[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5247":3,"related-5247":102},{"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":29,"primary_topic":35,"secondary_topics":41,"classification":42,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":101},5247,"From transfers to transformation: mobile money and the modernization of rice farming in Côte d’Ivoire","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34660410","In this study, we explore how Mobile Money (MM) adoption influences farm management choices of smallholder rice production in Côte d’Ivoire, a West African context characterised by rich agricultural potential and moderately low digital financial inclusion. Using an endogenous switching regression (ESR) framework to a sample of 1,370 smallholder producers from three rice-producing departments and complemented by an inverse probability weighted regression adjustment (IPWRA) estimator for robustness to estimate the impact of MM on both agricultural intensification (higher fertilizer use), extensification (greater expansion of cultivated areas for food and cash crops outside rice farming system), total rice productivity and better market participation. There is evidence to suggest that remittances may be a plausible channel through which these effects work, by alleviating liquidity constraints, thus allowing clients to invest seasonally in inputs and labor. Our findings extend the mobile money literature, which remains concentrated in East Africa, by providing new evidence from an under-explored West African context, and offers important implications for policies promoting digital financial inclusion as a means of rural transformation. This study looks at the role of mobile money in the modernization of smallholder rice farming in Côte d’Ivoire. Using survey data from rice farmers, we examine whether mobile money use is linked to changes in farm management, input use, productivity, and access to markets. The results provide evidence that mobile financial services can help farmers manage some of the financial and transaction constraints associated with agricultural production. The study adds to the limited empirical evidence on the productive use of mobile money in African agriculture and provides useful insights for policies aimed at expanding digital financial services among smallholder farmers.","本研究探讨移动货币（Mobile Money，MM）的采用如何影响科特迪瓦小农户水稻生产的农场管理选择，该西非背景以丰富的农业潜力和中等偏低的数字金融普惠水平为特征。研究采用内生转换回归（Endogenous Switching Regression，ESR）框架，对来自三个水稻生产部门的1，370名小农户生产者样本进行分析，并辅以逆概率加权回归调整（Inverse Probability Weighted Regression Adjustment，IPWRA）估计量进行稳健性检验，以估计移动货币对农业集约化（提高化肥使用量）、外延化（在稻田系统之外扩大粮食作物和经济作物的种植面积）、水稻总生产率以及更好的市场参与的影响。有证据表明，汇款可能是一个可行的渠道，通过这些效应发挥作用，即缓解流动性约束，从而使客户能够按季节投资于投入品和劳动力。我们的研究拓展了移动货币文献——该文献仍集中于东非——通过提供来自一个尚未充分探索的西非背景的新证据，并为促进数字金融普惠作为农村转型手段的政策提供了重要启示。本研究考察移动货币在科特迪瓦小农户水稻种植现代化中的作用。利用水稻种植者的调查数据，我们检验移动货币的使用是否与农场管理、投入品使用、生产率和市场准入的变化相关联。结果提供证据表明，移动金融服务可以帮助农民应对与农业生产相关的一些财务和交易约束。本研究补充了关于移动货币在非洲农业中生产性用途的有限实证证据，并为旨在扩大数字金融服务在小农户中覆盖面的政策提供了有益见解。",null,"Figshare","2026-10-17T00:00:00Z","论文",25,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,6,8,1,"该研究基于1370户样本，采用ESR与IPWRA方法，为西非移动支付促进水稻生产现代化提供了新证据，但属区域性学术成果，公共影响有限，未达每日精选门槛。",[25,26],{"name":10,"url":6},{"name":27,"url":28},"Cogent Economics & Finance","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23322039.2026.2740883",[30,31,32,33,34],"数字普惠金融","小农户","水稻种植","移动支付","科特迪瓦",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,55,62,69,75],{"code":36,"score":49,"supported":50,"title":51,"abstract":52,"tags":54},18,true,[37],[38,53,37],"稻田",[32],{"code":56,"score":57,"supported":50,"title":58,"abstract":59,"tags":61},"G2",7,[],[31,60],"smallholder",[31],{"code":63,"score":64,"supported":50,"title":65,"abstract":66,"tags":68},"F3",3,[],[67],"fertilizer",[],{"code":70,"score":64,"supported":50,"title":71,"abstract":72,"tags":74},"J3",[],[73],"非洲农业",[],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"G3",[],[],[30],[81,82],"科特迪瓦 水稻 移动支付","mobile money 小农户 水稻","科特迪瓦水稻移动支付-5247",0,"10.6084\u002Fm9.figshare.34660410",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":84,"oa_url":6,"card":94,"direction":98,"ingested_from":100},"W7222786991",[89,92],{"name":90,"orcid":91},"Innocent Daniel Gniza","https:\u002F\u002Forcid.org\u002F0000-0002-9617-1562",{"name":93,"orcid":9},"Grakolet Arnold Gourene",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"研究科特迪瓦移动支付对小农户水稻种植管理、生产力与市场参与的影响。","内生转换回归与IPWRA，基于三国1370户稻农调查数据。","移动支付通过缓解流动性约束促进施肥、扩种、提高水稻生产力与市场参与。","数字乡村与农业信息化","可延伸研究移动支付汇款渠道对西非小农户投入季节性与数字金融普惠政策的长期效应。","openalex","2026-10-18T23:30:39.982038Z",{"total":20,"page":22,"page_size":20,"items":103},[104,193,255,311,393,459],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":113,"score_detail":114,"sources":118,"tags":120,"primary_topic":125,"secondary_topics":126,"classification":139,"search_phrases":165,"slug":168,"view_count":169,"doi":170,"paper":171,"created_at":192},4427,"Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges","https:\u002F\u002Fdoi.org\u002F10.56581\u002Fijrerd.11.05.01-08","The development of Internet of Things (IoT) technology provides opportunities for faster and continuous soil monitoring to support precision agriculture.Monitoring soil pH and phosphorus is important in rice cultivation because both parameters are associated with soil chemical conditions and nutrient availability.This review aimed to examine the development of IoT-based soil sensors for monitoring soil pH and phosphorus, including sensor performance, factors affecting measurement results, and potential applications.A literature review was conducted on research and review articles published between 2019 and 2026.The literature was descriptively analyzed and synthesized based on technological development, sensor performance, and environmental and soil characteristics.The results indicate that pH and phosphorus sensors have been developed using various sensing approaches and have demonstrated promising performance under certain conditions; however, measurement results vary across parameters and soil conditions.Sensor performance is influenced by the sensing principle, soil characteristics, environmental conditions, calibration, and validation.IoT-based sensors have the potential to support real-time soil monitoring, but their application as quantitative measurement tools still requires validation and calibration under diverse soil conditions.","物联网（IoT）技术的发展为实现更快、连续性的土壤监测以支持精准农业提供了机遇。监测土壤pH和磷在水稻种植中具有重要意义，因为这两个参数均与土壤化学状况和养分有效性相关。本综述旨在考察用于监测土壤pH和磷的基于物联网的土壤传感器的发展现状，包括传感器性能、影响测量结果的因素以及潜在应用。本文对2019年至2026年间发表的研究论文和综述文章进行了文献综述。基于技术发展、传感器性能以及环境和土壤特征，对文献进行了描述性分析和综合。结果表明，pH和磷传感器已采用多种传感方法进行开发，并在特定条件下展现出良好性能；然而，测量结果因参数和土壤条件的不同而存在差异。传感器性能受传感原理、土壤特性、环境条件、校准和验证的影响。基于物联网的传感器具有支持实时土壤监测的潜力，但其作为定量测量工具的应用仍需在不同土壤条件下进行验证和校准。","International journal of recent engineering research and development.","2026-10-06T00:00:00Z",10,62,{"impact":17,"substance":49,"depth":115,"authority":116,"freshness":21,"relevant":22,"comment":117},15,9,"综述性论文，系统梳理IoT土壤pH与磷传感器的发展、精度与落地挑战，对智慧农业土壤监测方向有参考价值，但非突破性成果。",[119],{"name":110,"url":107},[121,122,123,124,32],"智慧农业","物联网","精准农业","土壤监测",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[127,133],{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":134,"slug":135,"name":136,"path":137,"parent_code":138},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":43,"locked":14,"version":44,"method":45,"evidence":140},{"candidates":141,"agricultural_evidence":50},[142,148,153,159],{"code":128,"score":49,"supported":50,"title":143,"abstract":145,"tags":147},[144],"internet of things",[122,144,146],"IoT",[122],{"code":36,"score":115,"supported":50,"title":149,"abstract":151,"tags":152},[150],"paddy",[38,37],[32],{"code":134,"score":154,"supported":50,"title":155,"abstract":156,"tags":158},14,[135],[157,135],"土壤",[],{"code":160,"score":21,"supported":50,"title":161,"abstract":162,"tags":164},"I1",[],[123,163],"precision agriculture",[121,123],[166,167],"物联网 土壤传感器 稻田","土壤pH 监测","物联网土壤传感器稻田-4427",2,"10.56581\u002Fijrerd.11.05.01-08",{"doi":170,"openalex_id":172,"authors":173,"venue":110,"cited_by_count":84,"oa_url":107,"card":186,"direction":190,"ingested_from":100},"W7220841659",[174,176,179,181,184],{"name":175,"orcid":9},"Fariha Laqania",{"name":177,"orcid":178},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":180,"orcid":9},"Ade Setiawan",{"name":182,"orcid":183},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":185,"orcid":9},"Wahyu Daradjat",{"tldr":187,"method":188,"finding":189,"direction":190,"opportunity":191},"综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。","文献综述，按技术发展、传感器性能与土壤环境特征归纳分析。","传感器在特定条件下表现良好，但精度受原理、土壤、环境与校准影响，定量应用仍需验证。","智慧农业 \u002F 农业物联网","稻田复杂土壤条件下pH\u002F磷传感器的原位校准与多源数据融合验证是待突破的研究空白。","2026-10-08T23:30:13.316611Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":111,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":200,"score_detail":201,"sources":204,"tags":206,"primary_topic":211,"secondary_topics":212,"classification":218,"search_phrases":234,"slug":237,"view_count":169,"doi":238,"paper":239,"created_at":254},4330,"Youth and the future of labor- and knowledge-intensive small-scale farming in West Africa under climate change and political instability","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1884625","Debates on the future of smallholder farming in sub-Saharan Africa often interpret rural youth mobility and non-farm work as withdrawal from agriculture. This article examines how rural youth in coastal Guinea-Bissau engage with labour- and knowledge-intensive, climate-vulnerable mangrove swamp rice farming, and the conditions under which agriculture remains part of their imagined futures. We combine long-term ethnographic research, updated between 2020 and 2024, with a survey of 770 household heads in 13 villages, 98 biographical interviews, and conversations and group discussions involving 90 participants across 17 villages. Quantitative indicators were compared across household-head age groups, while qualitative material was analysed thematically and narratively. Results show that many young household heads remain engaged in agriculture while combining it with education, mobility and non-farm activities. Age differences in access to mangrove rice land are limited, although young household heads face higher dependency ratios and substantial rice deficits. Biographical evidence indicates that mobility and non-farm earnings often complement rather than replace agriculture. Nevertheless, climate-related risks, high labour costs, weak markets, political instability and inadequate rural services undermine its attractiveness. The study challenges binary portrayals of youth as either farmers or agricultural ‘exiters’ and shows that the future of mangrove rice farming depends on enabling diversified and mobile rural livelihoods.","关于撒哈拉以南非洲小农农业未来的争论，常将农村青年流动与非农工作解读为脱离农业。本文考察了几内亚比绍沿海农村青年如何参与劳动与知识密集、易受气候影响的红树林沼泽稻作农业，以及在何种条件下农业仍留在他们设想中的未来里。我们结合了长期民族志研究（2020年至2024年间更新），以及对13个村庄770名户主的调查、98份传记访谈，以及覆盖17个村庄90名参与者的对话和小组讨论。定量指标按户主年龄组进行比较，定性材料则进行主题和叙事分析。结果表明，许多年轻户主仍从事农业，同时将其与教育、流动和非农活动相结合。获得红树林稻田方面的年龄差异有限，尽管年轻户主面临更高的抚养比和严重的水稻短缺。传记证据表明，流动和非农收入往往补充而非取代农业。然而，气候相关风险、高劳动力成本、薄弱市场、政治不稳定和农村服务不足削弱了其吸引力。该研究挑战了将青年二分为农民或农业“退出者”的刻板印象，并表明红树林稻作农业的未来取决于能否实现多元化和流动性的农村生计。","Frontiers in Sustainable Food Systems",68,{"impact":21,"substance":18,"depth":19,"authority":202,"freshness":116,"relevant":22,"comment":203},13,"基于长期田野调查与770户问卷的西非红树林水稻研究，方法扎实、结论有反思价值，但地域性强、与国内农业信息化关联有限，适合作为国际视野类选题而非每日精选头条。",[205],{"name":199,"url":196},[207,32,208,209,210],"气候变化","西非农业","小农农业","农村青年",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[213],{"code":214,"slug":215,"name":216,"path":217,"parent_code":138},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":43,"locked":14,"version":44,"method":45,"evidence":219},{"candidates":220,"agricultural_evidence":50},[221,225,230],{"code":36,"score":112,"supported":50,"title":222,"abstract":223,"tags":224},[],[38,53,37],[32],{"code":214,"score":116,"supported":50,"title":226,"abstract":228,"tags":229},[227],"climate change",[],[207],{"code":56,"score":64,"supported":50,"title":231,"abstract":232,"tags":233},[],[60],[],[235,236],"几内亚比绍 红树林水稻","西非 小农农业 青年","几内亚比绍红树林水稻-4330","10.3389\u002Ffsufs.2026.1884625",{"doi":238,"openalex_id":240,"authors":241,"venue":199,"cited_by_count":84,"oa_url":248,"card":249,"direction":98,"ingested_from":100},"W7220725843",[242,245],{"name":243,"orcid":244},"Merlin Leunda Martiarena","https:\u002F\u002Forcid.org\u002F0000-0001-5096-0737",{"name":246,"orcid":247},"Marina Padrão Temudo","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4233","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1884625\u002Fpdf",{"tldr":250,"method":251,"finding":252,"direction":98,"opportunity":253},"研究西非青年如何参与劳动与知识密集的红树林水稻种植，并探讨农业仍在其未来想象中保留的条件。","长期民族志（2020-2024更新）结合770户调查、98份传记访谈及17村小组","青年常将务农与教育、流动和非农活动结合，流动与非农收入补充而非替代农业，但气候风险、高劳动成本、弱市","可探索数字服务与信息干预如何降低劳动成本、改善市场接入，从而增强气候脆弱区青年务农的吸引力。","2026-10-07T23:30:06.363343Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":9,"content":9,"source_name":260,"source_url":258,"published_at":261,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":200,"score_detail":262,"sources":265,"tags":267,"primary_topic":271,"secondary_topics":272,"classification":279,"search_phrases":296,"slug":299,"view_count":22,"doi":300,"paper":301,"created_at":310},3879,"Utilization and Effects of Farm Management Data and Smart Agricultural Machinery on Labor Time in Large-Scale Paddy Farming","https:\u002F\u002Fdoi.org\u002F10.3173\u002Fair.35.123","Utilization and Effects of Farm Management Data and Smart Agricultural Machinery on Labor Time in Large-Scale Paddy Farming。Agricultural Information Research","Agricultural Information Research","2026-09-30T00:00:00Z",{"impact":17,"substance":49,"depth":263,"authority":202,"freshness":116,"relevant":22,"comment":264},16,"核心期刊论文，聚焦大规模水稻种植中农场管理数据与智能农机对劳动时间的影响，方法数据扎实但属细分领域研究，公共影响有限。",[266],{"name":260,"url":258},[121,268,32,269,270],"农业信息化","农机作业","农场管理数据",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[273],{"code":274,"slug":275,"name":276,"path":277,"parent_code":278},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"status":43,"locked":14,"version":44,"method":45,"evidence":280},{"candidates":281,"agricultural_evidence":50},[282,286,292],{"code":36,"score":17,"supported":50,"title":283,"abstract":284,"tags":285},[150],[150],[32],{"code":274,"score":287,"supported":50,"title":288,"abstract":290,"tags":291},11,[289],"agricultural machinery",[289],[],{"code":160,"score":169,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[268,121],[297,298],"Agricultural Information Research 水稻 智能农机","农场管理数据 农业信息化 农机作业 智慧农业","AgriculturalInformationResearch水稻智能农机-3879","10.3173\u002Fair.35.123",{"doi":300,"openalex_id":302,"authors":303,"venue":260,"cited_by_count":84,"oa_url":258,"card":9,"direction":190,"ingested_from":100},"W7214913919",[304,306,308],{"name":305,"orcid":9},"Yukari SHIMIZU",{"name":307,"orcid":9},"Tetsuya Ishikawa",{"name":309,"orcid":9},"Satoshi Yoshinaga","2026-10-01T23:30:09.347033Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":318,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":200,"score_detail":319,"sources":322,"tags":324,"primary_topic":328,"secondary_topics":329,"classification":340,"search_phrases":372,"slug":375,"view_count":169,"doi":376,"paper":377,"created_at":392},3825,"Hybrid Rule-Based and Machine Learning Decision-Support Framework for Smart Paddy Farming","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fatrsj.v6i1.70","Paddy farming decisions are affected by changing weather conditions, water availability, soil conditions, and crop health, creating a need for decision-support approaches that combine agronomic knowledge with data-driven prediction. This research presents a hybrid rule-based and machine-learning decision-support solution for paddy management that combines interpretable agronomic rules and lightweight predictive models. The temporal alignment, interpolation, normalization, feature engineering, and crop-stage aggregation of weather and NDVI data were integrated and preprocessed. It is a rule-based engine with 63 expert rules that was fused with Decision Tree, Logistic Regression, and Random Forest models using a weighted decision-fusion mechanism. The hybrid model performed with 90% accuracy, 0.91 precision, 0.88 recall, and 0.89 F1-score, while the rule-based model had 72% accuracy, and the best standalone Random Forest model had 86% accuracy. The hybrid model also yielded the minimum RMSE of 7.6. The results of the simulation scenarios showed increases in yield of 3–17% and savings in irrigation-water use of 12.4%. There were 10 farmers and 2 agricultural officers who participated in the preliminary usability test and were scored from 4.0 to 4.8 out of 5. The key is an adaptive machine-learning prediction integrated with clear agronomic logic, in a lightweight advisory system. The yields and water-saving benefits, however, are simulated, and experimental testing on a wider range of locations and seasons will be needed to determine the full agronomic benefits.","水稻种植决策受到天气条件变化、水资源可用性、土壤条件和作物健康状况的影响，因此需要将农艺知识与数据驱动预测相结合的决策支持方法。本研究提出了一种基于规则与机器学习混合的水稻管理决策支持方案，将可解释的农艺规则与轻量级预测模型相结合。研究对天气和NDVI数据进行了时间对齐、插值、归一化、特征工程和作物阶段聚合的集成与预处理。构建了一个包含63条专家规则的基于规则的引擎，并通过加权决策融合机制将其与决策树、逻辑回归和随机森林模型相融合。混合模型的准确率为90%，精确率为0.91，召回率为0.88，F1分数为0.89，而基于规则的模型准确率为72%，最佳独立随机森林模型准确率为86%。混合模型还取得了最低的RMSE值7.6。模拟情景结果显示，产量提高了3%–17%，灌溉用水节省了12.4%。共有10名农民和2名农业官员参与了初步可用性测试，评分为4.0至4.8分（满分5分）。其核心在于将自适应机器学习预测与清晰的农艺逻辑相结合，构建一个轻量级咨询系统。然而，产量提升和节水效益均为模拟结果，需要在更广泛的地点和季节进行实验测试，以确定其完整的农艺效益。","Journal of Agro-Technology and Rural Sciences","2026-09-29T00:00:00Z",{"impact":17,"substance":320,"depth":19,"authority":112,"freshness":116,"relevant":22,"comment":321},20,"提出规则与机器学习融合的水稻决策支持框架，指标与仿真结果具体，但验证样本小、增产节水为模拟值，属细分领域方法学进展。",[323],{"name":317,"url":314},[121,325,326,32,327],"农业人工智能","遥感监测","灌溉节水",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[330,335],{"code":331,"slug":332,"name":333,"path":334,"parent_code":132},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":336,"slug":337,"name":338,"path":339,"parent_code":138},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":43,"locked":14,"version":44,"method":45,"evidence":341},{"candidates":342,"agricultural_evidence":50},[343,347,353,359,363,367],{"code":36,"score":115,"supported":50,"title":344,"abstract":345,"tags":346},[150],[38,150],[32],{"code":331,"score":17,"supported":50,"title":348,"abstract":350,"tags":352},[349],"machine learning",[351],"机器学习",[325],{"code":336,"score":116,"supported":50,"title":354,"abstract":355,"tags":358},[],[356,357,337],"灌溉","水资源",[],{"code":134,"score":20,"supported":50,"title":360,"abstract":361,"tags":362},[],[157,135],[],{"code":160,"score":22,"supported":14,"title":364,"abstract":365,"tags":366},[],[],[121],{"code":368,"score":22,"supported":14,"title":369,"abstract":370,"tags":371},"I3",[],[],[326],[373,374],"水稻 决策支持 机器学习","NDVI 水稻 灌溉","水稻决策支持机器学习-3825","10.4038\u002Fatrsj.v6i1.70",{"doi":376,"openalex_id":378,"authors":379,"venue":317,"cited_by_count":84,"oa_url":385,"card":386,"direction":190,"ingested_from":100},"W7214735276",[380,383],{"name":381,"orcid":382},"Sawjanya Sathyaseelan","https:\u002F\u002Forcid.org\u002F0009-0003-1194-527X",{"name":384,"orcid":9},"K. Sriram","https:\u002F\u002Fatrsj.sljol.info\u002Farticles\u002F70\u002Ffiles\u002F6abb528a0792a.pdf",{"tldr":387,"method":388,"finding":389,"direction":390,"opportunity":391},"提出规则与机器学习混合的水稻决策支持框架，提升决策精度并节水增产。","融合63条专家规则与决策树、逻辑回归、随机森林，处理天气和NDVI数据。","混合模型准确率90%，模拟增产3-17%，节水12.4%，可用性评分4.0-4.8。","农业人工智能与决策模型","当前增产节水为模拟结果，未来需在多地点多季节开展田间试验验证农艺效益。","2026-09-30T23:30:09.772909Z",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":9,"content":9,"source_name":398,"source_url":396,"published_at":399,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":405,"primary_topic":408,"secondary_topics":409,"classification":415,"search_phrases":435,"slug":438,"view_count":169,"doi":439,"paper":440,"created_at":458},3500,"Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10333-026-01102-6","Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam。Paddy and Water Environment","Paddy and Water Environment","2026-09-24T00:00:00Z",67,{"impact":17,"substance":49,"depth":263,"authority":202,"freshness":21,"relevant":22,"comment":402},"核心期刊论文，遥感驱动土壤肥力建模与水稻适宜性制图，方法有参考价值但属区域案例，未达每日精选门槛。",[404],{"name":398,"url":396},[121,268,406,407,32],"遥感","土壤肥力",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[410,411],{"code":134,"slug":135,"name":136,"path":137,"parent_code":138},{"code":368,"slug":412,"name":413,"path":414,"parent_code":132},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":43,"locked":14,"version":44,"method":45,"evidence":416},{"candidates":417,"agricultural_evidence":50},[418,422,426,431],{"code":36,"score":115,"supported":50,"title":419,"abstract":420,"tags":421},[37],[37,150],[32],{"code":134,"score":17,"supported":50,"title":423,"abstract":424,"tags":425},[135],[135],[407],{"code":368,"score":17,"supported":50,"title":427,"abstract":429,"tags":430},[428],"remote sensing",[428],[406],{"code":160,"score":169,"supported":14,"title":432,"abstract":433,"tags":434},[],[],[268,121],[436,437],"Assam 洪泛平原 水稻 遥感","土壤肥力 遥感 建模","Assam洪泛平原水稻遥感-3500","10.1007\u002Fs10333-026-01102-6",{"doi":439,"openalex_id":441,"authors":442,"venue":398,"cited_by_count":84,"oa_url":9,"card":9,"direction":457,"ingested_from":100},"W7214202229",[443,445,448,450,453,455],{"name":444,"orcid":9},"Pompy Deka",{"name":446,"orcid":447},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":449,"orcid":9},"Mrinal Choudhury",{"name":451,"orcid":452},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":454,"orcid":9},"Britan Rahman",{"name":456,"orcid":9},"Ranjit Sarma","农业遥感与作物表型","2026-09-25T23:30:30.923946Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":112,"is_selected":14,"score":467,"score_detail":468,"sources":471,"tags":473,"primary_topic":476,"secondary_topics":477,"classification":479,"search_phrases":494,"slug":497,"view_count":498,"doi":499,"paper":500,"created_at":522},3186,"Cross-Regional Classification of Rice Cropping Systems Based on Within-Year Seasonal Composition Using Multimodal Remote-Sensing Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183241","Rice cropping-intensity products indicate the number of rice-growing seasons within a year but cannot reveal the seasonal compositions that define rice cropping systems. This limitation constrains detailed characterization of regional rice production patterns. To address this gap, this study developed a framework for rice cropping-system classification based on combinations of early-, middle-, and late-season rice cultivation. The framework integrates SAR-assisted reconstruction of cloud-contaminated Sentinel-2 time series using Sentinel-1 observations, Phenology-Constrained Time-Series Splicing (PTS) to construct PTS samples for underrepresented rice cropping-system classes, and an Ordered-Cycle Query Transformer (OCQT), which encodes annual-global and chronologically ordered-cycle representations with learnable season-query tokens for eight-class classification. On the spatial-block validation set, OCQT trained using real samples together with PTS samples achieved an overall accuracy of 0.9040, a macro-F1 of 0.8643, and a region-balanced macro-F1 of 0.8275, outperforming OCQT trained using only real samples. Across all four independent regional test sets, this configuration increased weighted-F1 by an average of 3.98 percentage points, with a maximum gain of 9.88 percentage points. This study advances rice monitoring from cropping intensity to explicit characterization of within-year seasonal composition and provides a framework for detailed agricultural monitoring using multi-source remote sensing and ordered-cycle representation learning.","水稻种植强度产品能够指示一年内的水稻种植季数，但无法揭示界定水稻种植系统的季节组成。这一局限制约了对区域水稻生产格局的精细刻画。为弥补这一不足，本研究构建了一个基于早稻、中稻和晚稻种植组合的水稻种植系统分类框架。该框架集成了以下三个部分：利用Sentinel-1观测对受云污染的Sentinel-2时间序列进行SAR辅助重建；采用物候约束时间序列拼接（Phenology-Constrained Time-Series Splicing, PTS）为样本不足的水稻种植系统类别构建PTS样本；以及有序循环查询Transformer（Ordered-Cycle Query Transformer, OCQT），该模型通过可学习的季节查询令牌编码年度全局表示和时间有序循环表示，以实现八类分类。在空间分块验证集上，使用真实样本与PTS样本共同训练的OCQT达到了0.9040的总体精度、0.8643的宏F1和0.8275的区域均衡宏F1，优于仅使用真实样本训练的OCQT。在全部四个独立区域测试集上，该配置将加权F1平均提升了3.98个百分点，最大提升达9.88个百分点。本研究推动了水稻监测从种植强度向年内季节组成的显式刻画迈进，并为利用多源遥感和有序循环表示学习开展精细农业监测提供了框架。","Remote Sensing","2026-09-20T00:00:00Z",81,{"impact":49,"substance":469,"depth":49,"authority":154,"freshness":116,"relevant":22,"comment":470},22,"提出融合SAR与光学时序及有序周期Transformer的水稻种植制度分类框架，方法新颖、精度可靠，对农业遥感监测有实质参考价值。",[472],{"name":465,"url":462},[121,326,32,474,475],"作物分类","多源遥感",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[478],{"code":368,"slug":412,"name":413,"path":414,"parent_code":132},{"status":43,"locked":14,"version":44,"method":45,"evidence":480},{"candidates":481,"agricultural_evidence":50},[482,486,490],{"code":36,"score":115,"supported":50,"title":483,"abstract":484,"tags":485},[37],[38,37],[32],{"code":368,"score":116,"supported":50,"title":487,"abstract":488,"tags":489},[],[406,428],[474,475,326],{"code":160,"score":22,"supported":14,"title":491,"abstract":492,"tags":493},[],[],[121],[495,496],"Sentinel-1 Sentinel-2 水稻","水稻种植制度 遥感分类","Sentinel-1Sentinel-2水稻-3186",5,"10.3390\u002Frs18183241",{"doi":499,"openalex_id":501,"authors":502,"venue":465,"cited_by_count":84,"oa_url":462,"card":517,"direction":457,"ingested_from":100},"W7213906853",[503,505,508,510,512,514],{"name":504,"orcid":9},"Jingrou Wang",{"name":506,"orcid":507},"Tingting Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3140-5882",{"name":509,"orcid":9},"Beibei Xue",{"name":511,"orcid":9},"Lu Xu",{"name":513,"orcid":9},"Yanyan Shi",{"name":515,"orcid":516},"Dongping Ming","https:\u002F\u002Forcid.org\u002F0000-0002-3422-7399",{"tldr":518,"method":519,"finding":520,"direction":457,"opportunity":521},"提出多模态遥感时序框架，实现跨区域水稻种植制度八分类。","Sentinel-1\u002F2融合重建、PTS样本拼接与OCQT有序周期Transfo","结合PTS样本的OCQT精度达0.904，跨区加权F1平均提升3.98个百分点。","可将有序周期表示学习扩展至其他多季作物制度识别与跨区域泛化研究。","2026-09-22T23:30:25.478404Z"]