[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5235":3,"related-5235":128},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":106,"slug":109,"view_count":110,"doi":111,"paper":112,"created_at":127},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。",null,"International Journal of Plant & Soil Science","2026-10-17T00:00:00Z","论文",10,false,83,{"impact":17,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,18,13,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","无人机","精准施肥","变量施肥","遥感监测","养分利用效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,57,65,73,80,87,94,101],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":56},20,true,[49],"fertilization",[51,52,53,54,55],"施肥","肥料","水肥一体化","养分管理","fertigation",[31,29,28],{"code":58,"score":59,"supported":47,"title":60,"abstract":61,"tags":64},"I3",7,[],[62,63],"遥感","remote sensing",[30],{"code":66,"score":67,"supported":47,"title":68,"abstract":69,"tags":72},"A1",6,[],[70,71],"水稻","rice",[],{"code":74,"score":67,"supported":47,"title":75,"abstract":76,"tags":79},"A2",[],[77,78],"小麦","wheat",[],{"code":81,"score":67,"supported":47,"title":82,"abstract":83,"tags":86},"A3",[],[84,85],"玉米","maize",[],{"code":88,"score":67,"supported":47,"title":89,"abstract":90,"tags":93},"H2",[],[91,92],"土壤","soil",[],{"code":95,"score":67,"supported":47,"title":96,"abstract":97,"tags":100},"I4",[],[98,99],"地理信息","geographic information",[],{"code":102,"score":21,"supported":14,"title":103,"abstract":104,"tags":105},"I1",[],[],[26],[107,108],"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":111,"openalex_id":113,"authors":114,"venue":10,"cited_by_count":119,"oa_url":6,"card":120,"direction":124,"ingested_from":126},"W7222838957",[115,117],{"name":116,"orcid":9},"S. T. Panusha",{"name":118,"orcid":9},"T. M. Soumya",0,{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"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。","openalex","2026-10-18T23:30:07.850055Z",{"total":67,"page":21,"page_size":67,"items":129},[130,206,290,335,414,480],{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":144,"tags":146,"primary_topic":148,"secondary_topics":151,"classification":153,"search_phrases":187,"slug":190,"view_count":21,"doi":191,"paper":192,"created_at":205},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",71,{"impact":140,"substance":46,"depth":141,"authority":19,"freshness":142,"relevant":21,"comment":143},12,17,9,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[145],{"name":136,"url":133},[26,29,84,147,30,54],"精准农业",{"code":81,"slug":85,"name":84,"path":149,"parent_code":150},"\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[152],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":154},{"candidates":155,"agricultural_evidence":47},[156,162,167,172,176,183],{"code":102,"score":157,"supported":47,"title":158,"abstract":160,"tags":161},16,[159],"precision agriculture",[147,159],[26,147],{"code":81,"score":163,"supported":47,"title":164,"abstract":165,"tags":166},15,[85],[84,85],[84],{"code":33,"score":168,"supported":47,"title":169,"abstract":170,"tags":171},11,[],[51,52,54],[54,29],{"code":88,"score":67,"supported":47,"title":173,"abstract":174,"tags":175},[],[91,92],[],{"code":177,"score":178,"supported":47,"title":179,"abstract":180,"tags":182},"I2",3,[],[181],"机器学习",[],{"code":58,"score":21,"supported":14,"title":184,"abstract":185,"tags":186},[],[],[30],[188,189],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":191,"openalex_id":193,"authors":194,"venue":136,"cited_by_count":119,"oa_url":133,"card":199,"direction":203,"ingested_from":126},"W7221366291",[195,197],{"name":196,"orcid":9},"Piyali Pal",{"name":198,"orcid":9},"Disha Chattopadhyay",{"tldr":200,"method":201,"finding":202,"direction":203,"opportunity":204},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","农业人工智能与决策模型","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":213,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":214,"score_detail":215,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":229,"search_phrases":267,"slug":270,"view_count":110,"doi":271,"paper":272,"created_at":289},2006,"Precision fertilizer management for germplasm maintenance of queen’s flower (Lagerstroemia speciosa)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffhort.2026.1933427","Introduction Nitrogen management in container-grown ornamentals must balance plant quality against the risk of nutrient loss to surrounding water systems, a challenge that is especially pronounced in subtropical nursery environments. This study evaluated precision nitrogen strategies for Lagerstroemia speciosa ‘Big Pink,’ an increasingly popular landscaping tree, by integrating leachate monitoring with plant-based optical sensing. Methods We evaluated the effects of six Azalea fertilizer (8-4–8 N-P-K) treatments: T0 (25 g), T1 (15 g + 10 g supplemental fertilizer, SF), T2 (20 g + 10 g SF), T3 (25 g + 10 g SF), T4 (30 g + 10 g SF), and T5 (35 g + 10 g SF), each with five replications, on container-grown Lagerstroemia speciosa ‘Big Pink’ under subtropical nursery conditions in southern Florida. Plant growth (leaf count), chlorophyll-based indices (SPAD, atLEAF), canopy reflectance (NDVI), and leachate chemical properties (Ca 2+ , NO 3 − , K + , Na + , pH, salinity, and EC) were assessed monthly and analyzed across early, mid-, and late growth stages using ANOVA, Tukey’s HSD test, and Pearson correlation analysis. Results The two highest fertilizer treatments, T4 (30 g base rate plus a 10 g supplemental application) and T5 (35 g base rate plus a 10 g supplemental application), consistently produced greater NO 3 − and K + concentrations in leachate across all growth stages, indicating elevated nutrient mobility and leaching risk. Treatment (T5) further lowered substrate pH and raised salinity and EC at the late stage, reflecting salt accumulation in the root zone. Leaf production was maximized under intermediate fertilizer rates (T2-T4), while both insufficient (T0-T1) and excessive (T5) fertilization reduced growth. Chlorophyll indices (SPAD, atLEAF) reliably detected nitrogen deficiency under T0 from the mid-stage onward, whereas NDVI became a responsive indicator only at the late stage as canopy development increased. Correlation analysis showed progressively stronger associations among leachate variables over time, alongside consistent agreement between SPAD and atLEAF across all stages. Conclusion Moderate fertilizer rates (T2-T3) optimized leaf growth and plant nitrogen status in container-grown L . speciosa ‘Big Pink’ while minimizing nutrient losses, supporting their adoption as a precision nitrogen management strategy for subtropical ornamental nursery production.","引言 容器栽培观赏植物的氮素管理需在植物品质与养分流失至周边水体的风险之间取得平衡，这一挑战在亚热带苗圃环境中尤为突出。本研究通过整合淋滤液监测与基于植物的光学传感技术，评估了针对紫薇‘大粉’（一种日益流行的景观树种）的精准氮素管理策略。方法 本研究在佛罗里达州南部的亚热带苗圃条件下，评估了六种杜鹃花肥料（8-4-8 N-P-K）处理对容器栽培紫薇‘大粉’的影响：T0（25克）、T1（15克+10克补充肥料，SF）、T2（20克+10克SF）、T3（25克+10克SF）、T4（30克+10克SF）和T5（35克+10克SF），每个处理设五次重复。每月测定植物生长量（叶片数）、基于叶绿素的指数（SPAD、atLEAF）、冠层反射率（NDVI）以及淋滤液化学性质（Ca²⁺、NO₃⁻、K⁺、Na⁺、pH、盐度和电导率EC），并在早期、中期和晚期生长阶段采用方差分析（ANOVA）、Tukey HSD检验和Pearson相关分析进行评估。结果 两个最高施肥处理T4（30克基肥加10克补充肥）和T5（35克基肥加10克补充肥）在所有生长阶段均持续产生较高的淋滤液NO₃⁻和K⁺浓度，表明养分迁移性和淋失风险升高。处理T5进一步降低了基质pH，并在晚期阶段提高了盐度和EC，反映出根区盐分积累。叶片产量在中等施肥量（T2-T4）下达到最大，而施肥不足（T0-T1）和过量（T5）均减少了生长量。叶绿素指数（SPAD、atLEAF）从中期阶段起即可可靠检测T0处理下的氮素缺乏，而NDVI仅在晚期阶段随着冠层发育增加才成为响应性指标。相关分析显示，淋滤液变量之间的关联随时间逐渐增强，同时SPAD和atLEAF在所有阶段均保持一致的一致性。结论 中等施肥量（T2-T3）在容器栽培紫薇‘大粉’中优化了叶片生长和植物氮素状况，同时最大限度地减少了养分损失，支持其作为亚热带观赏植物苗圃生产中精准氮素管理策略的推广应用。","Frontiers in Horticulture","2026-09-08T00:00:00Z",60,{"impact":140,"substance":18,"depth":157,"authority":140,"freshness":110,"relevant":21,"comment":216},"针对紫薇精准氮肥管理的研究，结合光学传感与淋洗监测，为亚热带苗圃生产提供减损增效方案，但影响范围限于细分领域。",[218],{"name":212,"url":209},[26,28,30,220,221],"园艺作物","氮素管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[224],{"code":225,"slug":226,"name":227,"path":228,"parent_code":37},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry",{"status":40,"locked":14,"version":41,"method":42,"evidence":230},{"candidates":231,"agricultural_evidence":47},[232,237,242,248,254,259,263],{"code":33,"score":18,"supported":47,"title":233,"abstract":235,"tags":236},[234],"fertilizer",[51,52,234,49],[28],{"code":225,"score":20,"supported":47,"title":238,"abstract":240,"tags":241},[239],"germplasm",[],[],{"code":243,"score":178,"supported":47,"title":244,"abstract":245,"tags":247},"B3",[],[246],"可可",[],{"code":249,"score":178,"supported":47,"title":250,"abstract":251,"tags":253},"B4",[],[252],"ornamental",[],{"code":255,"score":21,"supported":14,"title":256,"abstract":257,"tags":258},"B",[],[],[220],{"code":102,"score":21,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[26],{"code":58,"score":21,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[30],[268,269],"园艺作物 智慧农业 氮素管理 精准施肥","园艺作物 智慧农业","园艺作物智慧农业氮素管理精准施肥-2006","10.3389\u002Ffhort.2026.1933427",{"doi":271,"openalex_id":273,"authors":274,"venue":212,"cited_by_count":119,"oa_url":283,"card":284,"direction":203,"ingested_from":126},"W7211954156",[275,277,279,281],{"name":276,"orcid":9},"Sofia Lopez Farina",{"name":278,"orcid":9},"Isha Poudel",{"name":280,"orcid":9},"Madhugiri Nageswara-Rao",{"name":282,"orcid":9},"Amir Ali Khoddamzadeh","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1933427\u002Fpdf",{"tldr":285,"method":286,"finding":287,"direction":124,"opportunity":288},"通过淋洗监测与光学传感，优化紫薇容器苗氮肥管理，减少养分流失。","设置六种施肥处理，结合SPAD、NDVI及淋洗液化学分析，进行方差分析和相关分析","中等施肥量（T2-T3）最佳，兼顾生长与环保；过量施肥增加淋失和盐分积累。","可探索基于实时传感的闭环精准施肥系统，结合机器学习预测作物需肥量，减少环境风险。","2026-09-09T23:30:37.456859Z",{"id":291,"title":292,"url":293,"summary":294,"summary_zh":9,"content":9,"source_name":295,"source_url":9,"published_at":296,"category":297,"cover_url":9,"hotness":298,"is_selected":14,"score":299,"score_detail":300,"sources":303,"tags":308,"primary_topic":312,"secondary_topics":313,"classification":314,"search_phrases":330,"slug":333,"view_count":119,"doi":9,"paper":9,"created_at":334},5212,"人保财险联合浙江省农科院发布《无人机施肥施药作业质量划分》团体标准","https:\u002F\u002Fwww.cnr.cn\u002Fzjfw\u002Fmlnb\u002Fnbgd\u002F20261016\u002Ft20261016_527843650.shtml","在宁波举办的首届四明保险论坛科技保险主题活动上,人保财险联合浙江省农业科学院发布《无人机施肥施药作业质量划分》团体标准,为农业无人机作业划定质量标尺,促进低空农业高质量发展和风险防控。","央广网(2026-10-16)","2026-10-16T00:00:00Z","报道",25,68,{"impact":18,"substance":157,"depth":140,"authority":301,"freshness":20,"relevant":21,"comment":302},14,"省级科研机构与保险企业联合发布团体标准，为农业无人机作业质量提供标尺，具行业参考价值，但属细分领域进展，未达重大突破层级。",[304,305],{"name":295,"url":293},{"name":306,"url":307},"新浪财经(2026-10-15)","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-15\u002Fdoc-inivhyai8038690.shtml",[26,309,27,310,311],"低空经济","农业保险","农业标准",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":315},{"candidates":316,"agricultural_evidence":47},[317,321,326],{"code":33,"score":168,"supported":47,"title":318,"abstract":319,"tags":320},[51],[51],[],{"code":322,"score":21,"supported":14,"title":323,"abstract":324,"tags":325},"G3",[],[],[310],{"code":102,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},[],[],[26],[331,332],"人保财险 浙江省农科院 无人机施肥","无人机施肥施药 作业质量划分","人保财险浙江省农科院无人机施肥-5212","2026-10-18T00:05:23.058110Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":346,"tags":348,"primary_topic":352,"secondary_topics":353,"classification":364,"search_phrases":393,"slug":396,"view_count":119,"doi":9,"paper":397,"created_at":413},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":140,"substance":157,"depth":163,"authority":140,"freshness":67,"relevant":21,"comment":345},"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。",[347],{"name":341,"url":338},[26,349,28,350,351],"农业人工智能","农业物联网","智能农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[354,359],{"code":177,"slug":355,"name":356,"path":357,"parent_code":358},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":360,"slug":361,"name":362,"path":363,"parent_code":358},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":365},{"candidates":366,"agricultural_evidence":47},[367,371,377,384,388],{"code":33,"score":163,"supported":47,"title":368,"abstract":369,"tags":370},[234],[52,234],[28],{"code":177,"score":13,"supported":47,"title":372,"abstract":373,"tags":376},[],[181,374,375],"machine learning","artificial intelligence",[349],{"code":360,"score":13,"supported":47,"title":378,"abstract":379,"tags":383},[],[380,381,382],"物联网","internet of things","IoT",[350],{"code":102,"score":59,"supported":47,"title":385,"abstract":386,"tags":387},[],[147,159],[26],{"code":389,"score":21,"supported":14,"title":390,"abstract":391,"tags":392},"F5",[],[],[351],[394,395],"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":9,"openalex_id":398,"authors":399,"venue":341,"cited_by_count":119,"oa_url":338,"card":408,"direction":124,"ingested_from":126},"W7221394563",[400,402,404,406],{"name":401,"orcid":9},"Anjali Manoj Kumbhar",{"name":403,"orcid":9},"Shruti Prashant Kulkarni",{"name":405,"orcid":9},"Snehal Santosh Khune",{"name":407,"orcid":9},"B. B. Godbole",{"tldr":409,"method":410,"finding":411,"direction":124,"opportunity":412},"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":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":425,"tags":427,"primary_topic":429,"secondary_topics":430,"classification":433,"search_phrases":461,"slug":464,"view_count":110,"doi":465,"paper":466,"created_at":479},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","2026-09-30T00:00:00Z",63,{"impact":140,"substance":46,"depth":141,"authority":67,"freshness":20,"relevant":21,"comment":424},"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。",[426],{"name":420,"url":417},[26,349,28,70,428],"氮肥管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[431],{"code":66,"slug":71,"name":70,"path":432,"parent_code":150},"\u002Ftopic\u002Ffield-crops\u002Frice",{"status":40,"locked":14,"version":41,"method":42,"evidence":434},{"candidates":435,"agricultural_evidence":47},[436,441,447,451,457],{"code":33,"score":437,"supported":47,"title":438,"abstract":439,"tags":440},19,[234],[51,52,54,234],[428,28],{"code":66,"score":13,"supported":47,"title":442,"abstract":443,"tags":446},[],[70,444,71,445],"稻田","paddy",[70],{"code":102,"score":59,"supported":47,"title":448,"abstract":449,"tags":450},[],[147,159],[26],{"code":177,"score":59,"supported":47,"title":452,"abstract":453,"tags":456},[],[454,455],"深度学习","deep learning",[349],{"code":88,"score":67,"supported":47,"title":458,"abstract":459,"tags":460},[],[91,92],[],[462,463],"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":465,"openalex_id":467,"authors":468,"venue":420,"cited_by_count":119,"oa_url":417,"card":474,"direction":203,"ingested_from":126},"W7214974109",[469,471],{"name":470,"orcid":9},"Rusanara Akhter",{"name":472,"orcid":473},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":475,"method":476,"finding":477,"direction":203,"opportunity":478},"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":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":486,"source_url":483,"published_at":487,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":488,"score_detail":489,"sources":491,"tags":493,"primary_topic":495,"secondary_topics":497,"classification":505,"search_phrases":565,"slug":568,"view_count":178,"doi":569,"paper":570,"created_at":625},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":18,"substance":17,"depth":18,"authority":19,"freshness":142,"relevant":21,"comment":490},"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。",[492],{"name":486,"url":483},[26,28,77,494,53],"保护性耕作",{"code":74,"slug":78,"name":77,"path":496,"parent_code":150},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[498,499],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":500,"slug":501,"name":502,"path":503,"parent_code":504},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":506},{"candidates":507,"agricultural_evidence":47},[508,512,516,521,525,532,536,543,549,555,561],{"code":33,"score":157,"supported":47,"title":509,"abstract":510,"tags":511},[55],[51,55],[53,28],{"code":74,"score":163,"supported":47,"title":513,"abstract":514,"tags":515},[78],[77,78],[77],{"code":500,"score":140,"supported":47,"title":517,"abstract":519,"tags":520},[518],"conservation agriculture",[518],[494],{"code":81,"score":67,"supported":47,"title":522,"abstract":523,"tags":524},[],[84,85],[],{"code":526,"score":67,"supported":47,"title":527,"abstract":528,"tags":531},"A8",[],[529,530],"粮食安全","food security",[],{"code":88,"score":67,"supported":47,"title":533,"abstract":534,"tags":535},[],[91,92],[],{"code":537,"score":67,"supported":47,"title":538,"abstract":539,"tags":542},"H3",[],[540,541],"灌溉","irrigation",[],{"code":544,"score":178,"supported":47,"title":545,"abstract":546,"tags":548},"A7",[],[547],"轮作",[],{"code":550,"score":178,"supported":47,"title":551,"abstract":552,"tags":554},"H5",[],[553],"秸秆还田",[],{"code":556,"score":178,"supported":47,"title":557,"abstract":558,"tags":560},"H7",[],[559],"干旱",[],{"code":102,"score":21,"supported":14,"title":562,"abstract":563,"tags":564},[],[],[26],[566,567],"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":569,"openalex_id":571,"authors":572,"venue":486,"cited_by_count":119,"oa_url":483,"card":620,"direction":124,"ingested_from":126},"W7214579192",[573,575,578,580,582,584,586,589,591,593,595,597,599,602,604,606,609,612,615,618],{"name":574,"orcid":9},"Kadagonda Nithinkumar",{"name":576,"orcid":577},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":579,"orcid":9},"Devika A. R.",{"name":581,"orcid":9},"Arjun Singh",{"name":583,"orcid":9},"Susama Sudhishri",{"name":585,"orcid":9},"Vinod Kumar Singh",{"name":587,"orcid":588},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":590,"orcid":9},"Sarvendra Kumar",{"name":592,"orcid":9},"Teekam Singh",{"name":594,"orcid":9},"Jitendra Rajput",{"name":596,"orcid":9},"Pravin Kumar Upadhyaya",{"name":598,"orcid":9},"R. N. Sahoo",{"name":600,"orcid":601},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":603,"orcid":9},"Aye Aye San",{"name":605,"orcid":9},"K. S. Sachin",{"name":607,"orcid":608},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":610,"orcid":611},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":613,"orcid":614},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":616,"orcid":617},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":619,"orcid":9},"Subhash Babu",{"tldr":621,"method":622,"finding":623,"direction":124,"opportunity":624},"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"]