[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-7":3,"related-7":84},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":21,"tags":23,"primary_topic":28,"secondary_topics":34,"classification":41,"search_phrases":79,"slug":82,"view_count":13,"doi":8,"paper":8,"created_at":83},7,"科技赋能农田 内蒙古马铃薯施肥进入\"精准导航\"时代","https:\u002F\u002Fwww.northnews.cn\u002Fnews\u002F2026\u002F0714\u002F2507582.html","内蒙古农业大学资源与环境学院李斐教授团队近日在国际期刊《Field Crops Research》和《IEEE Transactions on Geoscience and Remote Sensing》发表突破性成果，针对滴灌马铃薯氮素管理难题，以乌兰察布市马铃薯科技小院为\"试验田\"，构建\"精准监测—模型迁移—优化管理\"完整技术链条。团队巧妙融合主动学习与迁移学习技术，从海量数据中筛选\"高信息量样本\"，教会模型快速适应不同环境；同时精准识别马铃薯\"氮素敏感波段\"，使传感器能像医生听诊器一样通过叶片光谱变化快速诊断植株营养状况，氮素监测模型具备跨年份、跨传感器的超强适应能力，为区域尺度氮素动态监测提供可靠工具。农户通过手机 APP 即可接收施肥建议，如同拥有\"私人营养顾问\"。",null,"![Image 1: 403](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002F403-icon.png)\n\n您的请求已中断\n\n您提交的请求可能对网站造成威胁，无法访问\n\n可能原因\n\nWeb应用防护服务检测您当前访问存在Web安全风险或访问不合规\n\n![Image 2](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002F403-link.svg)\n\nClient\n\n浏览器正常\n\n![Image 3](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002Fsuccess-status.svg)\n\n腾讯云WAF\n\n访问拦截\n\n![Image 4](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002Ferror-status.svg)\n\n源站服务器\n\n无法访问\n\n![Image 5](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002Fcheck-status.svg)\n\n您可以做什么？\n\n如有疑问，请点击提交反馈或联系网站安全运维中心反馈请求UUID\n\n请求UUID：11117786819026751308![Image 6](https:\u002F\u002Fdomain-config-1256704386.cos.accelerate.myqcloud.com\u002Fblock-pages\u002Fstatic\u002Fcopy.svg)\n\n提交反馈","《正北方网》2026 年 07 月 14 日 07:27","2026-07-13T23:27:00Z","报道",10,false,29,{"impact":13,"substance":17,"depth":17,"authority":18,"freshness":19,"relevant":19,"comment":20},5,8,1,"标题涉及精准施肥与遥感技术，但正文被拦截，信息量有限，时效性差，仅作地方动态收录。",[22],{"name":10,"url":6},[24,25,26,27],"智慧农业","精准施肥","内蒙古","遥感技术",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[35],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,54,60,67,74],{"code":36,"score":48,"supported":49,"title":50,"abstract":52,"tags":53},12,true,[51],"施肥",[51],[25],{"code":29,"score":55,"supported":49,"title":56,"abstract":58,"tags":59},11,[57],"马铃薯",[57],[],{"code":61,"score":62,"supported":49,"title":63,"abstract":64,"tags":66},"I3",4,[],[65],"remote sensing",[27],{"code":68,"score":69,"supported":49,"title":70,"abstract":71,"tags":73},"G1",3,[],[72],"科技小院",[],{"code":75,"score":19,"supported":14,"title":76,"abstract":77,"tags":78},"I1",[],[],[24],[80,81],"智慧农业 精准施肥 遥感技术 内蒙古","智慧农业 精准施肥","智慧农业精准施肥遥感技术内蒙古-7","2026-07-27T05:05:18.650976Z",{"total":85,"page":19,"page_size":85,"items":86},6,[87,202,295,351,422,488],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":8,"source_name":93,"source_url":90,"published_at":94,"category":95,"cover_url":8,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":109,"secondary_topics":110,"classification":118,"search_phrases":170,"slug":173,"view_count":62,"doi":174,"paper":175,"created_at":201},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z","论文",69,{"impact":48,"substance":98,"depth":99,"authority":100,"freshness":18,"relevant":19,"comment":101},20,16,13,"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[103],{"name":93,"url":90},[24,105,106,107,108],"农业人工智能","精准农业","遥感","高粱",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[111,117],{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"status":42,"locked":14,"version":43,"method":44,"evidence":119},{"candidates":120,"agricultural_evidence":49},[121,127,133,140,148,153,160,166],{"code":29,"score":122,"supported":49,"title":123,"abstract":125,"tags":126},15,[124],"sorghum",[108,124],[108],{"code":112,"score":122,"supported":49,"title":128,"abstract":130,"tags":132},[129],"machine learning",[131,129],"机器学习",[105],{"code":36,"score":134,"supported":49,"title":135,"abstract":136,"tags":139},9,[],[51,137,138],"养分管理","fertilizer",[],{"code":141,"score":134,"supported":49,"title":142,"abstract":143,"tags":147},"H3",[],[144,145,146],"灌溉","水资源","irrigation",[],{"code":75,"score":18,"supported":49,"title":149,"abstract":150,"tags":152},[],[106,151],"precision agriculture",[24,106],{"code":154,"score":85,"supported":49,"title":155,"abstract":156,"tags":159},"H2",[],[157,158],"土壤","soil",[],{"code":161,"score":69,"supported":49,"title":162,"abstract":163,"tags":165},"A7",[],[164],"间作",[],{"code":61,"score":19,"supported":14,"title":167,"abstract":168,"tags":169},[],[],[107],[171,172],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918","10.3389\u002Ffenvs.2026.1928610",{"doi":174,"openalex_id":176,"authors":177,"venue":93,"cited_by_count":192,"oa_url":90,"card":193,"direction":199,"ingested_from":200},"W7214994063",[178,180,183,185,188,190],{"name":179,"orcid":8},"Anoop Valiya Veettil",{"name":181,"orcid":182},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":184,"orcid":8},"Ali Fares",{"name":186,"orcid":187},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":189,"orcid":8},"Almoutaz Elhassan",{"name":191,"orcid":8},"Atikur Rahman",0,{"tldr":194,"method":195,"finding":196,"direction":197,"opportunity":198},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","农业人工智能与决策模型","openalex","2026-10-01T23:30:54.379150Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":8,"source_name":208,"source_url":205,"published_at":209,"category":95,"cover_url":8,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":215,"tags":217,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":275,"slug":278,"view_count":279,"doi":280,"paper":281,"created_at":294},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",83,{"impact":212,"substance":212,"depth":213,"authority":100,"freshness":18,"relevant":19,"comment":214},22,18,"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[216],{"name":208,"url":205},[24,218,25,219,220,221],"无人机","变量施肥","遥感监测","养分利用效率",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":225},{"candidates":226,"agricultural_evidence":49},[227,235,239,246,253,260,264,271],{"code":36,"score":98,"supported":49,"title":228,"abstract":230,"tags":234},[229],"fertilization",[51,231,232,137,233],"肥料","水肥一体化","fertigation",[221,219,25],{"code":61,"score":4,"supported":49,"title":236,"abstract":237,"tags":238},[],[107,65],[220],{"code":240,"score":85,"supported":49,"title":241,"abstract":242,"tags":245},"A1",[],[243,244],"水稻","rice",[],{"code":247,"score":85,"supported":49,"title":248,"abstract":249,"tags":252},"A2",[],[250,251],"小麦","wheat",[],{"code":254,"score":85,"supported":49,"title":255,"abstract":256,"tags":259},"A3",[],[257,258],"玉米","maize",[],{"code":154,"score":85,"supported":49,"title":261,"abstract":262,"tags":263},[],[157,158],[],{"code":265,"score":85,"supported":49,"title":266,"abstract":267,"tags":270},"I4",[],[268,269],"地理信息","geographic information",[],{"code":75,"score":19,"supported":14,"title":272,"abstract":273,"tags":274},[],[],[24],[276,277],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235",2,"10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":280,"openalex_id":282,"authors":283,"venue":208,"cited_by_count":192,"oa_url":205,"card":288,"direction":292,"ingested_from":200},"W7222838957",[284,286],{"name":285,"orcid":8},"S. T. Panusha",{"name":287,"orcid":8},"T. M. Soumya",{"tldr":289,"method":290,"finding":291,"direction":292,"opportunity":293},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","智慧农业 \u002F 农业物联网","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","2026-10-18T23:30:07.850055Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":8,"content":8,"source_name":300,"source_url":8,"published_at":301,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":308,"primary_topic":312,"secondary_topics":313,"classification":320,"search_phrases":346,"slug":349,"view_count":192,"doi":8,"paper":8,"created_at":350},4853,"数智新农人绘出好'丰'景(3)|智慧秋收,让谷子精准归仓","https:\u002F\u002Fhebei.hebnews.cn\u002F2026-10\u002F13\u002Fcontent_9568139.htm","秋染沃野,金谷飘香。近日在武安市白府村,新配备的全喂入式联合收割机加装谷子专用扶禾割台和多传感监测传感器,多组传感器可实时监测喂入量、籽粒损失率、谷粒湿度,自动微调滚筒转速与风机风力。播种端使用北斗导航精量播种机,每亩用种从1斤降到4两。在河北省农林科学院谷子所研究员夏雪岩团队指导下,先定点取土检测,再结合谷子特征制定施肥方案。武安市科源种植公司800余亩基地谷子亩产稳定在400多公斤,亩均增收显著。","河北新闻网 河北日报 2026 年 10 月 13 日","2026-10-13T00:00:00Z",66,{"impact":304,"substance":213,"depth":122,"authority":13,"freshness":134,"relevant":19,"comment":305},14,"县级智慧秋收实践报道，含传感器监测、北斗播种与测土施肥等具体数据，具行业参考价值但影响层级限于地市。",[307],{"name":300,"url":298},[24,309,25,310,311],"北斗导航","智能农机","谷子",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[314],{"code":315,"slug":316,"name":317,"path":318,"parent_code":319},"J4","seasonal-agriculture","农时与节庆活动","\u002Ftopic\u002Fpolicy\u002Fseasonal-agriculture","J",{"status":42,"locked":14,"version":43,"method":44,"evidence":321},{"candidates":322,"agricultural_evidence":49},[323,327,332,338,342],{"code":29,"score":48,"supported":49,"title":324,"abstract":325,"tags":326},[311],[311],[311],{"code":315,"score":18,"supported":49,"title":328,"abstract":330,"tags":331},[329],"秋收",[],[],{"code":333,"score":17,"supported":49,"title":334,"abstract":335,"tags":337},"F5",[],[336],"收割机",[309,310],{"code":36,"score":62,"supported":49,"title":339,"abstract":340,"tags":341},[],[51],[25],{"code":75,"score":19,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[24],[347,348],"武安 谷子 联合收割机","北斗导航 精量播种 谷子","武安谷子联合收割机-4853","2026-10-14T00:04:07.645393Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":8,"source_name":357,"source_url":354,"published_at":358,"category":95,"cover_url":8,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":362,"tags":364,"primary_topic":366,"secondary_topics":367,"classification":374,"search_phrases":401,"slug":404,"view_count":192,"doi":8,"paper":405,"created_at":421},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":48,"substance":99,"depth":122,"authority":48,"freshness":85,"relevant":19,"comment":361},"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[363],{"name":357,"url":354},[24,105,25,365,310],"农业物联网",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[368,369],{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},{"code":370,"slug":371,"name":372,"path":373,"parent_code":116},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":42,"locked":14,"version":43,"method":44,"evidence":375},{"candidates":376,"agricultural_evidence":49},[377,381,386,393,397],{"code":36,"score":122,"supported":49,"title":378,"abstract":379,"tags":380},[138],[231,138],[25],{"code":112,"score":13,"supported":49,"title":382,"abstract":383,"tags":385},[],[131,129,384],"artificial intelligence",[105],{"code":370,"score":13,"supported":49,"title":387,"abstract":388,"tags":392},[],[389,390,391],"物联网","internet of things","IoT",[365],{"code":75,"score":4,"supported":49,"title":394,"abstract":395,"tags":396},[],[106,151],[24],{"code":333,"score":19,"supported":14,"title":398,"abstract":399,"tags":400},[],[],[310],[402,403],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":8,"openalex_id":406,"authors":407,"venue":357,"cited_by_count":192,"oa_url":354,"card":416,"direction":292,"ingested_from":200},"W7221394563",[408,410,412,414],{"name":409,"orcid":8},"Anjali Manoj Kumbhar",{"name":411,"orcid":8},"Shruti Prashant Kulkarni",{"name":413,"orcid":8},"Snehal Santosh Khune",{"name":415,"orcid":8},"B. B. Godbole",{"tldr":417,"method":418,"finding":419,"direction":292,"opportunity":420},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":8,"source_name":428,"source_url":425,"published_at":94,"category":95,"cover_url":8,"hotness":13,"is_selected":14,"score":429,"score_detail":430,"sources":433,"tags":435,"primary_topic":437,"secondary_topics":438,"classification":441,"search_phrases":469,"slug":472,"view_count":279,"doi":473,"paper":474,"created_at":487},3920,"Automated Leaf Color Chart (LCC)-BasedNitrogen Fertilizer Recommendation UsingPaddy Leaf Image Color Classification","https:\u002F\u002Fdoi.org\u002F10.70818\u002Fijbass.2026.v07i02.0075","The demand for accurate and timely assessment of crop health is high in precision agriculture to support proper nutrient management. In this work, we present an intelligent deep learning-based solution that evaluates the colour of paddy rice leaves to provide real-time, context-sensitive recommendations regarding the addition of nitrogen fertilizers to grow rice sustainably. For optimum nitrogen management and sustainability in the production of rice crops, an accurate assessment of the color of paddy leaves is required. Herein, we present a deep learning-based framework for paddy leaf color classification with the capability of recommending an appropriate amount of nitrogen fertilizer as needed in real-time. In this research, the Paddy Net dataset containing 12,000 augmented rice leaf images will be used for training the neural network used for classifying the rice leaf's color based on the Leaf Color Chart (LCC) system, which consists of LCC levels from 2 to 5. Additionally, several deep learning architectures were trainedand tested, specifically Custom CNNs, VGG16, ResNet50 and EfficientNetB7, to identify which architecture provided the best classification performance of rice leaf color. In terms of the experimental results, it can be concluded that the Custom CNN with a classification accuracy of 97.55% significantly outperforms the transfer learning models, VGG16 (78.89%) and EfficientNetB7 (78.67%). To also improve practicality, multimodal vision-language model LLa MA 3.2-11B Vision is integrated into the system, allowing it to provide real-time, context-based nitrogen fertilizer recommendations based on visual input images. Additionally, the proposed framework takes into account other agronomic factors such as the growth stage of the crop, type of soil, and fertilizer application history. This allows for more accurate and relevant recommendations. The proposed framework allows for precise and objective nitrogen management without the subjective use of visual assessment methods typically used by farmers. Overall, the proposed system for fertilizer application is significant for the development of precision agriculture because it increases the efficiency of the decision-making process, optimizes the use of fertilizer, and increases the practice of sustainable rice cultivation.","在精准农业中，对作物健康状况进行准确且及时评估的需求很高，以支持合理的养分管理。在这项工作中，我们提出了一种基于深度学习的智能解决方案，通过评估水稻叶片的颜色，实时提供与情境相关的氮肥添加建议，以实现水稻的可持续种植。为了实现水稻作物生产中的优化氮管理和可持续性，需要对水稻叶片颜色进行准确评估。在此，我们提出了一个基于深度学习的稻田叶片颜色分类框架，能够根据实时需求推荐适量的氮肥。在本研究中，包含12,000张增强水稻叶片图像的Paddy Net数据集将用于训练神经网络，该网络基于叶色卡（Leaf Color Chart, LCC）系统对水稻叶片颜色进行分类，LCC等级从2到5。此外，我们训练并测试了多种深度学习架构，特别是自定义CNN、VGG16、ResNet50和EfficientNetB7，以确定哪种架构在水稻叶片颜色分类中表现最佳。从实验结果可以得出结论，自定义CNN以97.55%的分类准确率显著优于迁移学习模型VGG16（78.89%）和EfficientNetB7（78.67%）。为提高实用性，系统还集成了多模态视觉语言模型LLaMA 3.2-11B Vision，使其能够基于视觉输入图像提供实时、基于情境的氮肥推荐。此外，所提出的框架还考虑了其他农艺因素，如作物生长阶段、土壤类型和施肥历史，从而提供更准确和相关的建议。该框架实现了精确、客观的氮管理，避免了农民通常采用的主观视觉评估方法。总体而言，所提出的施肥系统对精准农业的发展具有重要意义，因为它提高了决策过程的效率，优化了肥料使用，并促进了可持续水稻种植实践。","International Journal of Business Arts and Scientific Study",63,{"impact":48,"substance":98,"depth":431,"authority":85,"freshness":18,"relevant":19,"comment":432},17,"用深度学习与视觉语言模型实现水稻叶色分级与氮肥实时推荐，方法新颖、数据规模可观，但期刊影响力有限，属细分领域技术进展。",[434],{"name":428,"url":425},[24,105,25,243,436],"氮肥管理",{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},[439],{"code":240,"slug":244,"name":243,"path":440,"parent_code":33},"\u002Ftopic\u002Ffield-crops\u002Frice",{"status":42,"locked":14,"version":43,"method":44,"evidence":442},{"candidates":443,"agricultural_evidence":49},[444,449,455,459,465],{"code":36,"score":445,"supported":49,"title":446,"abstract":447,"tags":448},19,[138],[51,231,137,138],[436,25],{"code":240,"score":13,"supported":49,"title":450,"abstract":451,"tags":454},[],[243,452,244,453],"稻田","paddy",[243],{"code":75,"score":4,"supported":49,"title":456,"abstract":457,"tags":458},[],[106,151],[24],{"code":112,"score":4,"supported":49,"title":460,"abstract":461,"tags":464},[],[462,463],"深度学习","deep learning",[105],{"code":154,"score":85,"supported":49,"title":466,"abstract":467,"tags":468},[],[157,158],[],[470,471],"水稻 叶片颜色 氮肥推荐","Paddy Net 深度学习 叶色卡","水稻叶片颜色氮肥推荐-3920","10.70818\u002Fijbass.2026.v07i02.0075",{"doi":473,"openalex_id":475,"authors":476,"venue":428,"cited_by_count":192,"oa_url":425,"card":482,"direction":199,"ingested_from":200},"W7214974109",[477,479],{"name":478,"orcid":8},"Rusanara Akhter",{"name":480,"orcid":481},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":483,"method":484,"finding":485,"direction":199,"opportunity":486},"用深度学习对水稻叶片颜色分级，并结合多模态大模型实时推荐氮肥用量。","Paddy Net 1.2万张增强叶片图像，训练Custom CNN、VGG16","Custom CNN分类准确率达97.55%，显著优于VGG16（78.89%）和Efficient","可探索轻量化模型部署到手机端，并结合多源农情数据提升跨品种、跨区域推荐泛化能力。","2026-10-01T23:30:59.367113Z",{"id":489,"title":490,"url":491,"summary":492,"summary_zh":493,"content":8,"source_name":494,"source_url":491,"published_at":495,"category":95,"cover_url":8,"hotness":13,"is_selected":14,"score":496,"score_detail":497,"sources":499,"tags":501,"primary_topic":503,"secondary_topics":505,"classification":513,"search_phrases":569,"slug":572,"view_count":69,"doi":573,"paper":574,"created_at":629},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",80,{"impact":213,"substance":212,"depth":213,"authority":100,"freshness":134,"relevant":19,"comment":498},"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[500],{"name":494,"url":491},[24,25,250,502,232],"保护性耕作",{"code":247,"slug":251,"name":250,"path":504,"parent_code":33},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[506,507],{"code":36,"slug":37,"name":38,"path":39,"parent_code":40},{"code":508,"slug":509,"name":510,"path":511,"parent_code":512},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":42,"locked":14,"version":43,"method":44,"evidence":514},{"candidates":515,"agricultural_evidence":49},[516,520,524,529,533,540,544,548,553,559,565],{"code":36,"score":99,"supported":49,"title":517,"abstract":518,"tags":519},[233],[51,233],[232,25],{"code":247,"score":122,"supported":49,"title":521,"abstract":522,"tags":523},[251],[250,251],[250],{"code":508,"score":48,"supported":49,"title":525,"abstract":527,"tags":528},[526],"conservation agriculture",[526],[502],{"code":254,"score":85,"supported":49,"title":530,"abstract":531,"tags":532},[],[257,258],[],{"code":534,"score":85,"supported":49,"title":535,"abstract":536,"tags":539},"A8",[],[537,538],"粮食安全","food security",[],{"code":154,"score":85,"supported":49,"title":541,"abstract":542,"tags":543},[],[157,158],[],{"code":141,"score":85,"supported":49,"title":545,"abstract":546,"tags":547},[],[144,146],[],{"code":161,"score":69,"supported":49,"title":549,"abstract":550,"tags":552},[],[551],"轮作",[],{"code":554,"score":69,"supported":49,"title":555,"abstract":556,"tags":558},"H5",[],[557],"秸秆还田",[],{"code":560,"score":69,"supported":49,"title":561,"abstract":562,"tags":564},"H7",[],[563],"干旱",[],{"code":75,"score":19,"supported":14,"title":566,"abstract":567,"tags":568},[],[],[24],[570,571],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":573,"openalex_id":575,"authors":576,"venue":494,"cited_by_count":192,"oa_url":491,"card":624,"direction":292,"ingested_from":200},"W7214579192",[577,579,582,584,586,588,590,593,595,597,599,601,603,606,608,610,613,616,619,622],{"name":578,"orcid":8},"Kadagonda Nithinkumar",{"name":580,"orcid":581},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":583,"orcid":8},"Devika A. R.",{"name":585,"orcid":8},"Arjun Singh",{"name":587,"orcid":8},"Susama Sudhishri",{"name":589,"orcid":8},"Vinod Kumar Singh",{"name":591,"orcid":592},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":594,"orcid":8},"Sarvendra Kumar",{"name":596,"orcid":8},"Teekam Singh",{"name":598,"orcid":8},"Jitendra Rajput",{"name":600,"orcid":8},"Pravin Kumar Upadhyaya",{"name":602,"orcid":8},"R. N. Sahoo",{"name":604,"orcid":605},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":607,"orcid":8},"Aye Aye San",{"name":609,"orcid":8},"K. S. Sachin",{"name":611,"orcid":612},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":614,"orcid":615},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":617,"orcid":618},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":620,"orcid":621},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":623,"orcid":8},"Subhash Babu",{"tldr":625,"method":626,"finding":627,"direction":292,"opportunity":628},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","2026-09-29T23:30:06.859116Z"]