[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3786":3,"related-3786":109},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":108},3786,"A cost-effective approach of building plant digital twin combining plant phenotyping and growth models: A case study on rapeseed plants","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128341","High-throughput phenotyping platforms enable dense temporal monitoring of plant growth, yet field-based image collection remains labor-intensive with sparse sampling intervals, leading to missing information on intermediate developmental stages. Functional–structural plant models (FSPMs) offer dynamic growth simulation but often lack realistic 3D architecture derived from actual plants. This study proposes a lightweight framework that bridges sparse phenotyping time points by integrating 3D reconstruction with the GreenLab plant growth model, enabling the construction of plant digital twins for rapeseed ( Brassica napus L.). Multi-angle smartphone images of four rapeseed cultivars at two key growth stages were used to reconstruct 3D plant architecture via 3D Gaussian Splatting (3DGS). Leaves were segmented from the point cloud to calculate area. GreenLab source and sink parameters were estimated from organ-level fresh weights (leaves, internodes, and roots) measured at two growth stages. The goodness–of–fit was high, with weighted sum of squares (WSS) between 5.63 and 9.33 and Pearson correlation coefficients ( r ) ranging from 0.93 to 0.95 across the four cultivars. The calibrated model reproduced leaf areas consistent with 3D reconstructed static structures. Reconstructed organ geometries were used for visualization and functional analysis of light interception. The integrated model thus recovered the complete dynamic development and growth trajectory between sparse sampling points. Temporal hold-out validation showed reasonable agreement between reconstructed leaf area and length–width–derived reference estimates, with a single-leaf mean absolute percentage error (MAPE) of 18.42%. Light interception analysis revealed cultivar-specific differences in canopy light use efficiency attributable to contrasting morphological architectures. Simulated and reconstructed canopy light interception showed strong agreement ( r = 0.98 ). This framework provides a cost-effective approach to reconstructing plant growth dynamics and evaluating light capture characteristics from minimal field data. It offers practical utility for breeders in cultivar evaluation and for optimizing environmental control strategies in precision agriculture.","高通量表型平台能够实现对植物生长的高密度时间监测，但田间图像采集仍然劳动密集且采样间隔稀疏，导致中间发育阶段的信息缺失。功能–结构植物模型（FSPMs）能够进行动态生长模拟，但通常缺乏源自实际植物的真实三维结构。本研究提出了一种轻量级框架，通过整合三维重建与GreenLab植物生长模型来桥接稀疏表型时间点，从而为甘蓝型油菜（Brassica napus L.）构建植物数字孪生。利用四个油菜品种在两个关键生长阶段的多角度智能手机图像，通过三维高斯泼溅（3DGS）重建植物三维结构。从点云中分割叶片以计算叶面积。基于两个生长阶段测定的器官水平鲜重（叶片、节间和根系）估计GreenLab源和库参数。拟合优度较高，四个品种的加权平方和（WSS）在5.63至9.33之间，皮尔逊相关系数（r）在0.93至0.95之间。校准后的模型再现的叶面积与三维重建的静态结构一致。重建的器官几何结构用于可视化和光截获功能分析。该集成模型由此恢复了稀疏采样点之间完整的动态发育和生长轨迹。时间留出验证表明，重建叶面积与基于长宽推导的参考估计之间具有合理的一致性，单叶平均绝对百分比误差（MAPE）为18.42%。光截获分析揭示了由于形态结构差异导致的品种特异性冠层光利用效率差异。模拟与重建的冠层光截获表现出高度一致性（r = 0.98）。该框架提供了一种经济有效的方法，可从最少的田间数据重建植物生长动态并评估光捕获特征。它为育种者进行品种评估以及优化精准农业中的环境调控策略提供了实用价值。",null,"European Journal of Agronomy","2026-09-28T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,9,1,"该研究提出结合3D高斯泼溅与GreenLab模型的低成本植物数字孪生框架，在油菜上验证可靠，对智慧育种与精准农业有实用价值，值得精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","油菜","表型组学","作物生长模型","植物数字孪生",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,55,60,67],{"code":46,"score":19,"supported":47,"title":48,"abstract":50,"tags":54},"I1",true,[49],"digital twin",[51,52,53],"精准农业","数字孪生","precision agriculture",[27],{"code":33,"score":17,"supported":47,"title":56,"abstract":58,"tags":59},[57],"rapeseed",[28,57],[28],{"code":61,"score":62,"supported":47,"title":63,"abstract":64,"tags":66},"B1",3,[],[65],"甘蓝",[],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"I2",[],[],[30],[73,74],"油菜 植物数字孪生 表型","GreenLab 生长模型 油菜","油菜植物数字孪生表型-3786",4,"10.1016\u002Fj.eja.2026.128341",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":100,"oa_url":6,"card":101,"direction":105,"ingested_from":107},"W7214617633",[81,84,87,89,91,93,95,97],{"name":82,"orcid":83},"Xiujuan Wang","https:\u002F\u002Forcid.org\u002F0000-0001-8327-1735",{"name":85,"orcid":86},"Bowen Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-1141-7158",{"name":88,"orcid":9},"Zhiqing Liu",{"name":90,"orcid":9},"Cuiping Bu",{"name":92,"orcid":9},"Jing Hua",{"name":94,"orcid":9},"Haoyu Wang",{"name":96,"orcid":9},"Qirun Huo",{"name":98,"orcid":99},"Mengzhen Kang","https:\u002F\u002Forcid.org\u002F0000-0002-5016-1772",0,{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"结合手机3D重建与GreenLab生长模型，低成本构建油菜植株数字孪生。","多角度手机图像3D高斯泼溅重建，GreenLab源库参数标定与光截获分析。","模型拟合优度高（r=0.93-0.95），可恢复稀疏采样间生长动态并评估光截获。","农业遥感与作物表型","可探索将轻量级数字孪生框架扩展至更多作物及田间高通量场景，降低表型监测成本。","openalex","2026-09-29T23:30:23.763490Z",{"total":110,"page":22,"page_size":110,"items":111},6,[112,177,241,295,355,422],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":133,"classification":134,"search_phrases":150,"slug":153,"view_count":100,"doi":154,"paper":155,"created_at":176},3983,"Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112469","Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling。Computers and Electronics in Agriculture","基于SPH-FEM耦合的磨料气体射流油菜茎秆切割机理数值与实验研究。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",72,{"impact":122,"substance":123,"depth":124,"authority":20,"freshness":21,"relevant":22,"comment":125},12,20,17,"核心期刊论文，SPH-FEM耦合方法研究油菜秸秆磨料气体射流切割机理，方法有新意但属细分领域基础研究，产业影响有限。",[127],{"name":118,"url":115},[27,28,129,130,131],"数值模拟","农业机械","秸秆处理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":135},{"candidates":136,"agricultural_evidence":47},[137,141,146],{"code":33,"score":17,"supported":47,"title":138,"abstract":139,"tags":140},[57],[28,57],[28],{"code":142,"score":22,"supported":14,"title":143,"abstract":144,"tags":145},"F5",[],[],[130],{"code":46,"score":22,"supported":14,"title":147,"abstract":148,"tags":149},[],[],[27],[151,152],"油菜秸秆 磨料气体射流 切割","SPH-FEM 耦合 秸秆切割","油菜秸秆磨料气体射流切割-3983","10.1016\u002Fj.compag.2026.112469",{"doi":154,"openalex_id":156,"authors":157,"venue":118,"cited_by_count":100,"oa_url":9,"card":170,"direction":174,"ingested_from":107},"W7215128671",[158,161,163,165,167],{"name":159,"orcid":160},"Mun-Sung Han","https:\u002F\u002Forcid.org\u002F0009-0000-4043-6982",{"name":162,"orcid":9},"Xiaokang Qin",{"name":164,"orcid":9},"Kai Yu",{"name":166,"orcid":9},"Yun Chen",{"name":168,"orcid":169},"Kaixiong Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9904-6137",{"tldr":171,"method":172,"finding":173,"direction":174,"opportunity":175},"采用SPH-FEM耦合方法数值与实验研究磨料气体射流切割油菜茎秆的机理。","SPH-FEM耦合数值模拟结合切割实验，分析磨料气体射流切割过程。","揭示了磨料气体射流切割油菜茎秆的力学机制与切割参数影响规律。","农业人工智能与决策模型","可将SPH-FEM耦合仿真拓展至其他作物茎秆切割，并融合机器视觉实现切割参数智能优化。","2026-10-02T23:30:03.593073Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":118,"source_url":180,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":183,"sources":185,"tags":187,"primary_topic":190,"secondary_topics":191,"classification":192,"search_phrases":209,"slug":212,"view_count":200,"doi":213,"paper":214,"created_at":240},3977,"Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112468","Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings。Computers and Electronics in Agriculture","基于模块化步进错位的链槽式侧向投苗装置开发与评估：面向油菜裸根苗多行移栽。Computers and Electronics in Agriculture",{"impact":122,"substance":123,"depth":124,"authority":20,"freshness":21,"relevant":22,"comment":184},"核心期刊论文，提出链槽式模块化错位送苗装置，对多行油菜裸苗移栽机械化有实质方法创新，但属细分领域技术进展，公共影响有限。",[186],{"name":118,"url":180},[27,188,51,28,189],"农业机器人","移栽机械",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":193},{"candidates":194,"agricultural_evidence":47},[195,199,204],{"code":33,"score":17,"supported":47,"title":196,"abstract":197,"tags":198},[57],[28,57],[28],{"code":46,"score":200,"supported":14,"title":201,"abstract":202,"tags":203},2,[],[],[27,51],{"code":205,"score":22,"supported":14,"title":206,"abstract":207,"tags":208},"F6",[],[],[188],[210,211],"油菜裸苗移栽 链槽式送苗装置","Computers and Electronics in Agriculture 移栽","油菜裸苗移栽链槽式送苗装置-3977","10.1016\u002Fj.compag.2026.112468",{"doi":213,"openalex_id":215,"authors":216,"venue":118,"cited_by_count":100,"oa_url":9,"card":234,"direction":238,"ingested_from":107},"W7215105622",[217,220,222,224,227,229,231],{"name":218,"orcid":219},"Hua Yuan","https:\u002F\u002Forcid.org\u002F0000-0001-7594-8718",{"name":221,"orcid":9},"Qingxi Liao",{"name":223,"orcid":9},"Zhengyu Liu",{"name":225,"orcid":226},"Zhe Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2653-0408",{"name":228,"orcid":9},"Lin Li",{"name":230,"orcid":9},"Yitao Liao",{"name":232,"orcid":233},"Wenli Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-8443-8987",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"研发并评估链槽式侧向供苗装置，实现油菜裸根苗多行移栽的模块化步进错位供苗。","设计链槽侧向供苗机构，采用模块化步进错位方案，并进行样机试验评估。","该装置能实现多行裸根苗有序错位供苗，提升移栽效率与供苗质量。","智慧农业 \u002F 农业物联网","可结合视觉检测与伺服控制，研究供苗状态实时监测与自适应错位调节，提升多行移栽智能化水平。","2026-10-02T23:30:02.682513Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":9,"content":246,"source_name":247,"source_url":9,"published_at":248,"category":249,"cover_url":9,"hotness":13,"is_selected":14,"score":250,"score_detail":251,"sources":253,"tags":255,"primary_topic":260,"secondary_topics":261,"classification":262,"search_phrases":290,"slug":293,"view_count":62,"doi":9,"paper":9,"created_at":294},3297,"山区油菜智能化监测成果在十堰国家农业科技园区落地推广——天空地一体化监测技术与山区油菜大数据平台","https:\u002F\u002Fwww.iarrp.cn\u002Fysdt\u002Fkydt\u002Fd6e5d598bf9f4f998f7f08d7c811699c.htm","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题山区油菜产业智能化关键技术和机械装备研究示范成果应用推广会在十堰国家农业科技园区召开。项目首席科学家梅德圣研究员及行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山区县基层农技推广部门、区域内油菜种植大户代表参会。课题组精准锚定秦巴山区油菜生产实际需求，构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判；研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备。","近日，由中国农业科学院农业资源与农业区划研究所牵头实施的国家重点研发计划课题“山区油菜产业智能化关键技术和机械装备研究示范”成果应用推广会在十堰国家农业科技园区顺利召开。本次会议聚焦山区油菜产业智能化转型痛点难点，集中交付项目核心科技成果，开展大数据平台实操培训，搭建成果落地、技术普及、产业赋能的产学研交流平台，助力秦巴山区油菜产业摆脱传统生产模式，全面迈向数字化、智能化、高效化高质量发展新阶段。项目首席科学家梅德圣研究员等行业专家，十堰市农技中心及下属各县农技中心、长阳县等秦巴山市区县基层农技推广部门，区域内油菜种植大户用户代表参会参训。\n\n我国秦巴等山区油菜种植体量较大，是保障区域油料供给、带动农民增收、助推乡村产业振兴的特色优势产业。但受山地、坡地为主的特殊地形制约，区域内耕地零散、地块不规则，长期存在机械化作业难、生产管理粗放、长势监测滞后、病虫害防控被动、生产数据缺失等突出问题，生产成本偏高、产量稳定性不足，严重制约山区油菜产业提质增效与绿色可持续发展。课题组精准锚定秦巴山区油菜生产实际需求，依托高水平产学研协同攻关体系，针对性开展智能技术改良、专用装备适配、数字体系构建与本地化示范推广，形成一套适配山地生产场景的成熟技术成果与应用模式。\n\n会上，课题组详细介绍了课题研发成果、技术创新亮点与本地化示范成效，顺利完成系列核心成果集中交付。中国农业科学院农业资源与农业区划研究所宋茜研究员表示，课题创新构建天空地一体化监测技术与山区油菜大数据平台，能够对区域气候环境、油菜长势、菌核病发生态势开展动态监测与智能研判，可为田间科学施肥、绿色防控、适时收获、精准植保提供精准数据支撑，改变山区油菜传统凭经验、粗放式的生产管理模式。同时，课题研发适配山地小地块、不规则地块的油菜播种、田间管护、无人植保等专用系统装备，有效破解山地农机作业难度大、精度低、损耗高的产业难题，全面提升山区油菜机械化、标准化作业水平。\n\n为切实打通科技成果落地“最后一公里”，会议同步开展山区油菜大数据平台专项实操培训。项目技术专家围绕平台功能架构、操作流程、数据填报、智能分析、后台运维等核心内容开展系统讲解，结合秦巴山区油菜生产真实场景进行实操演示，现场解答基层农技人员、种植大户在平台使用、技术落地、田间实操中的各类问题。培训内容贴合一线生产需求、实操性极强，有效提升了基层技术人员数字化应用能力，为成果规模化落地推广夯实人才基础。\n\n据了解，课题以十堰柳陂镇示范基地为核心辐射支点，持续拓展示范应用版图，辐射带动五峰、安阳、大柳等多个乡镇开展规模化示范种植，常态化开展技术培训、智能设备实操演示、田间观摩交流、入户指导等配套服务，不断优化完善技术应用模式，成功探索形成一套适配秦巴山区、可复制、可推广的智慧油菜绿色高效生产模式。通过智能化技术与装备的落地应用，有效实现油菜稳产提质、生产降本增效，显著提升山区油菜产业标准化、智能化、现代化发展水平。\n\n与会代表一致认为，该课题成果针对性解决了山区油菜生产的系列瓶颈问题，构建了适配山地特征的智慧油菜生产体系，为山区油料产业高质量发展提供了优秀示范样板。下一步，课题各方将持续深化产学研协同合作，依托十堰国家农业科技园区平台优势，持续迭代优化大数据平台功能与智能装备性能，持续扩大示范推广覆盖面，常态化开展基层技术赋能服务，全力推动智慧油菜生产技术在秦巴武陵山区落地普及、提质增效，持续助力区域油料稳产保供、农业绿色发展、农民增收致富与乡村全面振兴。","中国农业科学院农业资源与农业区划研究所 2026年09月18日","2026-09-18T00:00:00Z","报道",70,{"impact":19,"substance":19,"depth":20,"authority":20,"freshness":110,"relevant":22,"comment":252},"国家重点研发计划课题成果在十堰国家农业科技园区落地推广，天空地一体化监测与山区油菜大数据平台具备实质技术内容与示范价值，值得进入每日精选。",[254],{"name":247,"url":244},[27,256,257,28,258,259],"农业遥感","成果转化","天空地一体化","大数据平台",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":263},{"candidates":264,"agricultural_evidence":47},[265,269,275,281,285],{"code":33,"score":122,"supported":47,"title":266,"abstract":267,"tags":268},[28],[28],[28],{"code":270,"score":76,"supported":47,"title":271,"abstract":272,"tags":274},"G1",[],[273],"农技推广",[257],{"code":276,"score":62,"supported":47,"title":277,"abstract":278,"tags":280},"F4",[],[279],"植保",[],{"code":46,"score":22,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[27],{"code":286,"score":22,"supported":14,"title":287,"abstract":288,"tags":289},"I3",[],[],[256],[291,292],"十堰 山区油菜 智能化监测","天空地一体化 油菜大数据平台","十堰山区油菜智能化监测-3297","2026-09-24T00:03:59.027363Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":9,"content":9,"source_name":300,"source_url":9,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":302,"sources":306,"tags":308,"primary_topic":312,"secondary_topics":313,"classification":314,"search_phrases":342,"slug":345,"view_count":200,"doi":9,"paper":346,"created_at":354},3000,"Plant-GeoAT：几何感知3D植物点云器官身份解析用于器官级表型分析","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1809","四川农业大学吴俊杰等提出Plant-GeoAT几何感知解析网络，在RGB融合前编码局部相对-XYZ邻域并将空间邻域与特征空间关系耦合用于密集点预测。在自建油菜数据集、基于图像的大豆数据集、激光扫描Pheno4D玉米和番茄数据集上评估，5个随机种子下mIoU分别达92.26±0.28%、82.50±0.24%、99.74±0.05%、94.75±0.15%，玉米Stem IoU达99.57±0.09%。","MDPI Agronomy 16(18):1809","2026-09-15T00:00:00Z",{"impact":19,"substance":303,"depth":19,"authority":304,"freshness":110,"relevant":22,"comment":305},23,13,"方法新颖、多作物多数据集验证且精度数据扎实，属器官级表型分析细分领域的重要技术进展，值得进入每日精选。",[307],{"name":300,"url":298},[27,309,28,310,311],"农业人工智能","高通量表型","三维点云",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":315},{"candidates":316,"agricultural_evidence":47},[317,323,329,334,338],{"code":33,"score":318,"supported":47,"title":319,"abstract":320,"tags":322},7,[],[321,28],"大豆",[28],{"code":324,"score":62,"supported":47,"title":325,"abstract":326,"tags":328},"A3",[],[327],"玉米",[],{"code":61,"score":62,"supported":47,"title":330,"abstract":331,"tags":333},[],[332],"番茄",[],{"code":68,"score":200,"supported":14,"title":335,"abstract":336,"tags":337},[],[],[311,309],{"code":46,"score":22,"supported":14,"title":339,"abstract":340,"tags":341},[],[],[27],[343,344],"四川农业大学 植物点云 器官识别","Plant-GeoAT 表型分析","四川农业大学植物点云器官识别-3000",{"doi":9,"openalex_id":9,"authors":347,"venue":9,"cited_by_count":100,"oa_url":9,"card":348,"direction":105,"ingested_from":353},[],{"tldr":349,"method":350,"finding":351,"direction":105,"opportunity":352},"提出几何感知网络Plant-GeoAT，实现3D植物点云器官身份解析与器官级表型分析。","编码局部相对XYZ邻域并耦合空间与特征空间关系，在油菜、大豆、玉米、番茄点云数据","五个数据集mIoU最高达99.74%，玉米茎IoU达99.57%，验证了几何感知对器官分割的有效性。","可探索跨物种、跨传感器的轻量化几何感知模型，并推动器官级表型与基因型关联分析。","agent","2026-09-20T00:03:08.094512Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":9,"source_name":361,"source_url":358,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":363,"sources":365,"tags":367,"primary_topic":370,"secondary_topics":371,"classification":372,"search_phrases":384,"slug":387,"view_count":200,"doi":388,"paper":389,"created_at":421},2315,"A practical workflow combining Kaplan-Meier and Bayesian accelerated failure time analyses for censored reproductive phenology data in soybean","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09851-6","Abstract Developmental progression in plants is inherently a time-to-event process, yet plant phenology data are often analyzed as ordinary endpoint traits even when some individuals fail to reach the target stage within the observation window. Such observations are right-censored rather than missing and should be retained in inference. Here, we evaluated 21 soybean cultivars across 10 controlled-environment treatment combinations varying in CO 2 concentration, photoperiod, and LED light quality, and analyzed days from sowing to the R6 stage using a practical workflow that combined Kaplan-Meier survival analysis with a Bayesian Weibull accelerated failure time (AFT) model. Based on 210 cultivar × treatment observations derived from 1,050 plants, treatments resolved into favorable, intermediate, and strongly inhibitory classes. Elevated CO 2 showed the strongest association with reproductive timing: increasing CO 2 from ambient to 1000–1400 ppm reduced median survival time, while the additional gain from 1000 ppm to 1400 ppm was minimal, indicating a saturating response. Blue - Red spectral treatments and 6–8 h photoperiods were associated with rapid and synchronized development within the tested treatment combinations. The Bayesian AFT model included treatment as a random effect to account for the chamber-level experimental structure; the between-treatment standard deviation was estimated at 0.02 (95% credible interval: 0.00–0.06), indicating negligible chamber-to-chamber variation. The highest-ranked predicted combination (1400 ppm, blue: red = 2:1, 6 h) gave a median time to R6 of 66.90 d (mean 72.18 d). This study provides a practical and transferable workflow for censored plant phenology datasets in controlled-environment research, phenotyping, and breeding. The workflow integrates established survival analysis methods and explicitly accounts for chamber-level design, offering a framework for structured plant phenotyping experiments.","摘要 植物的发育进程本质上是一个时间到事件的过程，然而植物物候数据常常被当作普通的终点性状来分析，即使部分个体在观测窗口内未能达到目标阶段也是如此。此类观测属于右删失而非缺失，应在推断中予以保留。本研究评估了21个大豆品种在10种控制环境处理组合下的表现，这些组合在CO₂浓度、光周期和LED光质上有所不同，并采用将Kaplan-Meier生存分析与贝叶斯Weibull加速失效时间（AFT）模型相结合的实际工作流程，分析了从播种到R6阶段的天数。基于来自1，050株植物的210个品种×处理观测值，各处理可归纳为有利、中等和强抑制三类。升高CO₂与生殖时序的关联最强：将CO₂从环境浓度提高至1000–1400 ppm可缩短中位生存时间，而从1000 ppm进一步提高至1400 ppm的额外增益极小，表明存在饱和响应。在测试的处理组合中，蓝红光光谱处理和6–8 h光周期与快速且同步的发育相关。贝叶斯AFT模型将处理作为随机效应纳入，以考虑 chamber 层面的实验结构；处理间标准差估计为0.02（95%可信区间：0.00–0.06），表明 chamber 间变异可忽略不计。排名最高的预测组合（1400 ppm，蓝：红=2：1，6 h）给出的R6中位时间为66.90 d（均值72.18 d）。本研究为控制环境研究、表型分析和育种中的删失植物物候数据集提供了一个实用且可迁移的工作流程。该工作流程整合了已有的生存分析方法，并明确考虑了 chamber 层面的设计，为结构化植物表型实验提供了一个框架。","BMC Plant Biology","2026-09-12T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":364},"提出结合Kaplan-Meier与贝叶斯AFT模型的删失表型数据分析流程，方法可迁移，对大豆生殖物候与受控环境育种表型研究有实用价值。",[366],{"name":361,"url":358},[27,368,369,321,29],"设施农业","育种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":373},{"candidates":374,"agricultural_evidence":47},[375,380],{"code":33,"score":17,"supported":47,"title":376,"abstract":378,"tags":379},[377],"soybean",[321,377],[321],{"code":46,"score":22,"supported":14,"title":381,"abstract":382,"tags":383},[],[],[27],[385,386],"智慧农业 表型组学 设施农业 大豆","智慧农业 表型组学","智慧农业表型组学设施农业大豆-2315","10.1186\u002Fs12870-026-09851-6",{"doi":388,"openalex_id":390,"authors":391,"venue":361,"cited_by_count":100,"oa_url":358,"card":416,"direction":105,"ingested_from":107},"W7212354617",[392,395,397,400,402,404,406,408,410,412,414],{"name":393,"orcid":394},"Fan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8576-0273",{"name":396,"orcid":9},"Xiao Cui",{"name":398,"orcid":399},"Kanglin Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8747-5022",{"name":401,"orcid":9},"Huilong Hong",{"name":403,"orcid":9},"Xin Su",{"name":405,"orcid":9},"Yajun Xiong",{"name":407,"orcid":9},"Sawaira Jadoon",{"name":409,"orcid":9},"Huan Yu",{"name":411,"orcid":9},"Yijie Chen",{"name":413,"orcid":9},"Qiu Lijuan",{"name":415,"orcid":9},"Jun Wang",{"tldr":417,"method":418,"finding":419,"direction":105,"opportunity":420},"用Kaplan-Meier与贝叶斯AFT分析大豆R6期右删失数据，评估21个品种在10种环境组合下的","21个大豆品种、1050株、10种CO2\u002F光周期\u002FLED光质组合，Kaplan-","CO2升高缩短R6中位时间但1000-1400ppm增益极小，蓝红2:1与6-8h光周期发育最快， ","可将该删失表型分析流程推广至田间多环境育种数据，并耦合高通量表型图像自动判定发育阶段。","2026-09-13T23:30:19.792952Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":9,"content":427,"source_name":428,"source_url":9,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":441,"secondary_topics":442,"classification":443,"search_phrases":465,"slug":468,"view_count":110,"doi":9,"paper":9,"created_at":469},541,"Towards Smart Agricultural Water–Nitrogen Management: A Multi-Criteria Decision Framework for Rapeseed Production in Southwest China Using EWM-TOPSIS Model","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542","针对中国西南油菜水氮利用效率低、智能水肥装备落地难的问题,基于两季田间试验(2 个灌溉水平 ×3 个施氮水平 + 雨养对照)耦合 EWM-TOPSIS 多准则评价模型。结果显示 W2F2 处理(100% ETc 灌溉 + 300 kg N\u002Fha)在两季分别提升叶片光合速率 51.2% 和 50.8%,在 EWM-TOPSIS 综合评价中两季均居首(S1 季贴近度 0.742,S2 季 0.650),在产量、品质、资源利用与经济效益间达到最优平衡。研究为西南丘陵油菜智能水肥装备应用提供了基于作物生理响应的决策优化框架。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agronomy-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagronomy-logo.png?010a42cd82f83819)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagronomy)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nRun Xue\n\n 1,2[](mailto:xue106700@163.com), \nYue Jiang\n\n 2,*[](mailto:jy261715267@126.com), \nHong Li\n\n 1, \nImran Ali Lakhiar\n\n 1,3[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-8590-4436) and \nJunjun Ran\n\n 4\n\n1\n\nResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China\n\n2\n\nSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China\n\n3\n\nFaculty of Agricultural Engineering and Technology, Sindh Agriculture University, Tandojam 70060, Pakistan\n\n4\n\nCollege of Water Resource and Hydropower, Sichuan University, Chengdu 650065, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 15 July 2026 \u002F Revised: 7 August 2026 \u002F Accepted: 10 August 2026 \u002F Published: 12 August 2026\n\n## Abstract\n\nTo address the limited synergy between integrated water–fertilizer technologies and intelligent application equipment, as well as the low water–nitrogen use efficiency in rapeseed production systems in southwestern China—which together constrain the large-scale adoption of smart fertigation equipment—this study conducted a two-season field experiment using an integrated water–fertilizer application system. The experiment included two irrigation regimes (W1: 60% ET c; W2: 100% ET c) and three nitrogen rates (F1: 220, F2: 300, F3: 360 kg N ha−1), plus a rainfed control (CK), to quantify rapeseed responses to water–nitrogen interactions under an equipment-based fertigation framework. Results showed that water–nitrogen interactions significantly regulated rapeseed physiological processes, growth, yield formation, and resource-use efficiency. Compared with CK, appropriate water and nitrogen supply markedly enhanced PSII efficiency and overall energy conversion. In particular, W2F2 increased leaf photosynthetic rate by 51.2% and 50.8% across the two seasons. Water–nitrogen coupling also improved yield components such as branch number and thousand-seed weight, thereby increasing final yield. Although nitrogen partial factor productivity declined with increasing N rates, smart fertigation improved economic returns to varying degrees. EWM-TOPSIS results indicated that W2F2 consistently achieved the highest comprehensive performance across both seasons. By balancing yield, seed quality, resource efficiency, and economic benefit, this treatment represents the optimal strategy under intelligent fertigation systems. Overall, this study provides a decision-oriented optimization framework based on crop physiological responses under smart water–fertilizer equipment, offering practical guidance for intelligent fertigation deployment in rapeseed systems in southwestern China.\n\n## 1. Introduction\n\nRapeseed (Brassica napus L.) is an economically important global crop valued for edible oil production and multiple-purpose utilization [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B1-agronomy-16-01542),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B2-agronomy-16-01542),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B3-agronomy-16-01542)]. In 2023, the global rapeseed planting area reached 43.45 million hectares. As the world’s second-largest producer, China contributes 17.96% of global rapeseed output [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B4-agronomy-16-01542)]. Winter rapeseed is a major cash crop in southwest China [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B5-agronomy-16-01542),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B6-agronomy-16-01542)]. Nevertheless, its long growth cycle and high water requirement conflict with frequent seasonal drought in this region. The seasonal precipitation of approximately 200 mm during the rapeseed growing period cannot meet the crop water demand of 300–500 mm [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B5-agronomy-16-01542)]. Drought stress thus severely restricts rapeseed yield, and supplementary irrigation is indispensable for local rapeseed production. Previous studies have demonstrated that over-winter supplementary irrigation improves the soil hydrothermal conditions and winter survival of rapeseed and mitigates spring drought damage [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B7-agronomy-16-01542),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B8-agronomy-16-01542),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B9-agronomy-16-01542)]. However, rapeseed is sensitive to both water shortage and surplus. Water deficit inhibits root activity and nitrogen acquisition, whereas excessive irrigation degrades soil aeration, lowers nitrogen-use efficiency and triggers nitrogen leaching-related environmental risks. Accordingly, rational supplementary irrigation is critical to balance rapeseed water demand and seasonal drought in Southwest China.\n\nNitrogen serves as another essential nutrient for winter rapeseed. Optimal nitrogen supply facilitates dry-matter accumulation, photosynthetic performance, yield and quality formation [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B10-agronomy-16-01542)]. Nevertheless, over-nitrogen application is widespread in local farming practice. Such fertilization patterns reduce nitrogen-use efficiency, raise nitrate leaching risks to groundwater, and increase lodging susceptibility, which eventually causes yield and quality losses [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B11-agronomy-16-01542),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B12-agronomy-16-01542),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B13-agronomy-16-01542)]. In traditional winter rapeseed production in southwestern China, single-dimensional nitrogen or irrigation management cannot simultaneously achieve high yield, superior quality, high resource efficiency and environmental sustainability [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B14-agronomy-16-01542)]. Driven by advances in smart agricultural hardware and precision water–fertilizer technologies, intelligent irrigation and variable-rate fertigation platforms offer promising solutions to improve water–nitrogen use efficiency for rainfed croplands [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B15-agronomy-16-01542),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B16-agronomy-16-01542)]. Such systems support timely, quantitative and site-specific water and nutrient supply, greatly departing from traditional extensive field management [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B17-agronomy-16-01542),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B18-agronomy-16-01542)]. Nevertheless, large-scale field deployment of these smart devices is still constrained by heterogeneous field conditions and the shortage of field-validated water–nitrogen regulation strategies. The mismatch between theoretical optimization and practical field performance further limits their popularization [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B19-agronomy-16-01542),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B20-agronomy-16-01542)]. Accordingly, it is necessary to consider the practical field adaptability of integrated smart fertigation equipment, and clarify rapeseed physiological, yield-quality and resource-use responses under equipment-driven water–nitrogen combinations, so as to develop practical precision management strategies.\n\nPreite et al. [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B21-agronomy-16-01542)] integrated soil and environmental sensor data with short-term weather conditions to develop an intelligent irrigation management system, enabling automated and intelligent irrigation regulation. Chen et al. [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B22-agronomy-16-01542)] combined the DSSAT crop model with distributed reinforcement learning techniques to establish a multi-source information-driven precision intelligent irrigation regulation system, which significantly improved cotton irrigation water-use efficiency and crop yield. However, existing intelligent irrigation and fertigation systems mainly rely on external information, such as soil moisture and meteorological conditions, for water–nitrogen regulation, while lacking water–nitrogen management strategies driven by crop physiological feedback. Under complex field conditions, environmental parameter-based water–nitrogen optimization strategies cannot fully capture real-time crop requirements, thereby limiting the precision application and broader adoption of intelligent irrigation and fertigation technologies. Nowadays, advanced crop intelligent monitoring technologies enable real-time acquisition of crop physiological status, providing reliable information for determining water and fertilizer input requirements in fertigation systems [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B23-agronomy-16-01542),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B24-agronomy-16-01542)]. Among these physiological indicators, chlorophyll fluorescence parameters reflect photosystem activity and stress levels, and can sensitively characterize the damage of abiotic stress to photosynthetic apparatus [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B25-agronomy-16-01542),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B26-agronomy-16-01542)]. Gas exchange parameters, such as photosynthetic rate, directly represent plant carbon assimilation capacity and growth status, serving as important physiological feedback signals for precision water–nitrogen decision-making [[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B27-agronomy-16-01542),[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B28-agronomy-16-01542)]. Nevertheless, multiple dimensions, including physiological responses, yield, quality, and resource-use efficiency, need to be comprehensively considered to identify optimal water–nitrogen management strategies rather than relying on single-indicator evaluation.\n\nThe entropy weight-TOPSIS (EWM-TOPSIS) is an objective multi-criteria decision-making tool that mitigates information redundancy and subjective weighting bias [[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B29-agronomy-16-01542),[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B30-agronomy-16-01542)]. In this framework, indicator weights are objectively quantified via entropy weighting according to experimental data variation, followed by TOPSIS calculation of distances to positive- and negative-ideal solutions to obtain relative closeness coefficients for treatment ranking. Widely adopted in crop water–fertilizer assessment, this method can reliably distinguish the comprehensive performance of different agronomic treatments [[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B31-agronomy-16-01542),[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B32-agronomy-16-01542)]. Therefore, the objectives of this study are (1) to analyze how irrigation–nitrogen regimes regulated by integrated smart fertigation equipment affect rapeseed growth, chlorophyll fluorescence and seed quality, and to reveal the regulatory mechanism of equipment-mediated water–nitrogen interaction on crop growth and quality formation; (2) to evaluate water–nitrogen use efficiency as well as economic input–output performance across different water–fertilizer coupling treatments, and quantify resource-use and economic responses under equipment-based management; and (3) to establish an EWM-TOPSIS-based multi-criteria optimization framework for winter rapeseed under smart fertigation, and determine the optimal management strategy for green-efficient production in southwestern China. This work provides theoretical support and decision-making references for parameter tuning and large-scale application of intelligent irrigation and variable fertilization equipment in rapeseed planting.\n\n## 2. Materials and Methods\n\n### 2.1. Experimental Site\n\nThe field experiment was conducted over two consecutive winter rapeseed growing seasons from October 2024 to May 2026 at Wufangpo Village, Daying County, Sichuan Province, China (105°08′ E, 30°34′ N; 377 m above sea level). The experimental site is located in the central Sichuan Basin and is characterized by a subtropical humid monsoon climate, with a mean annual air temperature of 16.5 °C, annual precipitation of approximately 930 mm, annual sunshine duration of 1380 h, and a frost-free period of approximately 279 days. The experimental soil was classified as a silt loam according to the USDA soil texture classification, with a sand:silt:clay ratio of 34:58:8. Prior to the experiment, composite soil samples were collected from the 0–20 cm soil layer to determine the initial physicochemical properties. The soil had a bulk density of 1.37 g cm−3, a field water-holding capacity of 0.42 cm 3 cm−3, and a permanent wilting point of 0.19 cm 3 cm−3. The soil pH was 6.8, with a soil organic matter content of 21.1 g kg−1, total nitrogen of 0.91 g kg−1, available phosphorus of 8.71 mg kg−1, and available potassium of 147.8 mg kg−1.\n\n[Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#fig_body_display_agronomy-16-01542-f001) presents the long-term (1974–2023) monthly climatic characteristics of the experimental site based on data obtained from the National Meteorological Data Service Center, including monthly maximum (T max), mean (T avg), and minimum (T min) air temperatures, average relative humidity (RH avg), sunshine duration (S d), and precipitation (rain). The lowest temperatures occurred in January, while most precipitation was concentrated in April and May. In contrast, cumulative precipitation during the winter rapeseed growing period from November to March was only 101.6 mm, which is insufficient to satisfy the crop water requirement without supplemental irrigation. The daily variations in precipitation, maximum, mean, and minimum air temperatures, and relative humidity during the two experimental seasons are presented in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#fig_body_display_agronomy-16-01542-f002). Total precipitation during the winter rapeseed growing period was 187.6 mm and 245.4 mm in the first and second seasons, respectively. The corresponding mean daily air temperatures were 11.1 °C and 12.4 °C, while the average relative humidities were 89.9% and 88.5%, respectively. The cumulative solar radiation reached respective values of 1295.7 and 1285.1 MJ m−2 during the two growing seasons.\n\n**Figure 1.** Meteorological data from the experimental site over the past 50 years.\n\n**Figure 2.** Meteorological data from the experimental site in the S1 (**a**) and S2 (**b**) seasons.\n\n### 2.2. Experimental Design\n\nThe experimental crop management and basal fertilization practices were consistent with local farmers’ conventional methods. The rapeseed cultivar used in this study was the hybrid variety “Xianyou 188”. Seedlings were transplanted manually on 6 November 2024, and 4 November 2025, with corresponding harvest dates on 6 May 2025, and 2 May 2026. The experiment included two irrigation levels (W1: low irrigation, 60% ET c; W2: high irrigation, 100% ET c) and three nitrogen application rates (F1: 220 kg N ha−1; F2: 300 kg N ha−1; F3: 360 kg N ha−1), resulting in six water–nitrogen coupling treatments. In addition, a rainfed control without nitrogen fertilization (CK) was included, giving a total of seven treatments. Each treatment was replicated three times, resulting in 21 plots arranged in a randomized complete block design. The planting density of rapeseed is 83,333 plants per hectare, with uniform planting density across all treatments. Before transplanting, a uniform basal application of rapeseed-specific compound fertilizer (N:P 2 O 5:K 2 O = 25:10:10, total nutrients ≥ 40%) was applied at 720 kg ha−1, corresponding to 180 kg N ha−1. This was used to ensure sufficient nutrient availability during early seedling establishment and to promote vigorous root and shoot development. During the overwintering and bolting stages, irrigation and topdressing nitrogen were applied at a ratio of 4:6. The total topdressing nitrogen amounts for N1, N2, and N3 were 60, 120, and 180 kg N ha−1, respectively. All irrigation events were conducted using a sprinkler system under non-rainy conditions with wind speed below 3 m s−1 to minimize meteorological interference. Other field management practices, including pest control and weed management, were carried out uniformly following local conventional agronomic practices. The intelligent fertigation system used in this study was independently developed and manufactured by Jiangsu University. The system mainly consisted of a fertigation controller, fertilizer mixing tank, pipeline system, and intelligent control unit. The sensing module was integrated with multiple sensors to enable real-time acquisition of environmental information, including soil moisture content and meteorological conditions. These data were used to calculate crop evapotranspiration (ET c) and determine irrigation water requirements.\n\n### 2.3. Physiological Indicators\n\nThe chlorophyll fluorometer (ChlF) was monitored using a portable photosynthesis system (MultispeQ V2.0, PHOTOSYNQ INC, East Lansing, MI, USA). Saturation pulse method (PAM) technology was used to measure ChlF parameters, while simultaneously recording leaf temperature, photosynthetically active radiation (PAR), leaf thickness, SPAD, and environmental temperature and humidity in the field. The ChlF contains the maximum quantum yield of PSII (Fv\u002FFm), the actual quantum yield of PSII (Φ PSII), the relative photosynthetic electron transfer rate (ETR), the photochemical quenching (qP), and the non-photochemical quenching (NPQ). The Fv\u002FFm and Φ PSII represents the maximum and actual proportion of energy absorbed by the crop that can be used for photosynthetic processes, respectively. In the condition without any stress, the plant’s Fv\u002FFm is generally 0.84, which means that 84% of the energy is converted to the photosynthetic process. The qL represents the proportion of open-state photosystem II reaction centers and it reflects the level of photosynthetic activity. The NPQ represents the proportion of energy absorbed by photosystem II that is used to dissipate as heat, and the ability of plants to dissipate excess light energy as heat. When measuring leaf physiological parameters at different growth stages of rapeseed, representative functional leaves were selected. During the seedling stage, the second and third fully expanded true leaves at the top were selected; during the bolting stage, the third and fourth functional leaves below the main stem apex were selected; during the flowering stage, the first and second fully expanded leaves below the main inflorescence were selected; and during the silique development stage, the upper functional leaves adjacent to the siliques were measured. All measurements were conducted on sunny days between 9:00 and 11:00 a.m. and at night under no sunlight. For each growth stage, three plants were randomly selected from different treatments and arranged in a diagonal pattern to measure leaf ChlF parameters, avoiding leaves affected by disease, damage, or shading to ensure that the data accurately reflected the true photosynthetic physiological dynamics of the rapeseed. The quantum yield of regulated energy dissipation in photosystem II (Φ NPQ) represents the fraction of excitation energy absorbed by PSII that is dissipated as heat through regulated photoprotective mechanisms. The quantum yield of non-regulated energy dissipation in photosystem II (Φ NO) represents the fraction of excitation energy absorbed by PSII that is passively dissipated as heat and re-emitted","Agronomy","2026-08-11T22:00:00Z",80,{"impact":19,"substance":18,"depth":123,"authority":20,"freshness":110,"relevant":22,"comment":432},"研究提出基于EWM-TOPSIS的水氮智能管理决策框架，为西南油菜智能水肥一体化提供优化方案，方法新颖且数据详实，但影响范围限于区域细分领域。",[434],{"name":428,"url":425},[27,436,51,28,437,438,439,440],"农业物联网","丘陵山区","智能水肥","EWM-TOPSIS","中国西南",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":444},{"candidates":445,"agricultural_evidence":47},[446,450,456,460],{"code":33,"score":122,"supported":47,"title":447,"abstract":448,"tags":449},[57],[28],[28],{"code":451,"score":62,"supported":47,"title":452,"abstract":453,"tags":455},"H3",[],[454],"灌溉",[],{"code":46,"score":200,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[27,51],{"code":461,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},"I5",[],[],[436],[466,467],"农业物联网 丘陵山区 中国西南 智慧农业","农业物联网 丘陵山区","农业物联网丘陵山区中国西南智慧农业-541","2026-08-15T00:02:55.759543Z"]