[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3371":3,"related-3371":129},{"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":90,"slug":93,"view_count":21,"doi":94,"paper":95,"created_at":128},3371,"Optimizing nitrogen fertilization in mustard through GreenSeeker-based precision agriculture: Impacts on productivity, economics, and environmental sustainability","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0358762","Excessive nitrogen (N) fertilization is a major challenge to sustainable agriculture, adversely affecting crop productivity, soil quality, and environmental health, emits a potent greenhouse gas nitrous oxide (N 2 O), and nearly 80% of sectoral emissions linked to nutrient inputs applied during crop production. The escalating use of N fertilizers further amplifies N 2 O emissions, undermining soil sustainability and contributing to environmental degradation. So, the hypothesis used behind the study is increasing nitrogen use efficiency from its current level (approx. 30–40%) would reduce the need for N fertilizer requirement, resulting in more cost-effective and environmentally sustainable production system with no yield penalty. With this objective, a field study was executed in rabi 2021–2024, consisting of nine N management treatments and one unfertilized treatment as control, randomized in complete block design in three replicates. To overcome this problem, first we standardized NDVI based N application and developed a Nitrogen Estimation Rate Chart (NERC) for precision N management in real time for specific set target yield. Results showed that sensor based N application enhanced growth and yield attributes, registered 22.75% seed yield enhancement with 18.7% N saving over RDF and 34.4 & 30.2% increment in NMR and BCR. This treatment augmented AE N and PFP N by 14.18 to 124.7% and 1.78 to 51.1%, respectively achieved higher NRE (35–69.5%). The per cent enrichment in SOC content was found superior in RDN 100 + 2% Urea FS (52.6%) followed by RDN 100 + 1.5% KNO 3 FS (39.4%) statistically comparable to RDF (38.1%). The fertilizers contributed largest share of total carbon emissions, accounting for 45.6–52.3%. NDVI based N management reduced GHGs emissions by approximately 11.23% over RDF. The mean biological yield (seed + stover) unveiled positive correlation to total nitrogen (r 2 = 98), total phosphorus (r 2 = 95) and total potassium (r 2 = 98) uptake. This innovative technology enables farmers to prevent excessive N fertilization, thereby reducing resource wastage and minimizing nitrous oxide emission. This technology provides insight on N need of crop in real time in right quantity.","过量施氮是可持续农业面临的一项重大挑战，会对作物生产力、土壤质量和环境健康产生不利影响，并排放强效温室气体氧化亚氮（N₂O），而该领域近80%的排放与作物生产过程中投入的养分有关。氮肥用量的不断攀升进一步加剧了N₂O排放，削弱土壤可持续性并导致环境退化。因此，本研究提出的假设是：将氮利用效率从当前水平（约30%–40%）提高，可减少对氮肥的需求，从而在不造成产量损失的前提下，实现更具成本效益和环境可持续性的生产体系。基于此目标，于2021—2024年rabi季开展了一项田间研究，包括9个氮管理处理和1个不施肥对照处理，采用完全随机区组设计，3次重复。为解决上述问题，首先标准化了基于NDVI的氮肥施用方法，并开发了氮估算速率表（NERC），用于针对特定目标产量进行实时精准氮管理。结果表明，基于传感器的氮肥施用促进了生长和产量性状，种子产量较推荐施肥量（RDF）提高22.75%，节省氮肥18.7%，净收益（NMR）和效益成本比（BCR）分别提高34.4%和30.2%。该处理使氮农学效率（AE N）和氮偏生产力（PFP N）分别提高14.18%–124.7%和1.78%–51.1%，并实现了更高的氮回收效率（NRE，35%–69.5%）。土壤有机碳（SOC）含量的提升幅度以RDN 100 + 2%尿素叶面喷施（52.6%）最优，其次为RDN 100 + 1.5% KNO₃叶面喷施（39.4%），与RDF（38.1%）在统计上相当。肥料对总碳排放的贡献最大，占45.6%–52.3%。基于NDVI的氮管理较RDF减少温室气体排放约11.23%。平均生物产量（种子+秸秆）与总氮（r² = 98）、总磷（r² = 95）和总钾（r² = 98）吸收量呈正相关。这项创新技术使农民能够避免过量施氮，从而减少资源浪费并降低氧化亚氮排放。该技术可实时提供作物氮需求信息，并指导适宜用量的施用。",null,"PLoS ONE","2026-09-23T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于NDVI传感器的实时精准施氮研究，四年田间试验数据扎实，兼具增产、节肥与减排价值，对智慧农业施肥决策有参考意义。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准施肥","氮肥管理","NDVI遥感","绿色低碳农业",{"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":46},[44,54,61,69,76,83],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":53},19,true,[48],"fertilization",[50,51,52,48],"施肥","肥料","fertilizer",[28,27],{"code":55,"score":56,"supported":46,"title":57,"abstract":59,"tags":60},"I1",9,[58],"precision agriculture",[],[26],{"code":62,"score":63,"supported":46,"title":64,"abstract":65,"tags":68},"B7",6,[],[66,67],"温室","greenhouse",[],{"code":70,"score":63,"supported":46,"title":71,"abstract":72,"tags":75},"H2",[],[73,74],"土壤","soil",[],{"code":77,"score":63,"supported":46,"title":78,"abstract":79,"tags":82},"H8",[],[80,81],"可持续农业","sustainable agriculture",[],{"code":84,"score":85,"supported":46,"title":86,"abstract":87,"tags":89},"H4",3,[],[88],"碳排放",[],[91,92],"GreenSeeker NDVI 氮肥管理","芥菜型油菜 精准施氮","GreenSeekerNDVI氮肥管理-3371","10.1371\u002Fjournal.pone.0358762",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":119,"oa_url":6,"card":120,"direction":126,"ingested_from":127},"W7214115167",[98,101,103,105,107,109,111,113,115,117],{"name":99,"orcid":100},"V. D. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-9528-4832",{"name":102,"orcid":9},"M.L. Dotaniya",{"name":104,"orcid":9},"M.D. Meena",{"name":106,"orcid":9},"R. S. Jat",{"name":108,"orcid":9},"MK Meena",{"name":110,"orcid":9},"R. L. Choudhary",{"name":112,"orcid":9},"H. V. Singh",{"name":114,"orcid":9},"HS MEENA",{"name":116,"orcid":9},"B.L. Meena",{"name":118,"orcid":9},"V. V. Singh",0,{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"基于GreenSeeker的NDVI实时氮肥管理优化芥菜施氮，提升产量并减少氮肥与温室气体排放。","2021-2024年田间试验，9个氮处理+对照，用GreenSeeker NDV","传感器施氮使种子产量增22.75%、节氮18.7%，NRE达35-69.5%，温室气体减排约11.2","智慧农业 \u002F 农业物联网","可将NDVI实时氮管理扩展至其他作物与区域，并结合碳足迹模型量化减排经济价值。","农业绿色发展与碳","openalex","2026-09-24T23:30:41.022035Z",{"total":63,"page":21,"page_size":63,"items":130},[131,209,296,377,522,586],{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":138,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":145,"tags":147,"primary_topic":150,"secondary_topics":151,"classification":157,"search_phrases":188,"slug":191,"view_count":192,"doi":193,"paper":194,"created_at":208},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":141,"substance":142,"depth":143,"authority":63,"freshness":20,"relevant":21,"comment":144},12,20,17,"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。",[146],{"name":137,"url":134},[26,148,27,149,28],"农业人工智能","水稻",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[152],{"code":153,"slug":154,"name":149,"path":155,"parent_code":156},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",{"status":39,"locked":14,"version":40,"method":41,"evidence":158},{"candidates":159,"agricultural_evidence":46},[160,165,171,177,184],{"code":32,"score":45,"supported":46,"title":161,"abstract":162,"tags":164},[52],[50,51,163,52],"养分管理",[28,27],{"code":153,"score":13,"supported":46,"title":166,"abstract":167,"tags":170},[],[149,168,154,169],"稻田","paddy",[149],{"code":55,"score":172,"supported":46,"title":173,"abstract":174,"tags":176},7,[],[175,58],"精准农业",[26],{"code":178,"score":172,"supported":46,"title":179,"abstract":180,"tags":183},"I2",[],[181,182],"深度学习","deep learning",[148],{"code":70,"score":63,"supported":46,"title":185,"abstract":186,"tags":187},[],[73,74],[],[189,190],"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",2,"10.70818\u002Fijbass.2026.v07i02.0075",{"doi":193,"openalex_id":195,"authors":196,"venue":137,"cited_by_count":119,"oa_url":134,"card":202,"direction":206,"ingested_from":127},"W7214974109",[197,199],{"name":198,"orcid":9},"Rusanara Akhter",{"name":200,"orcid":201},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":203,"method":204,"finding":205,"direction":206,"opportunity":207},"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","农业人工智能与决策模型","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":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":220,"tags":222,"primary_topic":227,"secondary_topics":228,"classification":229,"search_phrases":278,"slug":281,"view_count":192,"doi":282,"paper":283,"created_at":295},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":18,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":219},"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。",[221],{"name":215,"url":212},[26,223,27,224,225,226],"无人机","变量施肥","遥感监测","养分利用效率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":230},{"candidates":231,"agricultural_evidence":46},[232,238,245,249,256,263,267,274],{"code":32,"score":142,"supported":46,"title":233,"abstract":234,"tags":237},[48],[50,51,235,163,236],"水肥一体化","fertigation",[226,224,27],{"code":239,"score":172,"supported":46,"title":240,"abstract":241,"tags":244},"I3",[],[242,243],"遥感","remote sensing",[225],{"code":153,"score":63,"supported":46,"title":246,"abstract":247,"tags":248},[],[149,154],[],{"code":250,"score":63,"supported":46,"title":251,"abstract":252,"tags":255},"A2",[],[253,254],"小麦","wheat",[],{"code":257,"score":63,"supported":46,"title":258,"abstract":259,"tags":262},"A3",[],[260,261],"玉米","maize",[],{"code":70,"score":63,"supported":46,"title":264,"abstract":265,"tags":266},[],[73,74],[],{"code":268,"score":63,"supported":46,"title":269,"abstract":270,"tags":273},"I4",[],[271,272],"地理信息","geographic information",[],{"code":55,"score":21,"supported":14,"title":275,"abstract":276,"tags":277},[],[],[26],[279,280],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235","10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":282,"openalex_id":284,"authors":285,"venue":215,"cited_by_count":119,"oa_url":212,"card":290,"direction":124,"ingested_from":127},"W7222838957",[286,288],{"name":287,"orcid":9},"S. T. Panusha",{"name":289,"orcid":9},"T. M. Soumya",{"tldr":291,"method":292,"finding":293,"direction":124,"opportunity":294},"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","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":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":9,"source_name":302,"source_url":299,"published_at":303,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":304,"score_detail":305,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":315,"classification":326,"search_phrases":356,"slug":359,"view_count":119,"doi":9,"paper":360,"created_at":376},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":141,"substance":306,"depth":307,"authority":141,"freshness":63,"relevant":21,"comment":308},16,15,"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。",[310],{"name":302,"url":299},[26,148,27,312,313],"农业物联网","智能农机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[316,321],{"code":178,"slug":317,"name":318,"path":319,"parent_code":320},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":322,"slug":323,"name":324,"path":325,"parent_code":320},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":327},{"candidates":328,"agricultural_evidence":46},[329,333,340,347,351],{"code":32,"score":307,"supported":46,"title":330,"abstract":331,"tags":332},[52],[51,52],[27],{"code":178,"score":13,"supported":46,"title":334,"abstract":335,"tags":339},[],[336,337,338],"机器学习","machine learning","artificial intelligence",[148],{"code":322,"score":13,"supported":46,"title":341,"abstract":342,"tags":346},[],[343,344,345],"物联网","internet of things","IoT",[312],{"code":55,"score":172,"supported":46,"title":348,"abstract":349,"tags":350},[],[175,58],[26],{"code":352,"score":21,"supported":14,"title":353,"abstract":354,"tags":355},"F5",[],[],[313],[357,358],"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":361,"authors":362,"venue":302,"cited_by_count":119,"oa_url":299,"card":371,"direction":124,"ingested_from":127},"W7221394563",[363,365,367,369],{"name":364,"orcid":9},"Anjali Manoj Kumbhar",{"name":366,"orcid":9},"Shruti Prashant Kulkarni",{"name":368,"orcid":9},"Snehal Santosh Khune",{"name":370,"orcid":9},"B. B. Godbole",{"tldr":372,"method":373,"finding":374,"direction":124,"opportunity":375},"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":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":392,"secondary_topics":394,"classification":401,"search_phrases":461,"slug":464,"view_count":85,"doi":465,"paper":466,"created_at":521},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":17,"substance":18,"depth":17,"authority":19,"freshness":56,"relevant":21,"comment":387},"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。",[389],{"name":383,"url":380},[26,27,253,391,235],"保护性耕作",{"code":250,"slug":254,"name":253,"path":393,"parent_code":156},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[395,396],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":77,"slug":397,"name":398,"path":399,"parent_code":400},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":402},{"candidates":403,"agricultural_evidence":46},[404,408,412,417,421,428,432,439,445,451,457],{"code":32,"score":306,"supported":46,"title":405,"abstract":406,"tags":407},[236],[50,236],[235,27],{"code":250,"score":307,"supported":46,"title":409,"abstract":410,"tags":411},[254],[253,254],[253],{"code":77,"score":141,"supported":46,"title":413,"abstract":415,"tags":416},[414],"conservation agriculture",[414],[391],{"code":257,"score":63,"supported":46,"title":418,"abstract":419,"tags":420},[],[260,261],[],{"code":422,"score":63,"supported":46,"title":423,"abstract":424,"tags":427},"A8",[],[425,426],"粮食安全","food security",[],{"code":70,"score":63,"supported":46,"title":429,"abstract":430,"tags":431},[],[73,74],[],{"code":433,"score":63,"supported":46,"title":434,"abstract":435,"tags":438},"H3",[],[436,437],"灌溉","irrigation",[],{"code":440,"score":85,"supported":46,"title":441,"abstract":442,"tags":444},"A7",[],[443],"轮作",[],{"code":446,"score":85,"supported":46,"title":447,"abstract":448,"tags":450},"H5",[],[449],"秸秆还田",[],{"code":452,"score":85,"supported":46,"title":453,"abstract":454,"tags":456},"H7",[],[455],"干旱",[],{"code":55,"score":21,"supported":14,"title":458,"abstract":459,"tags":460},[],[],[26],[462,463],"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":465,"openalex_id":467,"authors":468,"venue":383,"cited_by_count":119,"oa_url":380,"card":516,"direction":124,"ingested_from":127},"W7214579192",[469,471,474,476,478,480,482,485,487,489,491,493,495,498,500,502,505,508,511,514],{"name":470,"orcid":9},"Kadagonda Nithinkumar",{"name":472,"orcid":473},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":475,"orcid":9},"Devika A. R.",{"name":477,"orcid":9},"Arjun Singh",{"name":479,"orcid":9},"Susama Sudhishri",{"name":481,"orcid":9},"Vinod Kumar Singh",{"name":483,"orcid":484},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":486,"orcid":9},"Sarvendra Kumar",{"name":488,"orcid":9},"Teekam Singh",{"name":490,"orcid":9},"Jitendra Rajput",{"name":492,"orcid":9},"Pravin Kumar Upadhyaya",{"name":494,"orcid":9},"R. N. Sahoo",{"name":496,"orcid":497},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":499,"orcid":9},"Aye Aye San",{"name":501,"orcid":9},"K. S. Sachin",{"name":503,"orcid":504},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":506,"orcid":507},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":509,"orcid":510},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":512,"orcid":513},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":515,"orcid":9},"Subhash Babu",{"tldr":517,"method":518,"finding":519,"direction":124,"opportunity":520},"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",{"id":523,"title":524,"url":525,"summary":526,"summary_zh":527,"content":9,"source_name":528,"source_url":525,"published_at":529,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":304,"score_detail":530,"sources":532,"tags":534,"primary_topic":537,"secondary_topics":540,"classification":542,"search_phrases":568,"slug":571,"view_count":192,"doi":572,"paper":573,"created_at":585},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",{"impact":141,"substance":17,"depth":307,"authority":20,"freshness":20,"relevant":21,"comment":531},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[533],{"name":528,"url":525},[26,148,27,535,536],"土壤健康","养分流失",{"code":70,"slug":74,"name":538,"path":539,"parent_code":400},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[541],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":543},{"candidates":544,"agricultural_evidence":46},[545,549,553,557,562],{"code":70,"score":307,"supported":46,"title":546,"abstract":547,"tags":548},[74],[73,74],[535],{"code":55,"score":307,"supported":46,"title":550,"abstract":551,"tags":552},[58],[175,58],[26],{"code":32,"score":13,"supported":46,"title":554,"abstract":555,"tags":556},[],[50,51,163,52],[27],{"code":178,"score":558,"supported":46,"title":559,"abstract":560,"tags":561},4,[],[338],[148],{"code":563,"score":85,"supported":46,"title":564,"abstract":565,"tags":567},"I6",[],[566],"agricultural data",[],[569,570],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":572,"openalex_id":574,"authors":575,"venue":528,"cited_by_count":119,"oa_url":525,"card":580,"direction":206,"ingested_from":127},"W7214553198",[576,578],{"name":577,"orcid":9},"Olufisayo Obebe",{"name":579,"orcid":9},"Ebenezer Akinola",{"tldr":581,"method":582,"finding":583,"direction":206,"opportunity":584},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":587,"title":588,"url":589,"summary":590,"summary_zh":591,"content":9,"source_name":592,"source_url":589,"published_at":593,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":594,"score_detail":595,"sources":597,"tags":599,"primary_topic":601,"secondary_topics":602,"classification":604,"search_phrases":627,"slug":630,"view_count":85,"doi":631,"paper":632,"created_at":645},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",62,{"impact":141,"substance":17,"depth":143,"authority":63,"freshness":56,"relevant":21,"comment":596},"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[598],{"name":592,"url":589},[26,148,27,600,235],"可解释AI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[603],{"code":178,"slug":317,"name":318,"path":319,"parent_code":320},{"status":39,"locked":14,"version":40,"method":41,"evidence":605},{"candidates":606,"agricultural_evidence":46},[607,611,615,619,623],{"code":32,"score":45,"supported":46,"title":608,"abstract":609,"tags":610},[236],[50,51,52,236],[235,27],{"code":178,"score":19,"supported":46,"title":612,"abstract":613,"tags":614},[338],[336],[148,600],{"code":55,"score":56,"supported":46,"title":616,"abstract":617,"tags":618},[58],[],[26],{"code":433,"score":63,"supported":46,"title":620,"abstract":621,"tags":622},[],[436,437],[],{"code":322,"score":63,"supported":46,"title":624,"abstract":625,"tags":626},[],[343,345],[],[628,629],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":631,"openalex_id":633,"authors":634,"venue":592,"cited_by_count":119,"oa_url":639,"card":640,"direction":124,"ingested_from":127},"W7212934364",[635,637],{"name":636,"orcid":9},"Shraddha S. Tayade",{"name":638,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":641,"method":642,"finding":643,"direction":206,"opportunity":644},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z"]