[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3677":3,"related-3677":136},{"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":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":106,"slug":109,"view_count":89,"doi":110,"paper":111,"created_at":135},3677,"A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04697-2","Abstract Sustainable development has become a key priority for the agricultural sector, both globally and in Quebec. Digital agriculture, as an important component of sustainable agricultural systems, represents a promising pathway toward achieving sustainable development goals. However, despite the recognized benefits of digital agriculture, the opportunities, limitations, and research gaps associated with its contribution to sustainable development in Quebec remain insufficiently understood. This study applies a systematic tracking approach to identify applications of digital technologies in Quebec’s agricultural sector and evaluate their potential contributions to provincial sustainable development objectives while highlighting gaps and opportunities for their enhanced deployment. A total of 59 peer-reviewed publications and grey literature reports were identified and analyzed. Overall, 14 categories of digital technologies were documented within Quebec’s agricultural sector. Their representation in the scientific literature was primarily concentrated in three dominant categories: web\u002Fmobile applications and software (31%), machine learning and sensors (14%), and satellite technologies (13%). The identified digital technologies have significant potential to support sustainable development objectives in Quebec agriculture through improved pesticide monitoring, soil health assessment and conservation, fertilizer management optimization, water-use efficiency, and biodiversity conservation. Collectively, these technologies could be combined in 65 distinct ways to support sustainable development goals; however, only 54% of this potential is currently being exploited in Quebec. This highlights substantial opportunities for expanding the role of digital agriculture in addressing sustainability challenges. The study also reveals important knowledge gaps, particularly regarding the social dimensions influencing the diffusion and adoption of digital technologies in agriculture. A limited understanding of the factors affecting Quebec farmers’ adoption decisions remains a major barrier to maximizing the benefits of digital agriculture. Addressing these social and behavioral dimensions will be essential for accelerating the transition toward more sustainable, digitally enabled agricultural systems in Quebec.","摘要 可持续发展已成为全球及魁北克农业部门的重要优先事项。数字农业作为可持续农业系统的重要组成部分，是实现可持续发展目标的一条充满前景的路径。然而，尽管数字农业的益处已得到认可，但其对魁北克可持续发展所作贡献的机遇、局限及研究空白仍未被充分理解。本研究采用系统性追踪方法，识别数字技术在魁北克农业部门中的应用，评估其对省级可持续发展目标的潜在贡献，同时揭示其加强部署的空白与机遇。共识别并分析了59篇同行评审出版物和灰色文献报告。总体而言，魁北克农业部门中记录到14类数字技术。其在科学文献中的呈现主要集中在三个主导类别：网络\u002F移动应用程序与软件（31%）、机器学习与传感器（14%）以及卫星技术（13%）。所识别的数字技术通过改善农药监测、土壤健康评估与保护、肥料管理优化、水资源利用效率以及生物多样性保护，具有支持魁北克农业可持续发展目标的显著潜力。总体而言，这些技术可以65种不同方式组合以支持可持续发展目标；然而，魁北克目前仅开发利用了其中54%的潜力。这凸显了扩大数字农业在应对可持续性挑战方面作用的重大机遇。本研究还揭示了重要的知识空白，特别是影响数字技术在农业中扩散与采纳的社会维度方面。对影响魁北克农民采纳决策的因素了解有限，仍是最大化数字农业效益的主要障碍。解决这些社会与行为维度对于加速魁北克向更可持续、数字化赋能的农业系统转型至关重要。",null,"Discover Sustainability","2026-09-27T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"基于59篇文献的系统梳理，量化了魁北克数字农业技术类别与可持续目标支撑潜力，指出仅54%潜力被利用及农户采纳的社会维度研究缺口，对区域数字农业政策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业遥感","机器学习","农业可持续发展",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58,66,73,80,87,94,101],{"code":46,"score":17,"supported":47,"title":48,"abstract":50,"tags":51},"I1",true,[49],"digital agriculture",[27,49],[27,28],{"code":33,"score":53,"supported":47,"title":54,"abstract":55,"tags":57},7,[],[30,56],"machine learning",[30],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":65},"F3",6,[],[63,64],"肥料","fertilizer",[],{"code":67,"score":60,"supported":47,"title":68,"abstract":69,"tags":72},"F4",[],[70,71],"农药","pesticide",[],{"code":74,"score":60,"supported":47,"title":75,"abstract":76,"tags":79},"H2",[],[77,78],"土壤","soil",[],{"code":81,"score":60,"supported":47,"title":82,"abstract":83,"tags":86},"H6",[],[84,85],"生物多样性","biodiversity",[],{"code":88,"score":89,"supported":47,"title":90,"abstract":91,"tags":93},"H8",4,[],[92],"可持续农业",[31],{"code":95,"score":96,"supported":47,"title":97,"abstract":98,"tags":100},"H3",3,[],[99],"水资源",[],{"code":102,"score":22,"supported":14,"title":103,"abstract":104,"tags":105},"I3",[],[],[29],[107,108],"魁北克 数字农业","魁北克 农业技术 应用","魁北克数字农业-3677","10.1007\u002Fs43621-026-04697-2",{"doi":110,"openalex_id":112,"authors":113,"venue":10,"cited_by_count":127,"oa_url":6,"card":128,"direction":132,"ingested_from":134},"W7214562996",[114,116,119,122,124],{"name":115,"orcid":9},"Sambiani D. Y. Tindjiete",{"name":117,"orcid":118},"Terence Épule Épule","https:\u002F\u002Forcid.org\u002F0000-0002-5756-382X",{"name":120,"orcid":121},"Daniel Etongo","https:\u002F\u002Forcid.org\u002F0000-0002-8237-0843",{"name":123,"orcid":9},"Changhui Peng",{"name":125,"orcid":126},"Paul Célicourt","https:\u002F\u002Forcid.org\u002F0000-0001-9297-6593",0,{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"系统盘点魁北克数字农业技术应用及其对可持续发展的贡献与知识空白。","系统追踪59篇同行评议与灰色文献，归类14类数字技术并评估组合潜力。","技术可65种组合支撑可持续发展目标，但仅54%潜力被利用，社会采纳因素研究不足。","数字乡村与农业信息化","可深入研究农户数字技术采纳的社会行为因素，填补魁北克及类似地区扩散机制的研究空白。","openalex","2026-09-28T23:30:36.987716Z",{"total":60,"page":22,"page_size":60,"items":137},[138,219,317,416,491,568],{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":144,"source_url":141,"published_at":11,"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":165,"search_phrases":198,"slug":201,"view_count":202,"doi":203,"paper":204,"created_at":218},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere",73,{"impact":147,"substance":18,"depth":19,"authority":20,"freshness":148,"relevant":22,"comment":149},12,8,"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[151],{"name":144,"url":141},[28,29,30,153,154],"土壤墒情","蒸散发估算",{"code":95,"slug":156,"name":157,"path":158,"parent_code":159},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[161,164],{"code":74,"slug":78,"name":162,"path":163,"parent_code":159},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":166},{"candidates":167,"agricultural_evidence":47},[168,175,180,184,190,194],{"code":95,"score":169,"supported":47,"title":170,"abstract":172,"tags":174},17,[171],"evapotranspiration",[99,173,171],"蒸散发",[],{"code":74,"score":176,"supported":47,"title":177,"abstract":178,"tags":179},15,[78],[77,78],[153],{"code":33,"score":21,"supported":47,"title":181,"abstract":182,"tags":183},[56],[],[30],{"code":185,"score":96,"supported":47,"title":186,"abstract":187,"tags":189},"J1",[],[188],"农业规划",[],{"code":46,"score":22,"supported":14,"title":191,"abstract":192,"tags":193},[],[],[28],{"code":102,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},[],[],[29],[199,200],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804",2,"10.3390\u002Fatmos17100941",{"doi":203,"openalex_id":205,"authors":206,"venue":144,"cited_by_count":127,"oa_url":141,"card":212,"direction":216,"ingested_from":134},"W7214588993",[207,210],{"name":208,"orcid":209},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":211,"orcid":9},"Devrim Alkaya",{"tldr":213,"method":214,"finding":215,"direction":216,"opportunity":217},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","农业人工智能与决策模型","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z",{"id":220,"title":221,"url":222,"summary":223,"summary_zh":224,"content":9,"source_name":225,"source_url":222,"published_at":226,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":227,"score_detail":228,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":240,"classification":246,"search_phrases":269,"slug":272,"view_count":148,"doi":273,"paper":274,"created_at":316},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":148,"substance":19,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":229},"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[231],{"name":225,"url":222},[28,29,233,30,234],"水稻","高光谱遥感",{"code":236,"slug":237,"name":233,"path":238,"parent_code":239},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[241,245],{"code":102,"slug":242,"name":243,"path":244,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":247},{"candidates":248,"agricultural_evidence":47},[249,257,261,265],{"code":102,"score":250,"supported":47,"title":251,"abstract":254,"tags":256},27,[252,253],"remote sensing","hyperspectral",[255,252,253],"遥感",[29,234],{"code":33,"score":176,"supported":47,"title":258,"abstract":259,"tags":260},[56],[30,56],[30],{"code":236,"score":147,"supported":47,"title":262,"abstract":263,"tags":264},[237],[237],[233],{"code":46,"score":22,"supported":14,"title":266,"abstract":267,"tags":268},[],[],[28],[270,271],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":273,"openalex_id":275,"authors":276,"venue":225,"cited_by_count":127,"oa_url":9,"card":310,"direction":314,"ingested_from":134},"W7214027889",[277,280,283,286,289,292,294,296,299,302,304,306,308],{"name":278,"orcid":279},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":281,"orcid":282},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":284,"orcid":285},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":287,"orcid":288},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":290,"orcid":291},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":293,"orcid":9},"Ying Luo",{"name":295,"orcid":9},"Jiaqian Zhang",{"name":297,"orcid":298},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":300,"orcid":301},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":303,"orcid":9},"Chaoliang Peng",{"name":305,"orcid":9},"Duan Tian",{"name":307,"orcid":9},"Weihao Wang",{"name":309,"orcid":9},"Jiaxin Liu",{"tldr":311,"method":312,"finding":313,"direction":314,"opportunity":315},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","农业遥感与作物表型","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":333,"secondary_topics":339,"classification":346,"search_phrases":399,"slug":402,"view_count":202,"doi":403,"paper":404,"created_at":415},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",72,{"impact":19,"substance":18,"depth":19,"authority":147,"freshness":202,"relevant":22,"comment":327},"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[329],{"name":323,"url":320},[28,331,29,30,332],"精准施肥","小农户",{"code":334,"slug":335,"name":336,"path":337,"parent_code":338},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[340,345],{"code":59,"slug":341,"name":342,"path":343,"parent_code":344},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":347},{"candidates":348,"agricultural_evidence":47},[349,354,360,365,371,375,382,386,393],{"code":334,"score":176,"supported":47,"title":350,"abstract":352,"tags":353},[351],"smallholder",[332,351],[332],{"code":59,"score":13,"supported":47,"title":355,"abstract":356,"tags":359},[],[357,358,64],"施肥","养分管理",[331],{"code":33,"score":13,"supported":47,"title":361,"abstract":362,"tags":364},[],[30,56,363],"decision support",[30],{"code":46,"score":53,"supported":47,"title":366,"abstract":367,"tags":370},[],[368,369],"精准农业","precision agriculture",[28],{"code":102,"score":53,"supported":47,"title":372,"abstract":373,"tags":374},[],[255,252],[29],{"code":376,"score":60,"supported":47,"title":377,"abstract":378,"tags":381},"B7",[],[379,380],"温室","greenhouse",[],{"code":74,"score":60,"supported":47,"title":383,"abstract":384,"tags":385},[],[77,78],[],{"code":387,"score":60,"supported":47,"title":388,"abstract":389,"tags":392},"I5",[],[390,391],"物联网","IoT",[],{"code":394,"score":96,"supported":47,"title":395,"abstract":396,"tags":398},"H4",[],[397],"carbon sequestration",[],[400,401],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":403,"openalex_id":405,"authors":406,"venue":323,"cited_by_count":127,"oa_url":320,"card":409,"direction":413,"ingested_from":134},"W7211915396",[407],{"name":408,"orcid":9},"Anuga Liyanage",{"tldr":410,"method":411,"finding":412,"direction":413,"opportunity":414},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","智慧农业 \u002F 农业物联网","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":422,"source_url":419,"published_at":423,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":424,"score_detail":425,"sources":428,"tags":430,"primary_topic":433,"secondary_topics":434,"classification":436,"search_phrases":461,"slug":464,"view_count":22,"doi":465,"paper":466,"created_at":490},1532,"Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727176","Crop residue cover (CRC) is a critical parameter for evaluating conservation tillage practices, mitigating soil erosion, and developing models for global carbon cycle monitoring. Traditional optical remote sensing index-based methods are significantly affected by variations in soil and crop residue moisture content as well as interference from emerging subsequent crops, resulting in low estimation accuracy and poor stability of CRC. Due to the presence of subsequent crops, the non-photosynthetic vegetation fraction (fNPV) derived from linear spectral mixture analysis is typically lower than the actual field CRC. This study proposes a novel CRC estimation method based on linear spectral mixture analysis. The workflow consists of three main components: (1) development of a Crop Residue Random Forest (CRRF) index using a random forest regression model to simulate the narrowband SINDRI index – which is less sensitive to moisture – from broadband multispectral reflectance; (2) construction of a two-dimensional triangular feature space combining the CRRF and NDVI indices, followed by linear spectral mixture analysis to simultaneously estimate field fNPV, photosynthetic vegetation fraction (fPV), and bare soil fraction (fBS); and (3) retrieval of the true CRC fraction at the sowing stage using the formula CRC = fNPV \u002F (1 − fPV). The proposed method was validated and applied regionally using multi-temporal Sentinel-2 MSI and MODIS imagery. Results demonstrate that: (1) the method effectively mitigates moisture interference and achieves high-accuracy fNPV estimation (R2 = 0.84, RMSE = 0.10); (2) the integration of Sentinel-2 and MODIS sensors enables stable estimation and spatio-temporal dynamic analysis of CRC across the North China Plain, with high temporal consistency between images acquired approximately 16 days apart (the majority of agricultural pixels showing absolute differences within ±0.15). This approach provides an efficient and robust technical solution for remote sensing monitoring of CRC fraction in complex agricultural environments and offers a valuable methodological reference for the assessment of conservation tillage practices.","作物残茬覆盖度（CRC）是评估保护性耕作措施、减缓土壤侵蚀以及构建全球碳循环监测模型的关键参数。传统光学遥感指数方法受土壤和作物残茬含水量变化及新生后茬作物干扰的影响显著，导致CRC估算精度低、稳定性差。由于后茬作物的存在，基于线性光谱混合分析提取的非光合植被覆盖度（fNPV）通常低于田间实际CRC值。本研究提出了一种基于线性光谱混合分析的CRC估算新方法。该方法流程主要包括三个部分：（1）利用随机森林回归模型模拟对水分敏感性较低的窄带SINDRI指数，进而构建作物残茬随机森林（CRRF）指数，该指数可由宽带多光谱反射率计算得到；（2）结合CRRF与NDVI指数构建二维三角特征空间，通过线性光谱混合分析同时估算田间fNPV、光合植被覆盖度（fPV）和裸土覆盖度（fBS）；（3）利用公式CRC = fNPV \u002F (1 − fPV)反演播种期真实CRC值。该方法利用多时相Sentinel-2 MSI和MODIS影像进行了验证及区域应用。结果表明：（1）该方法能有效削弱水分干扰，实现高精度fNPV估算（R² = 0.84，RMSE = 0.10）；（2）Sentinel-2与MODIS传感器的联合应用可实现华北平原CRC的稳定估算及时空动态分析，且间隔约16天获取的影像间具有较高时间一致性（绝大多数农业像元的绝对差异在±0.15以内）。该方法为复杂农业环境下CRC覆盖度的遥感监测提供了高效、稳健的技术方案，也为保护性耕作措施评估提供了有价值的方法参考。","International Journal of Remote Sensing","2026-09-02T00:00:00Z",76,{"impact":19,"substance":426,"depth":19,"authority":20,"freshness":53,"relevant":22,"comment":427},20,"提出基于随机森林与线性光谱混合分析的秸秆覆盖度估算新方法，在华北平原多源遥感数据上验证有效，对保护性耕作监测有方法论参考价值。",[429],{"name":422,"url":419},[28,29,30,431,432],"保护性耕作","秸秆覆盖",{"code":102,"slug":242,"name":243,"path":244,"parent_code":37},[435],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":437},{"candidates":438,"agricultural_evidence":47},[439,445,449,453,457],{"code":102,"score":440,"supported":47,"title":441,"abstract":443,"tags":444},26,[252,442],"multispectral",[255,252,442],[29],{"code":33,"score":21,"supported":47,"title":446,"abstract":447,"tags":448},[56],[],[30],{"code":74,"score":60,"supported":47,"title":450,"abstract":451,"tags":452},[],[77,78],[],{"code":88,"score":89,"supported":47,"title":454,"abstract":455,"tags":456},[],[431],[431],{"code":46,"score":22,"supported":14,"title":458,"abstract":459,"tags":460},[],[],[28],[462,463],"保护性耕作 农业遥感 智慧农业 机器学习","保护性耕作 农业遥感","保护性耕作农业遥感智慧农业机器学习-1532","10.1080\u002F01431161.2026.2727176",{"doi":465,"openalex_id":467,"authors":468,"venue":422,"cited_by_count":127,"oa_url":9,"card":485,"direction":314,"ingested_from":134},"W7205007595",[469,472,475,477,480,483],{"name":470,"orcid":471},"Jibo Yue","https:\u002F\u002Forcid.org\u002F0000-0001-9766-5313",{"name":473,"orcid":474},"Ranran Yang","https:\u002F\u002Forcid.org\u002F0000-0003-1937-2042",{"name":476,"orcid":9},"Yinghao Lin",{"name":478,"orcid":479},"Nianxu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-0152-8750",{"name":481,"orcid":482},"Qingjiu Tian","https:\u002F\u002Forcid.org\u002F0000-0003-0986-6479",{"name":484,"orcid":9},"Jia Tian",{"tldr":486,"method":487,"finding":488,"direction":314,"opportunity":489},"提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。","随机森林模拟窄带SINDRI，结合NDVI构建三角特征空间，线性光谱混合分析估算","方法有效缓解水分干扰，fNPV估算R²=0.84，RMSE=0.10，多源影像时间一致性高。","可探索将方法扩展至其他作物类型或区域，或结合高光谱影像提高精度，并研究残茬覆盖度与土壤碳动态的关系。","2026-09-03T23:30:30.109393Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":497,"source_url":494,"published_at":498,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":499,"score_detail":500,"sources":503,"tags":505,"primary_topic":508,"secondary_topics":513,"classification":516,"search_phrases":545,"slug":548,"view_count":127,"doi":549,"paper":550,"created_at":567},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":17,"substance":501,"depth":169,"authority":20,"freshness":13,"relevant":22,"comment":502},21,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[504],{"name":497,"url":494},[28,30,506,153,507],"遥感监测","冬小麦",{"code":509,"slug":510,"name":511,"path":512,"parent_code":239},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[514,515],{"code":74,"slug":78,"name":162,"path":163,"parent_code":159},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":517},{"candidates":518,"agricultural_evidence":47},[519,524,528,532,536,541],{"code":509,"score":440,"supported":47,"title":520,"abstract":522,"tags":523},[521,510],"winter wheat",[511,521,510],[507],{"code":74,"score":176,"supported":47,"title":525,"abstract":526,"tags":527},[78],[77,78],[153],{"code":33,"score":176,"supported":47,"title":529,"abstract":530,"tags":531},[56],[30,56],[30],{"code":102,"score":13,"supported":47,"title":533,"abstract":534,"tags":535},[],[255,252,442],[506],{"code":95,"score":60,"supported":47,"title":537,"abstract":538,"tags":540},[],[539,156],"灌溉",[],{"code":46,"score":89,"supported":47,"title":542,"abstract":543,"tags":544},[],[28],[28],[546,547],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":549,"openalex_id":551,"authors":552,"venue":497,"cited_by_count":127,"oa_url":494,"card":562,"direction":413,"ingested_from":134},"W7223156204",[553,556,558,560],{"name":554,"orcid":555},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":557,"orcid":9},"Yi Zhang",{"name":559,"orcid":9},"WEI Lei",{"name":561,"orcid":9},"Chengcai Zhang",{"tldr":563,"method":564,"finding":565,"direction":314,"opportunity":566},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":569,"title":570,"url":571,"summary":572,"summary_zh":9,"content":9,"source_name":573,"source_url":9,"published_at":574,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":575,"score_detail":576,"sources":578,"tags":580,"primary_topic":583,"secondary_topics":584,"classification":585,"search_phrases":606,"slug":609,"view_count":127,"doi":610,"paper":611,"created_at":619},5529,"数字农业技术采纳的驱动因素与障碍：基于复杂网络与演化博弈的分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Ffull","文章从演化博弈视角构建数字农业技术采纳的驱动与阻碍机制框架，系统梳理影响农户采纳数字农业技术的关键因素与障碍路径。研究强调通用大模型难以直接满足专业知识、结果依据与溯源能力要求，为政策设计提供了技术采纳侧的理论依据。","Frontiers in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z",67,{"impact":147,"substance":19,"depth":17,"authority":20,"freshness":148,"relevant":22,"comment":577},"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[579],{"name":573,"url":571},[27,28,581,582],"技术采纳","农户行为",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":586},{"candidates":587,"agricultural_evidence":47},[588,592,597,602],{"code":46,"score":20,"supported":47,"title":589,"abstract":590,"tags":591},[27],[27],[27,28],{"code":33,"score":96,"supported":47,"title":593,"abstract":594,"tags":596},[],[595],"大模型",[],{"code":598,"score":22,"supported":14,"title":599,"abstract":600,"tags":601},"G1",[],[],[581],{"code":334,"score":22,"supported":14,"title":603,"abstract":604,"tags":605},[],[],[582],[607,608],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529","10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":610,"openalex_id":9,"authors":612,"venue":9,"cited_by_count":127,"oa_url":9,"card":613,"direction":132,"ingested_from":618},[],{"tldr":614,"method":615,"finding":616,"direction":132,"opportunity":617},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","agent","2026-10-22T00:07:09.498742Z"]