[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5144":3,"related-5144":78},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":77},5144,"Agricultural production services and farmland reallocation among wheat-growing households in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968687","Introduction Agricultural production services may influence farmland allocation, but their associations with farm expansion and land retention may differ. This study examines how service use relates to operated farmland area and land rented in and out among wheat-growing households in four Chinese provinces. Methods Using cross-sectional survey data, we measure service use by expenditure and, alternatively, by the number of production stages served, which is less mechanically related to cultivated area. We also use two-part models to distinguish rental participation from rented-in area conditional on participation. Fixed-effects estimates from a separate three-wave rural panel provide a complementary longitudinal check. Results Service use is positively associated with operated area and rented-in area and negatively associated with rented-out area, although the strength of the rented-out association varies by measure and model specification. Service-stage breadth is associated with participation in renting in land, but not significantly with conditional rented-in area. In the longitudinal panel, wheat service expenditure per mu is positively associated with operated area and wheat-growing area. Discussion These findings are consistent with agricultural production services being linked to farmland reallocation through both expansion and land-retention margins. Proposed labor-substitution and expected-return mechanisms remain correlational. Because the main survey is cross-sectional and limited to active wheat growers, the findings should not be generalized beyond comparable wheat-producing settings.","引言 农业生产服务可能影响农地配置，但其与农场扩张和土地保留的关联可能有所不同。本研究考察在中国四省小麦种植户中，服务使用与经营农地面积及土地租入租出的关系。方法 利用横截面调查数据，我们以支出衡量服务使用，并以所服务生产环节数量作为替代衡量方式，后者与耕地面积的相关性较少出于机械性关联。我们还使用两部分模型，将租入参与与参与条件下的租入面积区分开来。来自一个独立的三期农村面板数据的固定效应估计提供了补充性的纵向验证。结果 服务使用与经营面积和租入面积呈正相关，与租出面积呈负相关，尽管租出关联的强度因衡量方式和模型设定而异。服务环节广度与租入土地的参与相关，但与条件租入面积无显著相关。在纵向面板中，每亩小麦服务支出与经营面积和小麦种植面积呈正相关。讨论 这些发现与农业生产服务通过扩张和土地保留边际与农地再配置相关联的观点一致。所提出的劳动替代和预期回报机制仍属相关性关系。由于主要调查为横截面数据且仅限于活跃的小麦种植者，研究结果不应推广至可比小麦生产环境之外。",null,"Frontiers in Sustainable Food Systems","2026-10-16T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,9,1,"基于四省截面调查与三期面板数据，发现农业服务使用与麦农经营面积扩大、租入增加及租出减少相关，方法较严谨但属相关性证据，对农地流转与托管服务研究有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"农业社会化服务","小麦种植","农地流转","土地规模经营",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},14,true,[33],[34,33],[],{"code":51,"score":52,"supported":14,"title":53,"abstract":54,"tags":55},"G2",2,[],[],[27,29],[57,58],"农业生产服务 农地流转 小麦种植户","Frontiers Sustainable Food Systems 农地再配置","农业生产服务农地流转小麦种植户-5144",0,"10.3389\u002Ffsufs.2026.1968687",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":60,"oa_url":69,"card":70,"direction":74,"ingested_from":76},"W7222725123",[65,67],{"name":66,"orcid":9},"Yihang Jiang",{"name":68,"orcid":9},"Yue Sun","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1968687\u002Fpdf",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"研究农业生产服务使用与小麦种植户耕地经营规模及土地租入租出的关联。","四省截面调查数据，以服务支出和服务环节数度量，用两部模型与面板固定效应检验。","服务使用与经营面积和租入面积正相关、与租出面积负相关，环节广度影响租入参与。","数字乡村与农业信息化","可结合服务组织数字化数据，用面板或准实验方法识别服务使用对土地流转的因果机制。","openalex","2026-10-17T23:30:07.100958Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,142,200,269,362,424],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":9,"source_name":10,"source_url":84,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":89,"sources":92,"tags":94,"primary_topic":98,"secondary_topics":99,"classification":100,"search_phrases":121,"slug":124,"view_count":116,"doi":125,"paper":126,"created_at":141},3993,"Impact of cluster-farm participation on wheat efficiency and commercialization: practices from Northwest Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1847726","Introduction Cluster farming has increasingly been promoted as a collective agricultural approach to improve smallholder productivity, market integration, and rural livelihoods in Ethiopia. Using Collective Action Theory, this study examines the factors influencing farmers’ participation in wheat cluster farming and its impact on production efficiency, and household wheat commercialization. Methods A multi-stage sampling procedure was used to collect cross-sectional data from 384 wheat-producing households in Northwest Ethiopia. The association between cluster farming participation and wheat efficiency and commercialization level was estimated using an endogenous switching regression model. Results The probit results show that sex, family size, land size, livestock holding, cooperative membership, extension contact, cluster farming-specific training and awareness are positively associated with participation in cluster farming, while distance from the cluster farming site is negatively associated with participation. The ESR estimates further suggest that participation in cluster farming has a significant positive impact on production efficiency and commercialization. The estimated ATT shows that participation increases technical efficiency (0.383), allocative efficiency (0.252), economic efficiency (0.105), and commercialization level (0.252). Similarly, the ATU estimates indicate significant potential benefits for non-participating households if they participate, with increases of 0.398, 0.291, 0.152, and 0.274 in TE, AE, EE, and HCI, respectively. The robustness propensity score matching result also confirms the ESR findings. Conclusion The results imply that collective farming is associated with farm performance through better access to productive resources, improved institutional support, reduced transaction costs, and enhanced market integration. The results highlight the need for fostering inclusive cluster farming through specific extension services, support to cooperatives, and better access to cluster sites, which is associated with the efficiency and commercialization level of farmers.","引言 集群农业在埃塞俄比亚日益被推广为一种集体农业方式，旨在提高小农生产力、市场融入和农村生计。本研究运用集体行动理论，考察影响农户参与小麦集群农业的因素及其对生产效率、农户小麦商品化的影响。方法 采用多阶段抽样程序，从埃塞俄比亚西北部384个小麦种植户收集横截面数据。使用内生转换回归模型估计集群农业参与与小麦效率及商品化水平之间的关联。结果 Probit结果表明，性别、家庭规模、土地规模、牲畜存栏量、合作社成员身份、推广接触、集群农业专项培训及认知与参与集群农业呈正相关，而距集群农业站点的距离与参与呈负相关。ESR估计进一步表明，参与集群农业对生产效率和商品化具有显著正向影响。估计的ATT显示，参与使技术效率（0.383）、配置效率（0.252）、经济效率（0.105）和商品化水平（0.252）提高。同样，ATU估计表明，未参与农户若参与将获得显著潜在收益，其技术效率、配置效率、经济效率和户级商品化指数分别提高0.398、0.291、0.152和0.274。稳健性倾向得分匹配结果也证实了ESR的发现。结论 结果表明，集体农业通过更好地获取生产资源、改善制度支持、降低交易成本以及增强市场融入，与农场绩效相关联。结果凸显了通过专项推广服务、支持合作社以及改善集群站点可及性来促进包容性集群农业的必要性，这与农户的效率和商品化水平相关联。","2026-10-01T00:00:00Z",72,{"impact":90,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":91},12,"基于384户调查与内生转换回归的实证研究，方法规范、结论可靠，但属区域案例，对国内三农实践仅有间接参考价值。",[93],{"name":10,"url":84},[34,95,27,96,97],"小农户","埃塞俄比亚","集群农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":101},{"candidates":102,"agricultural_evidence":46},[103,108,114],{"code":32,"score":104,"supported":46,"title":105,"abstract":106,"tags":107},15,[33],[34,33],[34],{"code":51,"score":109,"supported":46,"title":110,"abstract":111,"tags":113},5,[],[112],"smallholder",[27,95],{"code":115,"score":116,"supported":46,"title":117,"abstract":118,"tags":120},"C",3,[],[119],"livestock",[],[122,123],"埃塞俄比亚 小麦 集群农业","小农户 商业化 生产效率","埃塞俄比亚小麦集群农业-3993","10.3389\u002Ffsufs.2026.1847726",{"doi":125,"openalex_id":127,"authors":128,"venue":10,"cited_by_count":60,"oa_url":84,"card":136,"direction":74,"ingested_from":76},"W7215048149",[129,131,134],{"name":130,"orcid":9},"Jianmin Cao",{"name":132,"orcid":133},"Gizachew Wosene","https:\u002F\u002Forcid.org\u002F0000-0003-2421-4655",{"name":135,"orcid":9},"Desalegn Wondim",{"tldr":137,"method":138,"finding":139,"direction":74,"opportunity":140},"基于埃塞俄比亚西北部384户小麦种植户数据，评估参与集群农业对小麦生产效率与商品化率的影响。","多阶段抽样调查，采用内生转换回归模型（ESR）和倾向得分匹配（PSM）进行实证分","参与集群农业显著提升技术效率（0.383）、配置效率、经济效率及商品化水平，未参与者若参与也有潜在收","可探索数字技术（如移动端培训、合作社信息平台）如何降低集群参与门槛并放大效率与商品化收益。","2026-10-02T23:30:09.685377Z",{"id":143,"title":144,"url":145,"summary":146,"summary_zh":147,"content":9,"source_name":10,"source_url":145,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":149,"score_detail":150,"sources":154,"tags":156,"primary_topic":161,"secondary_topics":162,"classification":163,"search_phrases":176,"slug":179,"view_count":171,"doi":180,"paper":181,"created_at":199},3659,"The dual effects of agricultural production service outsourcing on farmers' farmland transfer willingness: implications for rural household welfare in major wheat-producing areas of Henan, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1903026","Rural land use decisions and agricultural services are closely associated with rural household income, labor status and overall wellbeing, which are fundamental determinants of rural household welfare and livelihood sustainability. Based on 1,529 household-level datasets collected from four counties in major wheat-producing regions of China, the impact of agricultural production service outsourcing on farmers' willingness to transfer farmland is empirically analyzed using the ordered Probit and mediation effect models. The principal research findings are that this outsourcing significantly promotes farmers’ willingness to transfer in farmland, while it inhibits their willingness to transfer out farmland. Moreover, the land-stabilizing effect that inhibits farmland transfer-out is stronger than the land-expanding effect that encourages farmland transfer-in. Heterogeneity analysis demonstrates that those managing large-scale farms tend to be motivated to transfer-in farmland, while the transfer-out behavior of farmers with small-scale holdings is significantly inhibited. Furthermore, the decision-making response of female household heads is significantly stronger than that of their male counterparts. Mediation mechanism analysis indicates that agricultural income growth and labor allocation optimization play partial mediating roles, while the transmission contribution of the income effect is greater than that of the labor allocation effect. This study reveals the asymmetric impact of service outsourcing on farmland transfer willingness and the dominance of the income effect.","农村土地利用决策与农业服务紧密关联着农村家庭收入、劳动状况及整体福祉，这些是决定农村家庭福利与生计可持续性的基本要素。基于从中国主要小麦产区的四个县收集的1529份农户层面数据，运用有序Probit模型和中介效应模型，实证分析了农业生产服务外包对农户土地流转意愿的影响。主要研究发现，农业生产服务外包显著促进了农户的土地转入意愿，同时抑制了其土地转出意愿。此外，抑制土地转出的土地稳定效应强于鼓励土地转入的土地扩张效应。异质性分析表明，经营大规模农场的农户倾向于被激励转入土地，而小规模经营农户的土地转出行为则受到显著抑制。此外，女性户主的决策反应显著强于男性户主。中介机制分析表明，农业收入增长和劳动力配置优化发挥了部分中介作用，其中收入效应的传导贡献大于劳动力配置效应。本研究揭示了服务外包对土地流转意愿的非对称影响以及收入效应的主导地位。","2026-09-28T00:00:00Z",79,{"impact":17,"substance":151,"depth":152,"authority":20,"freshness":13,"relevant":22,"comment":153},22,18,"基于河南四县1529户微观数据的实证研究，揭示服务外包对农地流转意愿的非对称影响，方法规范、结论有政策参考价值，但属学术论文，公共传播性有限。",[155],{"name":10,"url":145},[157,29,158,159,160],"河南","农业生产托管","农户福祉","小麦主产区",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":164},{"candidates":165,"agricultural_evidence":46},[166,170],{"code":32,"score":45,"supported":46,"title":167,"abstract":168,"tags":169},[33],[34,33],[],{"code":51,"score":171,"supported":46,"title":172,"abstract":173,"tags":175},4,[],[174],"土地流转",[29],[177,178],"河南 小麦主产区 农地流转","农业生产服务外包 农户","河南小麦主产区农地流转-3659","10.3389\u002Ffsufs.2026.1903026",{"doi":180,"openalex_id":182,"authors":183,"venue":10,"cited_by_count":60,"oa_url":145,"card":194,"direction":74,"ingested_from":76},"W7214551960",[184,187,189,191],{"name":185,"orcid":186},"Xu Li","https:\u002F\u002Forcid.org\u002F0009-0007-3365-419X",{"name":188,"orcid":9},"Yashu Ji",{"name":190,"orcid":9},"Fukang Zhu",{"name":192,"orcid":193},"Jiguang Zhu","https:\u002F\u002Forcid.org\u002F0000-0001-7531-8089",{"tldr":195,"method":196,"finding":197,"direction":74,"opportunity":198},"基于河南小麦区1529户数据，分析农业生产服务外包对农户土地流转意愿的双重影响。","有序Probit与中介效应模型，使用四县1529户微观调查数据。","服务外包促进转入、抑制转出，且抑制转出效应更强；收入效应中介贡献大于劳动力配置效应。","可探究服务外包与土地流转的长期动态及对女性户主、小农户的差异化政策干预。","2026-09-28T23:30:06.580311Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":211,"tags":213,"primary_topic":217,"secondary_topics":218,"classification":219,"search_phrases":236,"slug":239,"view_count":60,"doi":240,"paper":241,"created_at":268},1710,"Design and field validation of a vision-based graded compaction intensity control system for wheat seeding","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112357","Design and field validation of a vision-based graded compaction intensity control system for wheat seeding。Computers and Electronics in Agriculture","基于视觉的小麦播种分级压实强度控制系统设计与田间验证。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-04T00:00:00Z",63,{"impact":90,"substance":152,"depth":17,"authority":104,"freshness":52,"relevant":22,"comment":210},"基于视觉的小麦播种压实分级控制系统设计与田间验证，方法新颖且具实践价值，但发表于一年前，时效性低。",[212],{"name":206,"url":203},[214,215,28,216],"智慧农业","农业人工智能","田间验证",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":220},{"candidates":221,"agricultural_evidence":46},[222,226,231],{"code":32,"score":45,"supported":46,"title":223,"abstract":224,"tags":225},[33],[34,33],[],{"code":227,"score":22,"supported":14,"title":228,"abstract":229,"tags":230},"I1",[],[],[214],{"code":232,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},"I2",[],[],[215],[237,238],"农业人工智能 小麦种植 智慧农业 田间验证","农业人工智能 小麦种植","农业人工智能小麦种植智慧农业田间验证-1710","10.1016\u002Fj.compag.2026.112357",{"doi":240,"openalex_id":242,"authors":243,"venue":206,"cited_by_count":60,"oa_url":203,"card":262,"direction":266,"ingested_from":76},"W7208716814",[244,247,249,251,253,256,259],{"name":245,"orcid":246},"Mingyu Qiu","https:\u002F\u002Forcid.org\u002F0009-0004-8755-335X",{"name":248,"orcid":9},"Song Huang",{"name":250,"orcid":9},"Chao Chen",{"name":252,"orcid":9},"Qingjie Wang",{"name":254,"orcid":255},"Wenwen Chen","https:\u002F\u002Forcid.org\u002F0000-0003-0277-0941",{"name":257,"orcid":258},"Bingzheng Li","https:\u002F\u002Forcid.org\u002F0009-0002-2154-195X",{"name":260,"orcid":261},"Yixuan Guo","https:\u002F\u002Forcid.org\u002F0000-0002-6465-3858",{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"设计并田间验证了基于视觉的小麦播种分级压实强度控制系统。","视觉系统检测土壤压实状态，分级控制压实强度。","系统能有效调节压实强度，提高播种质量。","智慧农业 \u002F 农业物联网","可探索基于深度学习的压实状态实时识别与自适应控制，提升复杂田间环境适应性。","2026-09-05T23:30:01.999741Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":280,"tags":282,"primary_topic":287,"secondary_topics":288,"classification":300,"search_phrases":338,"slug":341,"view_count":60,"doi":342,"paper":343,"created_at":361},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":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":279},"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。",[281],{"name":275,"url":272},[214,283,284,285,286],"机器学习","遥感监测","土壤墒情","冬小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[289,295],{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":232,"slug":296,"name":297,"path":298,"parent_code":299},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":301},{"candidates":302,"agricultural_evidence":46},[303,309,314,319,327,334],{"code":32,"score":304,"supported":46,"title":305,"abstract":307,"tags":308},26,[306,33],"winter 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Zhang",{"name":352,"orcid":9},"WEI Lei",{"name":354,"orcid":9},"Chengcai Zhang",{"tldr":356,"method":357,"finding":358,"direction":359,"opportunity":360},"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。","农业遥感与作物表型","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":363,"title":364,"url":365,"summary":366,"summary_zh":9,"content":9,"source_name":367,"source_url":9,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":371,"tags":373,"primary_topic":378,"secondary_topics":379,"classification":389,"search_phrases":411,"slug":414,"view_count":22,"doi":9,"paper":415,"created_at":423},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":152,"substance":151,"depth":152,"authority":45,"freshness":13,"relevant":22,"comment":370},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[372],{"name":367,"url":365},[214,374,375,376,377],"农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[380,384],{"code":320,"slug":381,"name":382,"path":383,"parent_code":299},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":385,"slug":386,"name":387,"path":388,"parent_code":294},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":390},{"candidates":391,"agricultural_evidence":46},[392,397,401,407],{"code":32,"score":393,"supported":46,"title":394,"abstract":395,"tags":396},11,[34],[34],[],{"code":320,"score":393,"supported":46,"title":398,"abstract":399,"tags":400},[376],[],[375,374,376],{"code":385,"score":402,"supported":46,"title":403,"abstract":405,"tags":406},8,[404],"渍害",[],[],{"code":227,"score":22,"supported":14,"title":408,"abstract":409,"tags":410},[],[],[214],[412,413],"江苏省农科院 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中晚稻已收购1800万吨","https:\u002F\u002Fwww.peopleweekly.cn\u002Fhtml\u002F2026\u002Fsannong_1019\u002F279469.html","农业农村部最新农情调度显示，全国秋粮收获过七成，其中东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。冬小麦播种过一成，冬油菜播种过三成。国家粮食和物资储备局数据显示，各类经营主体已收购中晚稻1800万吨；秋粮产量占全年粮食产量约四分之三。","本报北京10月18日电  （记者郁静娴）据农业农村部最新农情调度，全国秋粮收获过七成。其中，东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。同时，各地秋冬种有序推进，冬小麦播种过一成，冬油菜播种过三成。\n\n本报北京10月18日电  （记者王东辉）记者从国家粮食和物资储备局获悉：各地秋粮旺季收购正有序展开。初步统计，各类经营主体已收购中晚稻1800万吨。\n\n当前秋粮已开始收获上市，秋粮产量占全年粮食产量的3\u002F4左右，涉及品种多、数量规模大、覆盖区域广。相关部门将加强秋粮收购工作统筹组织，抓好市场化收购和政策性收储，落实落细各项政策举措，全力保障农民售粮渠道顺畅、市场平稳运行。","人民日报 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