,"supported":41,"title":423,"abstract":424,"tags":427},"A2",[],[425,426],"小麦","wheat",[],{"code":429,"score":76,"supported":41,"title":430,"abstract":431,"tags":434},"A3",[],[432,433],"玉米","maize",[],{"code":436,"score":76,"supported":41,"title":437,"abstract":438,"tags":441},"A5",[],[439,440],"马铃薯","potato",[],{"code":443,"score":98,"supported":41,"title":444,"abstract":445,"tags":447},"B2",[],[446],"葡萄",[],[449,450],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":452,"openalex_id":454,"authors":455,"venue":372,"cited_by_count":126,"oa_url":369,"card":471,"direction":476,"ingested_from":133},"W7223088921",[456,459,462,465,468],{"name":457,"orcid":458},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":460,"orcid":461},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":463,"orcid":464},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":466,"orcid":467},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":469,"orcid":470},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":472,"method":473,"finding":474,"direction":131,"opportunity":475},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":9,"source_name":484,"source_url":481,"published_at":485,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":486,"score_detail":487,"sources":489,"tags":491,"primary_topic":493,"secondary_topics":498,"classification":500,"search_phrases":529,"slug":532,"view_count":126,"doi":533,"paper":534,"created_at":547},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":17,"substance":212,"depth":17,"authority":148,"freshness":147,"relevant":21,"comment":488},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[490],{"name":484,"url":481},[26,27,492,327,30],"水稻",{"code":494,"slug":495,"name":492,"path":496,"parent_code":497},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[499],{"code":62,"slug":331,"name":332,"path":333,"parent_code":165},{"status":167,"locked":14,"version":34,"method":35,"evidence":501},{"candidates":502,"agricultural_evidence":41},[503,507,511,515,521,525],{"code":494,"score":40,"supported":41,"title":504,"abstract":505,"tags":506},[495],[492,495],[492],{"code":62,"score":147,"supported":41,"title":508,"abstract":509,"tags":510},[],[30,64],[327,30],{"code":75,"score":76,"supported":41,"title":512,"abstract":513,"tags":514},[],[79,80],[],{"code":516,"score":98,"supported":41,"title":517,"abstract":518,"tags":520},"J1",[],[519],"农业规划",[],{"code":47,"score":21,"supported":14,"title":522,"abstract":523,"tags":524},[],[],[26],{"code":55,"score":21,"supported":14,"title":526,"abstract":527,"tags":528},[],[],[27],[530,531],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":533,"openalex_id":535,"authors":536,"venue":484,"cited_by_count":126,"oa_url":481,"card":542,"direction":131,"ingested_from":133},"W7222942064",[537,539],{"name":538,"orcid":9},"Nayana Venkatesh",{"name":540,"orcid":541},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":543,"method":544,"finding":545,"direction":131,"opportunity":546},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z"]