[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-26":3,"related-26":81},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":76,"slug":79,"view_count":13,"doi":8,"paper":8,"created_at":80},26,"南疆棉田形成“感知—决策—执行”智慧栽培闭环","https:\u002F\u002Fcri.caas.cn\u002Fxwdt\u002Fmtbd\u002F5d60f1578007432180b9f36c5f0b4361.htm","2000亩示范田将物联网数据、数字化栽培标准和智能滴灌联动，自动生成灌溉、施肥和化控方案。示范结果显示，灌溉施肥用工节省85%以上，节水25%至30%，肥料利用率提高20%以上。",null,"### [光明日报]AI决策+智能滴灌！助力南疆棉花提质增效\n\n发布时间:2026-07-25 来源 ：**光明日报客户端**作者：**杨舒**\n\n2026年7月21日，由中国农业科学院棉花研究所与塔里木大学联合主办的“南疆棉花农艺农机融合精准高效栽培关键技术集成与应用”现场观摩会在新疆生产建设兵团第一师九团举办。2000亩示范田集中展示了“AI决策+智能装备+农艺农机融合”栽培技术体系，为新疆南疆地区植棉提质增效提供了可复制推广的样板。\n\n从“经验种地”到“数据种地”的转变，AI智能决策系统让棉田拥有了“智慧大脑”。据中棉所棉花智慧栽培团队首席科学家李亚兵研究员介绍，针对南疆棉花生产在保苗壮苗、水肥高效利用、化学调控等方面存在的农艺农机融合度不高等问题，中棉所进一步优化自主研发的智慧栽培技术，将海量物联网数据与团队积淀多年的棉花数字化栽培管理标准体系深度融合，研发了AI智能决策系统，该系统可以自动诊断棉花生长状态，生成精准的灌溉、施肥、化控等技术集成管理方案，实现棉田管理的精准化和科学化。\n\n该示范田配备了土壤、红外温度、光合有效辐射等传感器监测设备进行实时数据采集，AI智能决策系统根据数据分析结果给出棉花高效栽培集成管理方案，利用智能施肥系统、智能控制阀门等控制设备和小流量恒压滴灌系统进行精准的水肥管理，形成了从田间信息感知到智能决策再到精准执行的完整闭环。通过各项精准高效栽培技术集成，该示范田节省灌溉施肥用工85%以上，节水25%～30%，肥料利用率提高20%以上。\n\n“这套技术体系依托地块级实时数据，实现智能决策与精准栽培管理，破解了传统植棉管理粗放、水肥利用率低等痛点，技术成熟实用，可有效助力植棉主体降本增收。”新疆生产建设兵团农业技术推广总站毕显杰研究员在观摩后给予高度评价，认为该技术体系兼顾节本增效与绿色发展，为南疆棉花生产提质增效提供了可复制、可推广的技术方案。\n\n下一步，中棉所将加快精准高效栽培技术集成与示范推广，把农民能用、会用、好用的智慧植棉技术送到田间地头，切实解决农民实际生产问题，推动良种、良法、智能装备与数字化管理深度融合，持续为棉花产业高质量发展提供坚实的科技力量。\n\n打印本页\n\n关闭本页","中国农业科学院棉花研究所、光明日报 · 2026-07-21","2026-07-21T00:00:00Z","报道",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,22,16,15,2,1,"报道南疆棉花智慧栽培技术集成示范，形成感知-决策-执行闭环，具有产业推广价值，但时效性较低。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","棉花","新疆","AI决策","精准栽培",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,60,66,71],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},6,true,[],[50,51],"施肥","肥料",[],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":59},"H3",3,[],[58],"灌溉",[],{"code":61,"score":55,"supported":47,"title":62,"abstract":63,"tags":65},"I5",[],[64],"物联网",[],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"A6",[],[],[28],{"code":72,"score":22,"supported":14,"title":73,"abstract":74,"tags":75},"I1",[],[],[27],[77,78],"智慧农业 精准栽培 AI决策 新疆","智慧农业 精准栽培","智慧农业精准栽培AI决策新疆-26","2026-07-27T23:59:41.058899Z",{"total":46,"page":22,"page_size":46,"items":82},[83,162,246,342,386,474],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":91,"cover_url":8,"hotness":92,"is_selected":14,"score":93,"score_detail":94,"sources":100,"tags":104,"primary_topic":109,"secondary_topics":110,"classification":111,"search_phrases":142,"slug":145,"view_count":146,"doi":147,"paper":148,"created_at":161},5398,"Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470857","Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations examines the relationship between measurable colour and texture characteristics of banana leaves and their chemically determined nutrient concentrations. The study uses digital image processing to extract leaf features, including RGB colour values, colour intensity, texture patterns, and related statistical parameters. These image-based measurements are compared with laboratory-based nutrient analysis to identify significant correlations between optical features and nutrient levels. The research aims to determine whether leaf colour and texture can serve as reliable indicators of plant nutritional status. This approach may support the development of non-destructive, cost-effective, and efficient techniques for monitoring banana plant health. The findings can contribute to precision agriculture, improved nutrient management, and the advancement of image-based methods for crop assessment and agricultural research.","香蕉叶片颜色和纹理特征与化学测定养分浓度的相关性分析探讨了香蕉叶片可测量的颜色和纹理特征与其化学测定养分浓度之间的关系。该研究利用数字图像处理提取叶片特征，包括RGB颜色值、颜色强度、纹理模式及相关统计参数。将这些基于图像的测量结果与实验室养分分析进行比较，以识别光学特征与养分水平之间的显著相关性。该研究旨在确定叶片颜色和纹理能否作为植物营养状况的可靠指标。该方法可能有助于开发无损、经济高效且高效的香蕉植株健康监测技术。研究结果可为精准农业、改进养分管理以及推进基于图像的作物评估和农业研究方法作出贡献。","International Journal of Emerging Technologies and Innovative Research","2026-10-19T00:00:00Z","论文",25,50,{"impact":95,"substance":19,"depth":96,"authority":97,"freshness":98,"relevant":22,"comment":99},8,13,4,9,"该研究探索用叶片颜色与纹理图像无损判断香蕉营养状况，思路有价值，但期刊权威性低、样本规模未明，暂不宜进入每日精选。",[101,102],{"name":89,"url":86},{"name":89,"url":103},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23471101",[27,105,106,107,108],"无损检测","图像识别","香蕉","营养诊断",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":112},{"candidates":113,"agricultural_evidence":47},[114,121,126,132,137],{"code":72,"score":115,"supported":47,"title":116,"abstract":117,"tags":120},7,[],[118,119],"精准农业","precision agriculture",[27],{"code":33,"score":55,"supported":47,"title":122,"abstract":123,"tags":125},[],[124],"养分管理",[],{"code":127,"score":55,"supported":47,"title":128,"abstract":129,"tags":131},"I7",[],[130],"研究方法",[],{"code":133,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},"B2",[],[],[107],{"code":138,"score":22,"supported":14,"title":139,"abstract":140,"tags":141},"I2",[],[],[106],[143,144],"香蕉叶片 营养浓度 图像特征","图像识别 无损检测 智慧农业 营养诊断","香蕉叶片营养浓度图像特征-5398",0,"10.5281\u002Fzenodo.23470857",{"doi":147,"openalex_id":149,"authors":150,"venue":89,"cited_by_count":146,"oa_url":86,"card":153,"direction":159,"ingested_from":160},"W7222963764",[151],{"name":152,"orcid":8},"Anantjit Publication",{"tldr":154,"method":155,"finding":156,"direction":157,"opportunity":158},"分析香蕉叶片颜色与纹理特征同化学测定养分浓度的相关性。","数字图像处理提取RGB、颜色强度与纹理参数，与实验室养分分析对比。","叶片光学特征与养分水平存在显著相关，可作营养状态指标。","农业遥感与作物表型","可扩展到多作物、多生育期及高光谱纹理融合的养分无损诊断模型构建。","数字乡村与农业信息化","openalex","2026-10-20T23:30:23.713550Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":8,"source_name":168,"source_url":165,"published_at":169,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":201,"slug":204,"view_count":146,"doi":205,"paper":206,"created_at":245},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":172,"substance":18,"depth":172,"authority":173,"freshness":98,"relevant":22,"comment":174},18,14,"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。",[176],{"name":168,"url":165},[27,118,178,179,180],"控释肥","温度响应","蓖麻油基材料",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":184},{"candidates":185,"agricultural_evidence":47},[186,191,195],{"code":33,"score":173,"supported":47,"title":187,"abstract":189,"tags":190},[188],"fertilizer",[51,188],[],{"code":72,"score":95,"supported":47,"title":192,"abstract":193,"tags":194},[],[118,119],[27,118],{"code":196,"score":55,"supported":47,"title":197,"abstract":198,"tags":200},"H7",[],[199],"气候适应",[],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":168,"cited_by_count":146,"oa_url":165,"card":239,"direction":159,"ingested_from":160},"W7222872607",[209,211,214,216,218,221,223,225,227,230,233,236],{"name":210,"orcid":8},"Jiahui Liu",{"name":212,"orcid":213},"Li J","https:\u002F\u002Forcid.org\u002F0009-0008-8760-1365",{"name":215,"orcid":8},"Ziyao Li",{"name":217,"orcid":8},"Xiangjie Zhao",{"name":219,"orcid":220},"Fengyan Fang","https:\u002F\u002Forcid.org\u002F0009-0009-0081-4796",{"name":222,"orcid":8},"Yan Liu",{"name":224,"orcid":8},"Songyuan Wang",{"name":226,"orcid":8},"Boning Su",{"name":228,"orcid":229},"Yuanyuan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-7439-2970",{"name":231,"orcid":232},"Bin Gao","https:\u002F\u002Forcid.org\u002F0000-0003-3769-0191",{"name":234,"orcid":235},"Shugang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1428-9082",{"name":237,"orcid":238},"Yuechao Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4045-0252",{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"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。","智慧农业 \u002F 农业物联网","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。","2026-10-19T23:30:28.317764Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":8,"source_name":252,"source_url":249,"published_at":253,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":257,"tags":259,"primary_topic":265,"secondary_topics":266,"classification":267,"search_phrases":324,"slug":327,"view_count":21,"doi":328,"paper":329,"created_at":341},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":172,"authority":96,"freshness":95,"relevant":22,"comment":256},"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。",[258],{"name":252,"url":249},[27,260,261,262,263,264],"无人机","精准施肥","变量施肥","遥感监测","养分利用效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":268},{"candidates":269,"agricultural_evidence":47},[270,278,285,292,299,306,313,320],{"code":33,"score":271,"supported":47,"title":272,"abstract":274,"tags":277},20,[273],"fertilization",[50,51,275,124,276],"水肥一体化","fertigation",[264,262,261],{"code":279,"score":115,"supported":47,"title":280,"abstract":281,"tags":284},"I3",[],[282,283],"遥感","remote sensing",[263],{"code":286,"score":46,"supported":47,"title":287,"abstract":288,"tags":291},"A1",[],[289,290],"水稻","rice",[],{"code":293,"score":46,"supported":47,"title":294,"abstract":295,"tags":298},"A2",[],[296,297],"小麦","wheat",[],{"code":300,"score":46,"supported":47,"title":301,"abstract":302,"tags":305},"A3",[],[303,304],"玉米","maize",[],{"code":307,"score":46,"supported":47,"title":308,"abstract":309,"tags":312},"H2",[],[310,311],"土壤","soil",[],{"code":314,"score":46,"supported":47,"title":315,"abstract":316,"tags":319},"I4",[],[317,318],"地理信息","geographic information",[],{"code":72,"score":22,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[27],[325,326],"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":328,"openalex_id":330,"authors":331,"venue":252,"cited_by_count":146,"oa_url":249,"card":336,"direction":243,"ingested_from":160},"W7222838957",[332,334],{"name":333,"orcid":8},"S. T. Panusha",{"name":335,"orcid":8},"T. M. Soumya",{"tldr":337,"method":338,"finding":339,"direction":243,"opportunity":340},"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":343,"title":344,"url":345,"summary":346,"summary_zh":8,"content":8,"source_name":347,"source_url":8,"published_at":348,"category":12,"cover_url":8,"hotness":92,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":358,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":381,"slug":384,"view_count":146,"doi":8,"paper":8,"created_at":385},5212,"人保财险联合浙江省农科院发布《无人机施肥施药作业质量划分》团体标准","https:\u002F\u002Fwww.cnr.cn\u002Fzjfw\u002Fmlnb\u002Fnbgd\u002F20261016\u002Ft20261016_527843650.shtml","在宁波举办的首届四明保险论坛科技保险主题活动上,人保财险联合浙江省农业科学院发布《无人机施肥施药作业质量划分》团体标准,为农业无人机作业划定质量标尺,促进低空农业高质量发展和风险防控。","央广网(2026-10-16)","2026-10-16T00:00:00Z",68,{"impact":172,"substance":19,"depth":351,"authority":173,"freshness":95,"relevant":22,"comment":352},12,"省级科研机构与保险企业联合发布团体标准，为农业无人机作业质量提供标尺，具行业参考价值，但属细分领域进展，未达重大突破层级。",[354,355],{"name":347,"url":345},{"name":356,"url":357},"新浪财经(2026-10-15)","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-15\u002Fdoc-inivhyai8038690.shtml",[27,359,260,360,361],"低空经济","农业保险","农业标准",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":365},{"candidates":366,"agricultural_evidence":47},[367,372,377],{"code":33,"score":368,"supported":47,"title":369,"abstract":370,"tags":371},11,[50],[50],[],{"code":373,"score":22,"supported":14,"title":374,"abstract":375,"tags":376},"G3",[],[],[360],{"code":72,"score":22,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[27],[382,383],"人保财险 浙江省农科院 无人机施肥","无人机施肥施药 作业质量划分","人保财险浙江省农科院无人机施肥-5212","2026-10-18T00:05:23.058110Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":8,"source_name":392,"source_url":389,"published_at":393,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":403,"secondary_topics":404,"classification":405,"search_phrases":426,"slug":429,"view_count":146,"doi":430,"paper":431,"created_at":473},4892,"RGB-Derived Haralick Edge-Texture Features Discriminate Mung Bean Responses to Mineral and Biofertilizer Regimes","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202026","Low-cost RGB imaging can accelerate crop phenotyping, but its ability to capture plant responses to fertilizer regimes through edge-based texture descriptors remains insufficiently tested. Mung bean [Vigna radiata (L.) R. Wilczek] was grown in a randomized complete block design with six fertilizer regimes and four replicates. Side-view RGB images acquired under controlled conditions were segmented, converted to grayscale, transformed into Canny edge maps, and summarized using six gray-level co-occurrence matrix (GLCM) features. Agronomic variables were evaluated: plant height, stem diameter, nodule weight, fresh weight, pod weight, shoot dry matter, grain yield per plot, the chlorophyll index, root length, and root volume. Contrast, energy, homogeneity, entropy, and variance differed across regimes, whereas the GLCM correlation did not. Mineral fertilization resulted in the highest values of stem diameter, nodule mass, fresh biomass, shoot dry matter, and the chlorophyll index. Baker’s Gamma coefficient (0.9369) and the cophenetic correlation (0.9498) showed high similarity between the hierarchical structures, indicating that the texture descriptors preserved a treatment organization highly similar to that observed for the agronomic variables. The Haralick features calculated from controlled RGB-derived edge maps revealed fertilizer-associated differences in the mung bean canopy structure. This method is promising for nondestructive screening, but replicate-level predictive validation, full reporting of image-processing parameters, and external testing are needed before agronomic traits can be estimated from images.","低成本RGB成像可加速作物表型分析，但其通过基于边缘的纹理描述符捕捉植物对施肥制度响应的能力仍缺乏充分验证。绿豆[Vigna radiata (L.) R. Wilczek]采用随机完全区组设计种植，设6个施肥处理，4次重复。在受控条件下获取的侧视RGB图像经分割、转换为灰度图、转化为Canny边缘图，并利用6个灰度共生矩阵(GLCM)特征进行汇总。评估的农艺指标包括：株高、茎粗、根瘤重、鲜重、荚重、地上部干物质、每小区籽粒产量、叶绿素指数、根长和根体积。对比度、能量、同质性、熵和方差在不同施肥处理间存在差异，而GLCM相关性则无显著差异。矿质施肥处理下茎粗、根瘤质量、鲜生物量、地上部干物质和叶绿素指数均最高。Baker's Gamma系数(0.9369)和共表型相关系数(0.9498)表明层次结构之间高度相似，说明纹理描述符所保留的处理组织结构与农艺变量所观察到的结构高度一致。由受控RGB衍生边缘图计算的Haralick特征揭示了绿豆冠层结构中与施肥相关的差异。该方法在无损筛选中具有前景，但在能够从图像估算农艺性状之前，仍需进行重复水平的预测验证、图像处理参数的完整报告以及外部测试。","Agronomy","2026-10-13T00:00:00Z",66,{"impact":95,"substance":271,"depth":19,"authority":96,"freshness":98,"relevant":22,"comment":396},"低成本RGB边缘纹理特征用于绿豆施肥响应的无损表型筛选，方法新颖但尚需重复级预测验证，属细分领域方法学进展。",[398],{"name":392,"url":389},[27,400,105,401,402],"绿豆","作物表型","RGB成像",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":406},{"candidates":407,"agricultural_evidence":47},[408,412,417,422],{"code":33,"score":98,"supported":47,"title":409,"abstract":410,"tags":411},[],[50,188,273],[],{"code":279,"score":115,"supported":47,"title":413,"abstract":414,"tags":416},[],[401,415],"crop phenotyping",[401],{"code":418,"score":22,"supported":14,"title":419,"abstract":420,"tags":421},"A5",[],[],[400],{"code":72,"score":22,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[27],[427,428],"绿豆 施肥 表型","RGB图像 纹理特征 作物","绿豆施肥表型-4892","10.3390\u002Fagronomy16202026",{"doi":430,"openalex_id":432,"authors":433,"venue":392,"cited_by_count":146,"oa_url":389,"card":468,"direction":157,"ingested_from":160},"W7221615335",[434,437,440,443,446,449,452,455,458,461,463,465],{"name":435,"orcid":436},"Eder Pereira Neves","https:\u002F\u002Forcid.org\u002F0000-0001-9645-7061",{"name":438,"orcid":439},"Jorge González Aguilera","https:\u002F\u002Forcid.org\u002F0000-0002-7308-0967",{"name":441,"orcid":442},"Werverth Costa Martins","https:\u002F\u002Forcid.org\u002F0000-0002-2813-4276",{"name":444,"orcid":445},"Roseline da Silva Coêlho","https:\u002F\u002Forcid.org\u002F0000-0002-4412-7471",{"name":447,"orcid":448},"Fábio Steiner","https:\u002F\u002Forcid.org\u002F0000-0001-9091-1737",{"name":450,"orcid":451},"Marco Aparecido Queiroz Duarte","https:\u002F\u002Forcid.org\u002F0000-0003-2494-448X",{"name":453,"orcid":454},"Alan Mário Zuffo","https:\u002F\u002Forcid.org\u002F0000-0001-9704-5325",{"name":456,"orcid":457},"César Augusto Masgo Soto","https:\u002F\u002Forcid.org\u002F0000-0003-0720-0833",{"name":459,"orcid":460},"Carlos Genaro Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-4184-2365",{"name":462,"orcid":8},"María Paulina Aliaga Martínez",{"name":464,"orcid":8},"Nataniel Linares-Gutiérrez",{"name":466,"orcid":467},"Luis Fortunato Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-9421-9833",{"tldr":469,"method":470,"finding":471,"direction":157,"opportunity":472},"用RGB图像Canny边缘图的Haralick纹理特征区分绿豆对不同施肥处理的响应。","侧视RGB图像分割、灰度化、Canny边缘检测与GLCM六特征，结合农艺性状聚类","对比度、能量、同质性、熵和方差随施肥制度显著差异，纹理与农艺变量层次结构高度相似。","可探索边缘纹理特征与具体农艺性状的定量预测模型，并开展重复级验证与跨品种外部测试。","2026-10-14T23:30:20.148045Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":8,"source_name":480,"source_url":477,"published_at":481,"category":91,"cover_url":8,"hotness":13,"is_selected":14,"score":482,"score_detail":483,"sources":486,"tags":488,"primary_topic":489,"secondary_topics":492,"classification":494,"search_phrases":522,"slug":525,"view_count":22,"doi":526,"paper":527,"created_at":540},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":351,"substance":271,"depth":484,"authority":96,"freshness":98,"relevant":22,"comment":485},17,"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。",[487],{"name":480,"url":477},[27,262,303,118,263,124],{"code":300,"slug":304,"name":303,"path":490,"parent_code":491},"\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[493],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":495},{"candidates":496,"agricultural_evidence":47},[497,501,505,509,513,518],{"code":72,"score":19,"supported":47,"title":498,"abstract":499,"tags":500},[119],[118,119],[27,118],{"code":300,"score":20,"supported":47,"title":502,"abstract":503,"tags":504},[304],[303,304],[303],{"code":33,"score":368,"supported":47,"title":506,"abstract":507,"tags":508},[],[50,51,124],[124,262],{"code":307,"score":46,"supported":47,"title":510,"abstract":511,"tags":512},[],[310,311],[],{"code":138,"score":55,"supported":47,"title":514,"abstract":515,"tags":517},[],[516],"机器学习",[],{"code":279,"score":22,"supported":14,"title":519,"abstract":520,"tags":521},[],[],[263],[523,524],"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":526,"openalex_id":528,"authors":529,"venue":480,"cited_by_count":146,"oa_url":477,"card":534,"direction":538,"ingested_from":160},"W7221366291",[530,532],{"name":531,"orcid":8},"Piyali Pal",{"name":533,"orcid":8},"Disha Chattopadhyay",{"tldr":535,"method":536,"finding":537,"direction":538,"opportunity":539},"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"]