[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3680":3,"related-3680":107},{"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":44,"search_phrases":85,"slug":88,"view_count":89,"doi":90,"paper":91,"created_at":106},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。",null,"International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":22},12,18,15,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准施肥","土壤健康","养分流失",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,56,63,71,78],{"code":32,"score":19,"supported":51,"title":52,"abstract":53,"tags":55},true,[33],[54,33],"土壤",[29],{"code":57,"score":19,"supported":51,"title":58,"abstract":60,"tags":62},"I1",[59],"precision agriculture",[61,59],"精准农业",[26],{"code":39,"score":13,"supported":51,"title":64,"abstract":65,"tags":70},[],[66,67,68,69],"施肥","肥料","养分管理","fertilizer",[28],{"code":72,"score":73,"supported":51,"title":74,"abstract":75,"tags":77},"I2",4,[],[76],"artificial intelligence",[27],{"code":79,"score":80,"supported":51,"title":81,"abstract":82,"tags":84},"I6",3,[],[83],"agricultural data",[],[86,87],"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",2,"10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":103,"ingested_from":105},"W7214553198",[94,96],{"name":95,"orcid":9},"Olufisayo Obebe",{"name":97,"orcid":9},"Ebenezer Akinola",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"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。","农业人工智能与决策模型","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。","openalex","2026-09-28T23:30:49.535963Z",{"total":108,"page":21,"page_size":108,"items":109},6,[110,186,312,391,502,567],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":9,"content":9,"source_name":115,"source_url":113,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":129,"secondary_topics":134,"classification":137,"search_phrases":164,"slug":167,"view_count":108,"doi":168,"paper":169,"created_at":185},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",69,{"impact":17,"substance":18,"depth":119,"authority":120,"freshness":20,"relevant":21,"comment":121},17,14,"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。",[123],{"name":115,"url":113},[26,125,126,61,127,128],"变量施肥","玉米","向日葵","变量播种",{"code":130,"slug":131,"name":126,"path":132,"parent_code":133},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[135,136],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":138},{"candidates":139,"agricultural_evidence":51},[140,144,149,154,159],{"code":130,"score":17,"supported":51,"title":141,"abstract":142,"tags":143},[131],[131],[126],{"code":39,"score":17,"supported":51,"title":145,"abstract":147,"tags":148},[146],"fertilization",[146],[125],{"code":32,"score":150,"supported":51,"title":151,"abstract":152,"tags":153},11,[33],[33],[],{"code":57,"score":155,"supported":51,"title":156,"abstract":157,"tags":158},5,[],[59],[26,61],{"code":160,"score":21,"supported":14,"title":161,"abstract":162,"tags":163},"A4",[],[],[127],[165,166],"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":168,"openalex_id":170,"authors":171,"venue":115,"cited_by_count":98,"oa_url":9,"card":9,"direction":9,"ingested_from":105},"W7214357812",[172,175,177,179,181,183],{"name":173,"orcid":174},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":176,"orcid":9},"J. A. Martínez-Casasnovas",{"name":178,"orcid":9},"S. Artero",{"name":180,"orcid":9},"A. Vigo",{"name":182,"orcid":9},"M. Vidal",{"name":184,"orcid":9},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":51,"score":194,"score_detail":195,"sources":199,"tags":201,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":277,"slug":280,"view_count":98,"doi":281,"paper":282,"created_at":311},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":196,"substance":197,"depth":18,"authority":120,"freshness":20,"relevant":21,"comment":198},22,21,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[200],{"name":192,"url":189},[26,27,202,61,29,203],"种业振兴","多组学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":207},{"candidates":208,"agricultural_evidence":51},[209,215,220,227,234,238,245,252,259,263,267,272],{"code":57,"score":210,"supported":51,"title":211,"abstract":212,"tags":214},19,[59],[61,213,59],"数字孪生",[26,61],{"code":32,"score":216,"supported":51,"title":217,"abstract":218,"tags":219},7,[],[54,33],[29],{"code":221,"score":108,"supported":51,"title":222,"abstract":223,"tags":226},"A1",[],[224,225],"水稻","rice",[],{"code":228,"score":108,"supported":51,"title":229,"abstract":230,"tags":233},"A2",[],[231,232],"小麦","wheat",[],{"code":130,"score":108,"supported":51,"title":235,"abstract":236,"tags":237},[],[126,131],[],{"code":239,"score":108,"supported":51,"title":240,"abstract":241,"tags":244},"A8",[],[242,243],"粮食安全","food security",[],{"code":246,"score":108,"supported":51,"title":247,"abstract":248,"tags":251},"H7",[],[249,250],"气候变化","climate change",[],{"code":253,"score":108,"supported":51,"title":254,"abstract":255,"tags":258},"H8",[],[256,257],"可持续农业","sustainable agriculture",[],{"code":72,"score":73,"supported":51,"title":260,"abstract":261,"tags":262},[],[76],[27],{"code":39,"score":80,"supported":51,"title":264,"abstract":265,"tags":266},[],[67],[],{"code":268,"score":21,"supported":14,"title":269,"abstract":270,"tags":271},"F1",[],[],[202],{"code":273,"score":21,"supported":14,"title":274,"abstract":275,"tags":276},"F2",[],[],[203],[278,279],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":281,"openalex_id":283,"authors":284,"venue":192,"cited_by_count":98,"oa_url":189,"card":305,"direction":310,"ingested_from":105},"W7221567543",[285,288,290,293,295,297,300,303],{"name":286,"orcid":287},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":289,"orcid":9},"Wang Qingxia",{"name":291,"orcid":292},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":294,"orcid":9},"Shuzhen Ye",{"name":296,"orcid":9},"Zhijuan Ji",{"name":298,"orcid":299},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":301,"orcid":302},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":304,"orcid":9},"Younan Ouyang",{"tldr":306,"method":307,"finding":308,"direction":103,"opportunity":309},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":318,"source_url":315,"published_at":319,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":320,"sources":323,"tags":325,"primary_topic":328,"secondary_topics":329,"classification":340,"search_phrases":369,"slug":372,"view_count":98,"doi":9,"paper":373,"created_at":390},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",{"impact":17,"substance":321,"depth":19,"authority":17,"freshness":108,"relevant":21,"comment":322},16,"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。",[324],{"name":318,"url":315},[26,27,28,326,327],"农业物联网","智能农机",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[330,335],{"code":72,"slug":331,"name":332,"path":333,"parent_code":334},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":336,"slug":337,"name":338,"path":339,"parent_code":334},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":341},{"candidates":342,"agricultural_evidence":51},[343,347,353,360,364],{"code":39,"score":19,"supported":51,"title":344,"abstract":345,"tags":346},[69],[67,69],[28],{"code":72,"score":13,"supported":51,"title":348,"abstract":349,"tags":352},[],[350,351,76],"机器学习","machine learning",[27],{"code":336,"score":13,"supported":51,"title":354,"abstract":355,"tags":359},[],[356,357,358],"物联网","internet of things","IoT",[326],{"code":57,"score":216,"supported":51,"title":361,"abstract":362,"tags":363},[],[61,59],[26],{"code":365,"score":21,"supported":14,"title":366,"abstract":367,"tags":368},"F5",[],[],[327],[370,371],"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":374,"authors":375,"venue":318,"cited_by_count":98,"oa_url":315,"card":384,"direction":388,"ingested_from":105},"W7221394563",[376,378,380,382],{"name":377,"orcid":9},"Anjali Manoj Kumbhar",{"name":379,"orcid":9},"Shruti Prashant Kulkarni",{"name":381,"orcid":9},"Snehal Santosh Khune",{"name":383,"orcid":9},"B. B. Godbole",{"tldr":385,"method":386,"finding":387,"direction":388,"opportunity":389},"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。","智慧农业 \u002F 农业物联网","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":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":405,"tags":407,"primary_topic":413,"secondary_topics":414,"classification":416,"search_phrases":446,"slug":449,"view_count":80,"doi":450,"paper":451,"created_at":501},4292,"Agronomic performance and soil chemical properties following the application of pelletized compost and vermicompost derived from vineyard pruning residues and sewage sludge in lettuce production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948663","Introduction Compost and vermicompost are recognized as effective organic amendments for supporting plant growth, promoting nutrient recycling and enhancing soil quality within circular economy strategies. In this context, the present study evaluated the short-term performance of pelletized compost (CC) and vermicompost (VC) produced from vineyard prunings and sewage sludge on lettuce ( Lactuca sativa L.) growth, leaf biochemical traits, mineral composition, and soil chemical properties. The performance of both organic products was compared with a commercial organomineral fertilizer (CF) and an unfertilized control (CT). Methods A completely randomized greenhouse pot experiment was conducted for 55 days comprising four treatments with eight replicates each. . Data were analyzed by one-way ANOVA followed by Tukey´s HSD test at p \u003C 0.05, with non-parametric analysis used when ANOVA assumptions were not met. Results and discussion At the end of the experiment, organic amendments performed comparably to the commercial fertilizer CF for most morphological parameters, despite differences in absolute nutrient inputs among treatments, although shoot fresh weight remained higher under CF. Leaf biochemical responses varied among treatments: CC and CF exhibited high chlorophyll and carotenoid contents, whereas VC and CT showed lower pigment concentrations. Total soluble sugars were highest in CT and lowest in CF, while total protein and phenolic contents were not significantly affected by treatment. Leaf macronutrient concentrations under CC and VC were generally comparable to CF, while for micronutrients Fe and Mn concentrations were higher in CF. Final soil analyses showed higher organic matter, extractable P and K, and cation exchange capacity under organic fertilization, whereas CF showed the lowest soil pH. Trace metal concentration remained low in all treatments. Conclusions These findings support the potential use of compost and vermicompost derived from vineyard pruning residues and sewage sludge as recycled organic amendments for horticultural production. However, further research is recommended to validate these results under long-term field conditions and diverse soil types.","引言 堆肥和蚯蚓粪被认为是有效的有机改良剂，可在循环经济策略中支持植物生长、促进养分循环并提升土壤质量。在此背景下，本研究评估了由葡萄园修剪枝条和污水污泥制成的颗粒化堆肥（CC）和蚯蚓粪（VC）对生菜（Lactuca sativa L.）生长、叶片生化性状、矿质组成及土壤化学性质的短期效果。并将这两种有机产品与一种商品化有机无机复混肥（CF）及未施肥对照（CT）进行了比较。方法 采用完全随机温室盆栽试验，持续55天，包括4个处理，每处理8次重复。数据采用单因素方差分析，随后进行Tukey HSD检验，显著性水平为p \u003C 0.05；当不满足方差分析假设时，采用非参数分析。结果与讨论 试验结束时，尽管各处理间绝对养分投入量存在差异，但有机改良剂在大多数形态指标上与商品化肥料CF表现相当，不过CF下地上部鲜重仍较高。叶片生化响应因处理而异：CC和CF表现出较高的叶绿素和类胡萝卜素含量，而VC和CT的色素浓度较低。可溶性总糖在CT中最高，在CF中最低，而总蛋白和酚类物质含量未受处理的显著影响。CC和VC下叶片大量元素浓度总体与CF相当，而微量元素Fe和Mn浓度在CF中较高。最终土壤分析显示，有机施肥下有机质、可提取P和K以及阳离子交换量较高，而CF下土壤pH最低。所有处理中痕量金属浓度均保持较低水平。结论 这些发现支持将源自葡萄园修剪残体和污水污泥的堆肥和蚯蚓粪作为再生有机改良剂用于园艺生产的潜力。然而，建议进一步研究以在长期田间条件和多种土壤类型下验证这些结果。","Frontiers in Sustainable Food Systems","2026-10-05T00:00:00Z",66,{"impact":20,"substance":401,"depth":321,"authority":402,"freshness":403,"relevant":21,"comment":404},20,13,9,"温室盆栽短期试验，验证葡萄修剪废弃物与污泥制成颗粒堆肥和蚯蚓粪的农艺效果，结论有参考价值但需长期田间验证。",[406],{"name":397,"url":394},[408,409,410,411,412],"生菜","土壤改良","有机肥","蚯蚓粪","堆肥",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[415],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":417},{"candidates":418,"agricultural_evidence":51},[419,423,427,433,440],{"code":32,"score":19,"supported":51,"title":420,"abstract":421,"tags":422},[33],[54,33],[409],{"code":39,"score":13,"supported":51,"title":424,"abstract":425,"tags":426},[],[66,67,69,146],[410],{"code":428,"score":216,"supported":51,"title":429,"abstract":430,"tags":432},"B1",[],[408,431],"萝卜",[408],{"code":434,"score":108,"supported":51,"title":435,"abstract":436,"tags":439},"B7",[],[437,438],"温室","greenhouse",[],{"code":441,"score":80,"supported":51,"title":442,"abstract":443,"tags":445},"B2",[],[444],"葡萄",[],[447,448],"葡萄修剪废弃物 污泥 堆肥 生菜","土壤改良 有机肥 蚯蚓粪 堆肥","葡萄修剪废弃物污泥堆肥生菜-4292","10.3389\u002Ffsufs.2026.1948663",{"doi":450,"openalex_id":452,"authors":453,"venue":397,"cited_by_count":98,"oa_url":394,"card":495,"direction":499,"ingested_from":105},"W7220437154",[454,457,460,463,466,468,471,474,477,480,483,486,489,492],{"name":455,"orcid":456},"Maria Cristina Morais","https:\u002F\u002Forcid.org\u002F0000-0002-6598-7454",{"name":458,"orcid":459},"Elisabete Nascimento-Gonçalves","https:\u002F\u002Forcid.org\u002F0000-0001-6430-1513",{"name":461,"orcid":462},"Lia‐Tânia Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-9612-0909",{"name":464,"orcid":465},"Sandra Pereira","https:\u002F\u002Forcid.org\u002F0000-0003-2998-814X",{"name":467,"orcid":9},"Zélia Branco",{"name":469,"orcid":470},"Henda Lopes","https:\u002F\u002Forcid.org\u002F0000-0002-2830-1783",{"name":472,"orcid":473},"Tiago Azevedo","https:\u002F\u002Forcid.org\u002F0000-0002-3030-4510",{"name":475,"orcid":476},"Catarina Medeiros","https:\u002F\u002Forcid.org\u002F0009-0005-2706-7023",{"name":478,"orcid":479},"Ana Maria Coimbra","https:\u002F\u002Forcid.org\u002F0000-0003-3110-7153",{"name":481,"orcid":482},"João Claro","https:\u002F\u002Forcid.org\u002F0000-0001-5936-1036",{"name":484,"orcid":485},"Guilhermina Marques","https:\u002F\u002Forcid.org\u002F0000-0003-0963-5785",{"name":487,"orcid":488},"João Ricardo Sousa","https:\u002F\u002Forcid.org\u002F0000-0003-4365-9086",{"name":490,"orcid":491},"Paula A. Oliveira","https:\u002F\u002Forcid.org\u002F0000-0001-9519-4044",{"name":493,"orcid":494},"Marta Roboredo","https:\u002F\u002Forcid.org\u002F0000-0002-6177-4988",{"tldr":496,"method":497,"finding":498,"direction":499,"opportunity":500},"评估葡萄修剪物与污泥制成的颗粒堆肥和蚯蚓粪对生菜生长及土壤化学性质的影响。","温室盆栽试验，4处理8重复，55天，方差分析与Tukey检验。","两种有机肥多数形态指标媲美商品肥，且提升土壤有机质、磷钾和阳离子交换量。","农业绿色发展与碳","可开展长期田间试验，验证不同土壤类型下有机肥的稳定效果与重金属累积风险。","2026-10-06T23:30:06.098411Z",{"id":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":9,"source_name":508,"source_url":505,"published_at":509,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":510,"score_detail":511,"sources":513,"tags":515,"primary_topic":517,"secondary_topics":518,"classification":521,"search_phrases":548,"slug":551,"view_count":89,"doi":552,"paper":553,"created_at":566},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":17,"substance":401,"depth":119,"authority":108,"freshness":20,"relevant":21,"comment":512},"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。",[514],{"name":508,"url":505},[26,27,28,224,516],"氮肥管理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[519],{"code":221,"slug":225,"name":224,"path":520,"parent_code":133},"\u002Ftopic\u002Ffield-crops\u002Frice",{"status":45,"locked":14,"version":46,"method":47,"evidence":522},{"candidates":523,"agricultural_evidence":51},[524,528,534,538,544],{"code":39,"score":210,"supported":51,"title":525,"abstract":526,"tags":527},[69],[66,67,68,69],[516,28],{"code":221,"score":13,"supported":51,"title":529,"abstract":530,"tags":533},[],[224,531,225,532],"稻田","paddy",[224],{"code":57,"score":216,"supported":51,"title":535,"abstract":536,"tags":537},[],[61,59],[26],{"code":72,"score":216,"supported":51,"title":539,"abstract":540,"tags":543},[],[541,542],"深度学习","deep learning",[27],{"code":32,"score":108,"supported":51,"title":545,"abstract":546,"tags":547},[],[54,33],[],[549,550],"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":552,"openalex_id":554,"authors":555,"venue":508,"cited_by_count":98,"oa_url":505,"card":561,"direction":103,"ingested_from":105},"W7214974109",[556,558],{"name":557,"orcid":9},"Rusanara Akhter",{"name":559,"orcid":560},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":562,"method":563,"finding":564,"direction":103,"opportunity":565},"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":568,"title":569,"url":570,"summary":571,"summary_zh":572,"content":9,"source_name":573,"source_url":570,"published_at":574,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":575,"score_detail":576,"sources":578,"tags":580,"primary_topic":583,"secondary_topics":584,"classification":586,"search_phrases":611,"slug":614,"view_count":89,"doi":615,"paper":616,"created_at":640},3805,"Machine Learning Prediction of Soil Nutrients and pH in Tibetan Alpine Croplands: A Comparison of Nine Algorithms","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18199875","Soils underpin terrestrial ecosystem functioning and exert strong control over farmland productivity, nutrient turnover, environmental quality, and agricultural sustainability. Reliable prediction of soil nutrients and pH is therefore important for precision fertilization, soil-quality assessment, and the sustainability of farmland management, particularly in fragile alpine agroecosystems. However, the comparative performance of candidate algorithms for estimating soil properties in alpine croplands of the Qinghai-Tibetan Plateau remains poorly characterized. Here, we assessed nine approaches, namely random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive partitioning regression tree (RRT), for predicting soil carbon, nitrogen, phosphorus, potassium, and pH from annual mean temperature, annual precipitation, annual solar radiation, and maximum normalized difference vegetation index (NDVImax). Field observations were obtained from 103 sampling plots, with four individual soil samples collected per plot and depth, resulting in 412 observations for each depth layer. Clear contrasts in model performance emerged under alpine agricultural conditions. Across all soil variables and depths, eXGB and RF showed the best overall performance, with the lowest mean relative bias values of 1.35% and 1.55%, respectively, and the lowest mean RMSE values of 0.43 and 0.47. These results indicate that nonlinear ensemble algorithms better captured the heterogeneous relationships between environmental covariates and soil properties than linear models or single-tree approaches. Overall, RF and eXGB provide a practical methodological basis for precision fertilization, soil-quality assessment, and long-term sustainability in fragile alpine agroecosystems.","土壤是陆地生态系统功能的基础，并对农田生产力、养分周转、环境质量和农业可持续性具有强烈控制作用。因此，可靠预测土壤养分和pH对于精准施肥、土壤质量评估以及农田管理的可持续性十分重要，尤其是在脆弱的高寒农业生态系统中。然而，在青藏高原高寒农田中，用于估算土壤属性的候选算法的比较性能仍缺乏系统表征。本研究评估了九种方法，即随机森林（RF）、广义提升回归（GBR）、多元线性回归（MLR）、人工神经网络（ANN）、广义线性回归（GLR）、条件推断树（CIT）、极端梯度提升（eXGB）、支持向量机（SVM）和递归分割回归树（RRT），利用年平均气温、年降水量、年太阳辐射和最大归一化差异植被指数（NDVImax）预测土壤碳、氮、磷、钾和pH。野外观测来自103个采样样地，每个样地和每个深度采集4个独立土壤样品，每个深度层共获得412个观测值。在高寒农业条件下，模型性能表现出明显差异。在所有土壤变量和深度上，eXGB和RF总体表现最佳，平均相对偏差值最低，分别为1.35%和1.55%，平均RMSE值最低，分别为0.43和0.47。这些结果表明，与线性模型或单树方法相比，非线性集成算法更好地捕捉了环境协变量与土壤属性之间的异质性关系。总体而言，RF和eXGB为脆弱高寒农业生态系统中的精准施肥、土壤质量评估和长期可持续性提供了实用的方法学基础。","Sustainability","2026-09-27T00:00:00Z",73,{"impact":17,"substance":196,"depth":18,"authority":402,"freshness":20,"relevant":21,"comment":577},"基于103个样点412份观测对比九种算法预测高原农田土壤养分与pH，方法扎实、结论明确，对精准施肥有直接参考价值，但属区域性研究，产业影响有限。",[579],{"name":573,"url":570},[26,27,28,581,582],"土壤养分","青藏高原",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[585],{"code":72,"slug":331,"name":332,"path":333,"parent_code":334},{"status":45,"locked":14,"version":46,"method":47,"evidence":587},{"candidates":588,"agricultural_evidence":51},[589,593,597,601,607],{"code":32,"score":19,"supported":51,"title":590,"abstract":591,"tags":592},[33],[54,33],[581],{"code":72,"score":403,"supported":51,"title":594,"abstract":595,"tags":596},[351],[],[27],{"code":39,"score":216,"supported":51,"title":598,"abstract":599,"tags":600},[],[66,146],[28],{"code":602,"score":80,"supported":51,"title":603,"abstract":604,"tags":606},"H6",[],[605],"农业生态",[],{"code":57,"score":21,"supported":14,"title":608,"abstract":609,"tags":610},[],[],[26],[612,613],"青藏高原 农田 土壤养分 机器学习","土壤pH 预测 随机森林 XGBoost","青藏高原农田土壤养分机器学习-3805","10.3390\u002Fsu18199875",{"doi":615,"openalex_id":617,"authors":618,"venue":573,"cited_by_count":98,"oa_url":570,"card":635,"direction":103,"ingested_from":105},"W7214680092",[619,621,623,626,628,631,633],{"name":620,"orcid":9},"Chenjun Zhao",{"name":622,"orcid":9},"Shaowei Li",{"name":624,"orcid":625},"Yuan Tian","https:\u002F\u002Forcid.org\u002F0000-0002-8758-1826",{"name":627,"orcid":9},"ChengQun Yu",{"name":629,"orcid":630},"Gang Fu","https:\u002F\u002Forcid.org\u002F0000-0002-6055-8730",{"name":632,"orcid":9},"Zhiming Zhong",{"name":634,"orcid":9},"Wei Sun",{"tldr":636,"method":637,"finding":638,"direction":103,"opportunity":639},"比较九种机器学习算法预测青藏高原高寒农田土壤养分与pH，发现集成算法最优。","基于103个样点412个观测，用气候与NDVI变量比较RF、eXGB等九种算法。","eXGB和RF表现最佳，平均相对偏差最低（1.35%和1.55%），优于线性与单树模型。","可引入多时相遥感与深度学习方法，提升高寒区土壤属性制图精度与泛化能力。","2026-09-29T23:30:57.794777Z"]