[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4350":3,"related-4350":122},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":101,"slug":104,"view_count":105,"doi":106,"paper":107,"created_at":121},4350,"Role of Science and Technology in Poverty Alleviation Through Smart Agriculture Process in Nigeria","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no3.2025.pg.158.165","A smart Agriculture, is also known as precision agriculture which is rapidly growing field that involves the integration of technology and data analysis to optimize agriculture practice and increase efficiency in crop production in Nigeria to control the food shortage and scarcity. The smart agricultural it involves the use of sensor, drone satellite imagery and other technologies to monitor crops growth, soil condition, and weather partner. this information can be used to optimized irrigation, fertilization and pest control practice, ultimately leading to higher crop yield and reduce the waste of resource, more also smart agriculture involves the use of data analytic to analyze the collected data and identify pattern and correlations. This information can be used to informed decision about the planting, harvesting and storage. Automation is another key feature of smart agriculture, as it allows for more efficient and cost-effective farming practice, such as automated irrigation system and robotic harvesting equipment. Looking Nigeria in food shortage there are so many benefit of smart agricultural practice in a numerous way which include, increase efficiency, reduce waste and improve sustainability, it can also help to reduce the environmental impact of agriculture by reducing the use of pesticide and fertilizer, as well as reducing greenhouse gas emission through more efficiency farming practice. So in a nutshell smart agriculture present a high significant opportunity to improve the sustainability and productivity of agriculture. It can help farmers to make more inform decisions, reduce waste, and increase efficiency ultimately leading to a more sustainability and profitable agriculture sector and boost the economy growth of Nigeria.","智慧农业，又称精准农业，是一个快速发展的领域，涉及技术与数据分析的整合，以优化尼日利亚的农业实践并提高作物生产效率，从而控制粮食短缺和匮乏。智慧农业涉及利用传感器、无人机、卫星影像及其他技术来监测作物生长、土壤状况和天气模式。这些信息可用于优化灌溉、施肥和病虫害防治实践，最终实现更高的作物产量并减少资源浪费。此外，智慧农业还涉及利用数据分析来分析所收集的数据并识别模式和相关性。这些信息可用于为种植、收获和储存方面的决策提供依据。自动化是智慧农业的另一个关键特征，因为它能够实现更高效且更具成本效益的农业实践，例如自动化灌溉系统和机器人收割设备。鉴于尼日利亚面临粮食短缺，智慧农业实践在许多方面具有诸多益处，包括提高效率、减少浪费和改善可持续性，它还可以通过减少农药和化肥的使用来帮助降低农业对环境的影响，并通过更高效的农业实践减少温室气体排放。总而言之，智慧农业为改善农业的可持续性和生产力提供了重大机遇。它可以帮助农民做出更明智的决策、减少浪费并提高效率，最终形成更具可持续性和盈利性的农业部门，并促进尼日利亚的经济增长。",null,"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-05T00:00:00Z","论文",10,false,31,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,6,5,4,1,"主题相关但为泛泛综述，缺乏新数据与方法创新，信源权威性低，不宜进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","农业无人机","精准农业","尼日利亚农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,53,61,68,75,82,89,96],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},"I1",19,true,[49],"smart agriculture",[26,29,49,51],"precision agriculture",[26,29],{"code":32,"score":54,"supported":47,"title":55,"abstract":56,"tags":60},9,[],[57,58,59],"施肥","fertilizer","fertilization",[],{"code":62,"score":18,"supported":47,"title":63,"abstract":64,"tags":67},"B7",[],[65,66],"温室","greenhouse",[],{"code":69,"score":18,"supported":47,"title":70,"abstract":71,"tags":74},"F4",[],[72,73],"农药","pesticide",[],{"code":76,"score":18,"supported":47,"title":77,"abstract":78,"tags":81},"H2",[],[79,80],"土壤","soil",[],{"code":83,"score":18,"supported":47,"title":84,"abstract":85,"tags":88},"H3",[],[86,87],"灌溉","irrigation",[],{"code":90,"score":91,"supported":47,"title":92,"abstract":93,"tags":95},"F6",3,[],[94],"robotic harvesting",[],{"code":97,"score":21,"supported":14,"title":98,"abstract":99,"tags":100},"I3",[],[],[27],[102,103],"尼日利亚 智慧农业 减贫","尼日利亚农业 农业无人机 农业遥感 智慧农业","尼日利亚智慧农业减贫-4350",2,"10.56201\u002Fijcsmt.vol.11.no3.2025.pg.158.165",{"doi":106,"openalex_id":108,"authors":109,"venue":10,"cited_by_count":112,"oa_url":113,"card":114,"direction":118,"ingested_from":120},"W7220743666",[110],{"name":111,"orcid":9},"Aminu Ahmed",0,"https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 3 2025\u002FRole of Science and Technology 158-165.pdf",{"tldr":115,"method":116,"finding":117,"direction":118,"opportunity":119},"综述智慧农业技术如何通过精准农业助力尼日利亚减贫与粮食安全。","文献综述，整合传感器、无人机、卫星影像、数据分析与自动化技术。","智慧农业可提高效率、减少浪费、增强可持续性并促进经济增长。","智慧农业 \u002F 农业物联网","可针对尼日利亚小农户开展低成本传感器与无人机应用的本土化实证研究。","openalex","2026-10-07T23:30:12.855407Z",{"total":18,"page":21,"page_size":18,"items":123},[124,209,289,358,456,523],{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":138,"tags":140,"primary_topic":144,"secondary_topics":145,"classification":146,"search_phrases":164,"slug":167,"view_count":112,"doi":168,"paper":169,"created_at":208},5327,"Poloxamer‐Enabled Thermoresponsive Biobased Controlled‐Release Fertilizer for On‐Demand Nutrient Delivery","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadvs.78371","ABSTRACT Synchronizing nutrient release of fertilizers with crop uptake remains a central challenge in precision agriculture. Here, we present a novel temperature‐responsive biobased controlled‐release fertilizer (TRBFs) incorporating Poloxamer 407 (F127) as a thermal switch within a castor oil‐derived polyurethane coating. The optimized TRBF10 (3 wt.% coating rate, 10 wt.% F127 in coating) exhibits a sharp, reversible phase transition at ∼20°C, matching the phenological requirements of overwintering crops. As quantified by digital refractometry, the 30‐day cumulative nitrogen release is suppressed to 9.22% at 10°C (crop dormancy) and increases 8.7‐fold to 80.32% at 30°C (active growth). This scalable technology overcomes intrinsic limitations of conventional diffusion‐controlled coated fertilizers and offers potential for on‐demand nutrient delivery with reduced environmental loss. Our work establishes sustainable strategies for climate‐adaptive precision agriculture and offers new insights and design paradigms for intelligent controlled‐release materials.","摘要 实现肥料养分释放与作物吸收的同步仍是精准农业的核心挑战。本研究提出一种新型温度响应型生物基控释肥料（TRBFs），在蓖麻油衍生聚氨酯包膜中引入泊洛沙姆407（F127）作为热开关。优化后的TRBF10（包膜率3 wt.%，包膜中F127含量10 wt.%）在约20°C时表现出急剧且可逆的相变，与越冬作物的物候需求相匹配。经数字折光法定量测定，30天累积氮释放量在10°C（作物休眠期）被抑制至9.22%，而在30°C（活跃生长期）增加8.7倍至80.32%。该可规模化技术克服了传统扩散控制型包膜肥料的内在局限，为按需养分供给并减少环境损失提供了潜力。本研究为气候适应性精准农业建立了可持续策略，并为智能控释材料提供了新见解与设计范式。","Advanced Science","2026-10-18T00:00:00Z",81,{"impact":134,"substance":135,"depth":134,"authority":136,"freshness":54,"relevant":21,"comment":137},18,22,14,"该研究提出温敏生物基控释肥，实现按需养分释放，方法新颖、数据扎实，对气候适应性精准农业有参考价值。",[139],{"name":130,"url":127},[26,29,141,142,143],"控释肥","温度响应","蓖麻油基材料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":147},{"candidates":148,"agricultural_evidence":47},[149,154,158],{"code":32,"score":136,"supported":47,"title":150,"abstract":151,"tags":153},[58],[152,58],"肥料",[],{"code":45,"score":17,"supported":47,"title":155,"abstract":156,"tags":157},[],[29,51],[26,29],{"code":159,"score":91,"supported":47,"title":160,"abstract":161,"tags":163},"H7",[],[162],"气候适应",[],[165,166],"Poloxamer 温敏控释肥","蓖麻油基材料 智慧农业 温度响应 精准农业","Poloxamer温敏控释肥-5327","10.1002\u002Fadvs.78371",{"doi":168,"openalex_id":170,"authors":171,"venue":130,"cited_by_count":112,"oa_url":127,"card":202,"direction":207,"ingested_from":120},"W7222872607",[172,174,177,179,181,184,186,188,190,193,196,199],{"name":173,"orcid":9},"Jiahui Liu",{"name":175,"orcid":176},"Li J","https:\u002F\u002Forcid.org\u002F0009-0008-8760-1365",{"name":178,"orcid":9},"Ziyao Li",{"name":180,"orcid":9},"Xiangjie Zhao",{"name":182,"orcid":183},"Fengyan Fang","https:\u002F\u002Forcid.org\u002F0009-0009-0081-4796",{"name":185,"orcid":9},"Yan Liu",{"name":187,"orcid":9},"Songyuan Wang",{"name":189,"orcid":9},"Boning Su",{"name":191,"orcid":192},"Yuanyuan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-7439-2970",{"name":194,"orcid":195},"Bin Gao","https:\u002F\u002Forcid.org\u002F0000-0003-3769-0191",{"name":197,"orcid":198},"Shugang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1428-9082",{"name":200,"orcid":201},"Yuechao Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4045-0252",{"tldr":203,"method":204,"finding":205,"direction":118,"opportunity":206},"研发含F127温敏开关的生物基包膜控释肥，实现按温度按需释放养分。","蓖麻油基聚氨酯包膜中嵌入Poloxamer 407作热开关，数字折光法测氮释放。","10℃时30天氮累积释放仅9.22%，30℃时增至80.32%，提升8.7倍。","可探索多响应（温湿\u002FpH）智能包膜与田间作物需肥曲线耦合的精准释放调控。","数字乡村与农业信息化","2026-10-19T23:30:28.317764Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":224,"tags":226,"primary_topic":231,"secondary_topics":236,"classification":238,"search_phrases":270,"slug":273,"view_count":21,"doi":274,"paper":275,"created_at":288},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":219,"substance":220,"depth":221,"authority":222,"freshness":54,"relevant":21,"comment":223},12,20,17,13,"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。",[225],{"name":215,"url":212},[26,227,228,29,229,230],"变量施肥","玉米","遥感监测","养分管理",{"code":232,"slug":233,"name":228,"path":234,"parent_code":235},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[237],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":239},{"candidates":240,"agricultural_evidence":47},[241,246,251,256,260,266],{"code":45,"score":242,"supported":47,"title":243,"abstract":244,"tags":245},16,[51],[29,51],[26,29],{"code":232,"score":247,"supported":47,"title":248,"abstract":249,"tags":250},15,[233],[228,233],[228],{"code":32,"score":252,"supported":47,"title":253,"abstract":254,"tags":255},11,[],[57,152,230],[230,227],{"code":76,"score":18,"supported":47,"title":257,"abstract":258,"tags":259},[],[79,80],[],{"code":261,"score":91,"supported":47,"title":262,"abstract":263,"tags":265},"I2",[],[264],"机器学习",[],{"code":97,"score":21,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[229],[271,272],"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":274,"openalex_id":276,"authors":277,"venue":215,"cited_by_count":112,"oa_url":212,"card":282,"direction":286,"ingested_from":120},"W7221366291",[278,280],{"name":279,"orcid":9},"Piyali Pal",{"name":281,"orcid":9},"Disha Chattopadhyay",{"tldr":283,"method":284,"finding":285,"direction":286,"opportunity":287},"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":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":295,"source_url":292,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":297,"score_detail":298,"sources":300,"tags":302,"primary_topic":306,"secondary_topics":307,"classification":308,"search_phrases":337,"slug":340,"view_count":105,"doi":9,"paper":341,"created_at":357},4669,"REVIEW ON RENEWABLE ENERGY-BASED SMART FERTIGATION CONTROLLER USING NPK SENSOR","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F302","Agriculture will require efficient management of water, fertilizers, and energy to improve crop productivity and support sustainable farming practices. Traditional fertilizer application methods will often depend on manual observation and fixed schedules, which will lead to improper nutrient management, nutrient imbalance, and unnecessary fertilizer usage. Smart fertigation systems will provide an effective approach for addressing these limitations by integrating IoT, soil nutrient sensing, and real-time monitoring technologies. In this review, NPK-based soil monitoring and IoT-enabled fertigation approaches will be studied with emphasis on nitrogen, phosphorus, and potassium monitoring for precision agriculture. The proposed system will use an ESP32 microcontroller, NPK sensor, soil moisture sensor, and DHT11 sensor to monitor soil nutrient and environmental conditions in real time. The collected data will be displayed on an LCD and transmitted through Wi-Fi to the Blynk IoT platform for remote monitoring. Based on real-time NPK readings, the fertilizer requirement will be determined, and the appropriate fertilizer-supply motor will be manually operated. Solar energy will be integrated with a solar charge controller and rechargeable battery to reduce dependence on conventional electricity and support sustainable operation. The reviewed approaches will indicate that the integration of NPK sensors, IoT monitoring, fertigation, and renewable energy will provide a promising solution for improving fertilizer utilization, reducing resource wastage, minimizing manual effort, and supporting sustainable precision agriculture.","农业需要高效管理水、肥料和能源，以提高作物生产力并支持可持续农业实践。传统施肥方法往往依赖人工观察和固定日程，这会导致养分管理不当、养分失衡和不必要的肥料使用。智能水肥一体化系统通过整合物联网（IoT）、土壤养分传感和实时监测技术，为解决这些局限提供了一种有效途径。本文综述了基于氮磷钾（NPK）的土壤监测和物联网水肥一体化方法，重点关注精准农业中的氮、磷、钾监测。所提出的系统将使用ESP32微控制器、NPK传感器、土壤湿度传感器和DHT11传感器实时监测土壤养分和环境条件。采集的数据将显示在LCD上，并通过Wi-Fi传输至Blynk物联网平台进行远程监测。根据实时NPK读数确定肥料需求量，并手动操作相应的供肥电机。将太阳能与太阳能充电控制器和可充电电池集成，以减少对传统电力的依赖并支持可持续运行。综述的方法表明，将NPK传感器、物联网监测、水肥一体化和可再生能源相结合，为提高肥料利用率、减少资源浪费、降低人工投入和支持可持续精准农业提供了一种有前景的解决方案。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",46,{"impact":17,"substance":136,"depth":219,"authority":18,"freshness":18,"relevant":21,"comment":299},"综述性论文，提出基于ESP32与NPK传感器的太阳能智能水肥一体化方案，有一定技术参考价值，但属常规技术集成，非重大突破，且期刊权威性一般。",[301],{"name":295,"url":292},[26,303,29,304,305],"物联网","水肥一体化","可再生能源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":309},{"candidates":310,"agricultural_evidence":47},[311,316,320,327,331],{"code":32,"score":134,"supported":47,"title":312,"abstract":314,"tags":315},[313],"fertigation",[57,152,304,230,58,313],[304],{"code":45,"score":17,"supported":47,"title":317,"abstract":318,"tags":319},[],[29,51],[26,29],{"code":321,"score":322,"supported":47,"title":323,"abstract":324,"tags":326},"I5",7,[],[303,325],"IoT",[303],{"code":76,"score":18,"supported":47,"title":328,"abstract":329,"tags":330},[],[79,80],[],{"code":332,"score":91,"supported":47,"title":333,"abstract":334,"tags":336},"H8",[],[335],"可持续农业",[],[338,339],"ESP32 NPK传感器 智能灌溉","太阳能 水肥一体化 控制器","ESP32NPK传感器智能灌溉-4669",{"doi":9,"openalex_id":342,"authors":343,"venue":295,"cited_by_count":112,"oa_url":292,"card":352,"direction":118,"ingested_from":120},"W7221368129",[344,346,348,350],{"name":345,"orcid":9},"Niharika Bodake",{"name":347,"orcid":9},"Akshata Gaurkar",{"name":349,"orcid":9},"Vaishnavi Chavan",{"name":351,"orcid":9},"S. K. Godase",{"tldr":353,"method":354,"finding":355,"direction":118,"opportunity":356},"综述基于NPK传感器与物联网的可再生能源智能水肥一体化控制器方案。","ESP32、NPK\u002F土壤湿度\u002FDHT11传感器、Blynk平台与太阳能供电。","NPK传感+物联网+水肥一体化+可再生能源可提升肥料利用率并减少浪费。","肥料供给仍靠手动，可研究基于实时NPK的自动决策与闭环变量施肥控制。","2026-10-11T23:30:16.034599Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":361,"published_at":365,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":366,"score_detail":367,"sources":369,"tags":371,"primary_topic":375,"secondary_topics":380,"classification":387,"search_phrases":427,"slug":430,"view_count":20,"doi":431,"paper":432,"created_at":455},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",69,{"impact":219,"substance":220,"depth":242,"authority":222,"freshness":17,"relevant":21,"comment":368},"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。",[370],{"name":364,"url":361},[26,372,29,373,374],"农业人工智能","遥感","高粱",{"code":376,"slug":377,"name":378,"path":379,"parent_code":235},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[381,386],{"code":261,"slug":382,"name":383,"path":384,"parent_code":385},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":388},{"candidates":389,"agricultural_evidence":47},[390,395,400,404,409,413,417,423],{"code":376,"score":247,"supported":47,"title":391,"abstract":393,"tags":394},[392],"sorghum",[374,392],[374],{"code":261,"score":247,"supported":47,"title":396,"abstract":398,"tags":399},[397],"machine learning",[264,397],[372],{"code":32,"score":54,"supported":47,"title":401,"abstract":402,"tags":403},[],[57,230,58],[],{"code":83,"score":54,"supported":47,"title":405,"abstract":406,"tags":408},[],[86,407,87],"水资源",[],{"code":45,"score":17,"supported":47,"title":410,"abstract":411,"tags":412},[],[29,51],[26,29],{"code":76,"score":18,"supported":47,"title":414,"abstract":415,"tags":416},[],[79,80],[],{"code":418,"score":91,"supported":47,"title":419,"abstract":420,"tags":422},"A7",[],[421],"间作",[],{"code":97,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[373],[428,429],"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":431,"openalex_id":433,"authors":434,"venue":364,"cited_by_count":112,"oa_url":361,"card":449,"direction":286,"ingested_from":120},"W7214994063",[435,437,440,442,445,447],{"name":436,"orcid":9},"Anoop Valiya Veettil",{"name":438,"orcid":439},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":441,"orcid":9},"Ali Fares",{"name":443,"orcid":444},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":446,"orcid":9},"Almoutaz Elhassan",{"name":448,"orcid":9},"Atikur Rahman",{"tldr":450,"method":451,"finding":452,"direction":453,"opportunity":454},"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。","2026-10-01T23:30:54.379150Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":9,"content":9,"source_name":461,"source_url":459,"published_at":462,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":366,"score_detail":463,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":471,"classification":477,"search_phrases":501,"slug":504,"view_count":18,"doi":505,"paper":506,"created_at":522},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture","Precision Agriculture","2026-09-25T00:00:00Z",{"impact":219,"substance":134,"depth":221,"authority":136,"freshness":17,"relevant":21,"comment":464},"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[466],{"name":461,"url":459},[26,227,228,29,468,469],"向日葵","变量播种",{"code":232,"slug":233,"name":228,"path":234,"parent_code":235},[472,473],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":76,"slug":80,"name":474,"path":475,"parent_code":476},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":478},{"candidates":479,"agricultural_evidence":47},[480,484,488,492,496],{"code":232,"score":219,"supported":47,"title":481,"abstract":482,"tags":483},[233],[233],[228],{"code":32,"score":219,"supported":47,"title":485,"abstract":486,"tags":487},[59],[59],[227],{"code":76,"score":252,"supported":47,"title":489,"abstract":490,"tags":491},[80],[80],[],{"code":45,"score":19,"supported":47,"title":493,"abstract":494,"tags":495},[],[51],[26,29],{"code":497,"score":21,"supported":14,"title":498,"abstract":499,"tags":500},"A4",[],[],[468],[502,503],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523","10.1007\u002Fs11119-026-10458-y",{"doi":505,"openalex_id":507,"authors":508,"venue":461,"cited_by_count":112,"oa_url":9,"card":9,"direction":9,"ingested_from":120},"W7214357812",[509,512,514,516,518,520],{"name":510,"orcid":511},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":513,"orcid":9},"J. A. Martínez-Casasnovas",{"name":515,"orcid":9},"S. Artero",{"name":517,"orcid":9},"A. Vigo",{"name":519,"orcid":9},"M. Vidal",{"name":521,"orcid":9},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z",{"id":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":9,"source_name":529,"source_url":526,"published_at":530,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":531,"score_detail":532,"sources":534,"tags":536,"primary_topic":539,"secondary_topics":545,"classification":548,"search_phrases":592,"slug":595,"view_count":105,"doi":596,"paper":597,"created_at":607},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",72,{"impact":134,"substance":135,"depth":134,"authority":219,"freshness":105,"relevant":21,"comment":533},"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[535],{"name":529,"url":526},[26,537,27,264,538],"精准施肥","小农户",{"code":540,"slug":541,"name":542,"path":543,"parent_code":544},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[546,547],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":261,"slug":382,"name":383,"path":384,"parent_code":385},{"status":39,"locked":14,"version":40,"method":41,"evidence":549},{"candidates":550,"agricultural_evidence":47},[551,556,560,565,569,574,578,582,586],{"code":540,"score":247,"supported":47,"title":552,"abstract":554,"tags":555},[553],"smallholder",[538,553],[538],{"code":32,"score":13,"supported":47,"title":557,"abstract":558,"tags":559},[],[57,230,58],[537],{"code":261,"score":13,"supported":47,"title":561,"abstract":562,"tags":564},[],[264,397,563],"decision support",[264],{"code":45,"score":322,"supported":47,"title":566,"abstract":567,"tags":568},[],[29,51],[26],{"code":97,"score":322,"supported":47,"title":570,"abstract":571,"tags":573},[],[373,572],"remote sensing",[27],{"code":62,"score":18,"supported":47,"title":575,"abstract":576,"tags":577},[],[65,66],[],{"code":76,"score":18,"supported":47,"title":579,"abstract":580,"tags":581},[],[79,80],[],{"code":321,"score":18,"supported":47,"title":583,"abstract":584,"tags":585},[],[303,325],[],{"code":587,"score":91,"supported":47,"title":588,"abstract":589,"tags":591},"H4",[],[590],"carbon sequestration",[],[593,594],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":596,"openalex_id":598,"authors":599,"venue":529,"cited_by_count":112,"oa_url":526,"card":602,"direction":118,"ingested_from":120},"W7211915396",[600],{"name":601,"orcid":9},"Anuga Liyanage",{"tldr":603,"method":604,"finding":605,"direction":118,"opportunity":606},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z"]