[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1718":3,"related-1718":110},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":109},1718,"Prescription-Map-Guided Bi-Level Multi-Objective Path Planning for UAV–UGV Collaborative Spraying and Fertilization in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fdrones10090677","Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty.","变量率农药喷洒与施肥作业要求异构农业机械协同运行，尤其是在不规则农田中，任务需求、车辆机动性、载重能力、能耗及补给需求均存在空间差异。然而，现有研究大多分别优化空中喷洒或地面施肥，未联合考虑处方图需求、空地同步、农药飘移风险及农业车辆约束。本研究将无人机喷洒与无人车施肥的协同作业建模为多目标混合整数非线性规划问题，包含三个优化目标：最小化系统总完工时间、加权能耗及农药飘移惩罚。提出一种处方图引导的双层规划框架。在上层，问题专用求解器TNSAOO确定无人机与无人车的任务序列及协同补给点激活方案；在下层，自适应Theta*算法与行约束Hybrid A*算法分别生成无人机喷洒与无人车施肥轨迹，同时沿有效作业段分配依赖处方图的施药指令，并通过时间窗机制检测和修正残余空地冲突。该框架基于30个真实农田边界和90个随机处方场景进行评估。无人机喷洒与无人车施肥的平均几何覆盖率分别达到98.82%和98.95%，平均处方符合性误差分别为6.21%和2.13%。此外，96.7%的批次运行中检测到的空地冲突不超过一次，平均修正等待时间为1.07秒。与传统独立作业相比，协同规划使平均系统完工时间降低9.32%，加权能耗降低7.21%，模型化飘移惩罚降低3.62%，总路径长度降低5.79%。在多目标对比中，TNSAOO获得的平均超体积（hypervolume）为0.597，平均反转世代距离（inverted generational distance）为0.375，相较于既有对比算法（尤其是NSGA-II）展现出具有竞争力的Pareto搜索性能。额外的地形与飘移敏感性分析表明，在受控参数扰动下，能耗、完工时间及模型化飘移风险均产生系统性变化。上述研究结果验证了在共享处方图下对异构变量率喷洒与施肥进行联合规划的仿真可行性。",null,"Drones","2026-09-04T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,3,1,"提出处方图引导的无人机-无人车协同变量喷施与施肥双层规划框架，仿真验证效果显著，对智慧农业装备协同作业有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业机器人","路径规划","变量作业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,60,68,75],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":52},17,true,[48],"fertilization",[50,51,48],"施肥","fertilizer",[],{"code":54,"score":55,"supported":46,"title":56,"abstract":58,"tags":59},"I1",9,[57],"smart agriculture",[],[27],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":67},"F4",6,[],[65,66],"农药","pesticide",[],{"code":69,"score":70,"supported":46,"title":71,"abstract":72,"tags":74},"F5",4,[],[73],"农业机械",[29],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"F6",[],[],[28],[81,82],"农业机器人 变量作业 智慧农业 路径规划","农业机器人 变量作业","农业机器人变量作业智慧农业路径规划-1718",2,"10.3390\u002Fdrones10090677",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7208727280",[89,92,95,98],{"name":90,"orcid":91},"Shiyang Li","https:\u002F\u002Forcid.org\u002F0000-0003-2675-0419",{"name":93,"orcid":94},"Jisong Lv","https:\u002F\u002Forcid.org\u002F0009-0007-5060-0521",{"name":96,"orcid":97},"Yuchen Lu","https:\u002F\u002Forcid.org\u002F0009-0003-3027-6462",{"name":99,"orcid":100},"Yuxuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8617-0435",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"提出处方图引导的无人机-无人车协同变量喷洒施肥双层多目标路径规划方法，实现高效协同作业。","双层框架：上层TNSAOO求解任务序列与补给点，下层自适应Theta*和Hybr","协同规划较独立作业减少9.32%时间、7.21%能耗，覆盖率约99%，处方符合率高，冲突少。","智慧农业 \u002F 农业物联网","可探索实际田间验证、动态处方图更新、多机协同鲁棒性，以及将方法扩展至其他农业任务。","openalex","2026-09-05T23:30:08.846450Z",{"total":62,"page":22,"page_size":62,"items":111},[112,179,252,333,424,468],{"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":131,"secondary_topics":136,"classification":138,"search_phrases":158,"slug":161,"view_count":70,"doi":162,"paper":163,"created_at":178},1754,"Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112349","Low-cost autonomous unmanned ground vehicle for in-situ phosphorus screening to support zone-based fertilization in maize crops。Computers and Electronics in Agriculture","低成本自主无人地面车辆用于玉米作物原位磷筛查以支持分区施肥。农业计算机与电子","Computers and Electronics in Agriculture","2026-09-05T00:00:00Z",62,{"impact":122,"substance":17,"depth":123,"authority":124,"freshness":84,"relevant":22,"comment":125},12,16,14,"低成本无人车用于磷素原位检测，支持玉米分区施肥，技术可行但时效性低。",[127],{"name":118,"url":115},[27,28,129,130],"变量施肥","玉米",{"code":132,"slug":133,"name":130,"path":134,"parent_code":135},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[137],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":139},{"candidates":140,"agricultural_evidence":46},[141,146,150,154],{"code":132,"score":142,"supported":46,"title":143,"abstract":144,"tags":145},15,[133],[130,133],[130],{"code":32,"score":142,"supported":46,"title":147,"abstract":148,"tags":149},[48],[50,48],[129],{"code":76,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[28],{"code":54,"score":22,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[27],[159,160],"农业机器人 变量施肥 智慧农业 玉米","农业机器人 变量施肥","农业机器人变量施肥智慧农业玉米-1754","10.1016\u002Fj.compag.2026.112349",{"doi":162,"openalex_id":164,"authors":165,"venue":118,"cited_by_count":101,"oa_url":9,"card":173,"direction":106,"ingested_from":108},"W7208817219",[166,168,171],{"name":167,"orcid":9},"Lalo Villacorta",{"name":169,"orcid":170},"Adrian Medrano","https:\u002F\u002Forcid.org\u002F0009-0009-2010-3828",{"name":172,"orcid":9},"Miguel Lara",{"tldr":174,"method":175,"finding":176,"direction":106,"opportunity":177},"研发低成本自主地面机器人，原位筛查玉米磷含量，支持分区施肥。","低成本UGV搭载原位传感器，结合自主导航与磷筛查技术。","实现低成本、自主的田间磷检测，为分区施肥提供依据。","可延伸研究多养分原位传感与自主决策，或结合无人机与地面机器人协同，提升精准施肥效率。","2026-09-06T23:30:01.426286Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":187,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":196,"primary_topic":201,"secondary_topics":202,"classification":203,"search_phrases":234,"slug":237,"view_count":101,"doi":238,"paper":239,"created_at":251},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",25,50,{"impact":190,"substance":123,"depth":20,"authority":70,"freshness":55,"relevant":22,"comment":191},8,"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。",[193,194],{"name":185,"url":182},{"name":185,"url":195},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23471101",[27,197,198,199,200],"无损检测","图像识别","香蕉","营养诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,213,218,224,229],{"code":54,"score":207,"supported":46,"title":208,"abstract":209,"tags":212},7,[],[210,211],"精准农业","precision agriculture",[27],{"code":32,"score":21,"supported":46,"title":214,"abstract":215,"tags":217},[],[216],"养分管理",[],{"code":219,"score":21,"supported":46,"title":220,"abstract":221,"tags":223},"I7",[],[222],"研究方法",[],{"code":225,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},"B2",[],[],[199],{"code":230,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},"I2",[],[],[198],[235,236],"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","10.5281\u002Fzenodo.23470857",{"doi":238,"openalex_id":240,"authors":241,"venue":185,"cited_by_count":101,"oa_url":182,"card":244,"direction":250,"ingested_from":108},"W7222963764",[242],{"name":243,"orcid":9},"Anantjit Publication",{"tldr":245,"method":246,"finding":247,"direction":248,"opportunity":249},"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","2026-10-20T23:30:23.713550Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":270,"classification":271,"search_phrases":289,"slug":292,"view_count":101,"doi":293,"paper":294,"created_at":332},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":17,"substance":18,"depth":17,"authority":124,"freshness":55,"relevant":22,"comment":262},"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。",[264],{"name":258,"url":255},[27,210,266,267,268],"控释肥","温度响应","蓖麻油基材料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":272},{"candidates":273,"agricultural_evidence":46},[274,279,283],{"code":32,"score":124,"supported":46,"title":275,"abstract":276,"tags":278},[51],[277,51],"肥料",[],{"code":54,"score":190,"supported":46,"title":280,"abstract":281,"tags":282},[],[210,211],[27,210],{"code":284,"score":21,"supported":46,"title":285,"abstract":286,"tags":288},"H7",[],[287],"气候适应",[],[290,291],"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":293,"openalex_id":295,"authors":296,"venue":258,"cited_by_count":101,"oa_url":255,"card":327,"direction":250,"ingested_from":108},"W7222872607",[297,299,302,304,306,309,311,313,315,318,321,324],{"name":298,"orcid":9},"Jiahui Liu",{"name":300,"orcid":301},"Li J","https:\u002F\u002Forcid.org\u002F0009-0008-8760-1365",{"name":303,"orcid":9},"Ziyao Li",{"name":305,"orcid":9},"Xiangjie Zhao",{"name":307,"orcid":308},"Fengyan Fang","https:\u002F\u002Forcid.org\u002F0009-0009-0081-4796",{"name":310,"orcid":9},"Yan Liu",{"name":312,"orcid":9},"Songyuan Wang",{"name":314,"orcid":9},"Boning Su",{"name":316,"orcid":317},"Yuanyuan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-7439-2970",{"name":319,"orcid":320},"Bin Gao","https:\u002F\u002Forcid.org\u002F0000-0003-3769-0191",{"name":322,"orcid":323},"Shugang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1428-9082",{"name":325,"orcid":326},"Yuechao Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4045-0252",{"tldr":328,"method":329,"finding":330,"direction":106,"opportunity":331},"研发含F127温敏开关的生物基包膜控释肥，实现按温度按需释放养分。","蓖麻油基聚氨酯包膜中嵌入Poloxamer 407作热开关，数字折光法测氮释放。","10℃时30天氮累积释放仅9.22%，30℃时增至80.32%，提升8.7倍。","可探索多响应（温湿\u002FpH）智能包膜与田间作物需肥曲线耦合的精准释放调控。","2026-10-19T23:30:28.317764Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":9,"source_name":339,"source_url":336,"published_at":340,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":341,"score_detail":342,"sources":344,"tags":346,"primary_topic":351,"secondary_topics":352,"classification":353,"search_phrases":406,"slug":409,"view_count":84,"doi":410,"paper":411,"created_at":423},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",83,{"impact":18,"substance":18,"depth":17,"authority":20,"freshness":190,"relevant":22,"comment":343},"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。",[345],{"name":339,"url":336},[27,347,348,129,349,350],"无人机","精准施肥","遥感监测","养分利用效率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":354},{"candidates":355,"agricultural_evidence":46},[356,363,370,377,384,388,395,402],{"code":32,"score":357,"supported":46,"title":358,"abstract":359,"tags":362},20,[48],[50,277,360,216,361],"水肥一体化","fertigation",[350,129,348],{"code":364,"score":207,"supported":46,"title":365,"abstract":366,"tags":369},"I3",[],[367,368],"遥感","remote sensing",[349],{"code":371,"score":62,"supported":46,"title":372,"abstract":373,"tags":376},"A1",[],[374,375],"水稻","rice",[],{"code":378,"score":62,"supported":46,"title":379,"abstract":380,"tags":383},"A2",[],[381,382],"小麦","wheat",[],{"code":132,"score":62,"supported":46,"title":385,"abstract":386,"tags":387},[],[130,133],[],{"code":389,"score":62,"supported":46,"title":390,"abstract":391,"tags":394},"H2",[],[392,393],"土壤","soil",[],{"code":396,"score":62,"supported":46,"title":397,"abstract":398,"tags":401},"I4",[],[399,400],"地理信息","geographic information",[],{"code":54,"score":22,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[27],[407,408],"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":410,"openalex_id":412,"authors":413,"venue":339,"cited_by_count":101,"oa_url":336,"card":418,"direction":106,"ingested_from":108},"W7222838957",[414,416],{"name":415,"orcid":9},"S. T. Panusha",{"name":417,"orcid":9},"T. M. Soumya",{"tldr":419,"method":420,"finding":421,"direction":106,"opportunity":422},"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":425,"title":426,"url":427,"summary":428,"summary_zh":9,"content":9,"source_name":429,"source_url":9,"published_at":430,"category":431,"cover_url":9,"hotness":187,"is_selected":14,"score":432,"score_detail":433,"sources":435,"tags":440,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":463,"slug":466,"view_count":101,"doi":9,"paper":9,"created_at":467},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","报道",68,{"impact":17,"substance":123,"depth":122,"authority":124,"freshness":190,"relevant":22,"comment":434},"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。",[436,437],{"name":429,"url":427},{"name":438,"url":439},"新浪财经(2026-10-15)","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-10-15\u002Fdoc-inivhyai8038690.shtml",[27,441,347,442,443],"低空经济","农业保险","农业标准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":447},{"candidates":448,"agricultural_evidence":46},[449,454,459],{"code":32,"score":450,"supported":46,"title":451,"abstract":452,"tags":453},11,[50],[50],[],{"code":455,"score":22,"supported":14,"title":456,"abstract":457,"tags":458},"G3",[],[],[442],{"code":54,"score":22,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[27],[464,465],"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":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":9,"source_name":474,"source_url":471,"published_at":475,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":485,"secondary_topics":486,"classification":487,"search_phrases":508,"slug":511,"view_count":101,"doi":512,"paper":513,"created_at":555},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":190,"substance":357,"depth":123,"authority":20,"freshness":55,"relevant":22,"comment":478},"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。",[480],{"name":474,"url":471},[27,482,197,483,484],"绿豆","作物表型","RGB成像",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":488},{"candidates":489,"agricultural_evidence":46},[490,494,499,504],{"code":32,"score":55,"supported":46,"title":491,"abstract":492,"tags":493},[],[50,51,48],[],{"code":364,"score":207,"supported":46,"title":495,"abstract":496,"tags":498},[],[483,497],"crop phenotyping",[483],{"code":500,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},"A5",[],[],[482],{"code":54,"score":22,"supported":14,"title":505,"abstract":506,"tags":507},[],[],[27],[509,510],"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":512,"openalex_id":514,"authors":515,"venue":474,"cited_by_count":101,"oa_url":471,"card":550,"direction":248,"ingested_from":108},"W7221615335",[516,519,522,525,528,531,534,537,540,543,545,547],{"name":517,"orcid":518},"Eder Pereira Neves","https:\u002F\u002Forcid.org\u002F0000-0001-9645-7061",{"name":520,"orcid":521},"Jorge González Aguilera","https:\u002F\u002Forcid.org\u002F0000-0002-7308-0967",{"name":523,"orcid":524},"Werverth Costa Martins","https:\u002F\u002Forcid.org\u002F0000-0002-2813-4276",{"name":526,"orcid":527},"Roseline da Silva Coêlho","https:\u002F\u002Forcid.org\u002F0000-0002-4412-7471",{"name":529,"orcid":530},"Fábio Steiner","https:\u002F\u002Forcid.org\u002F0000-0001-9091-1737",{"name":532,"orcid":533},"Marco Aparecido Queiroz Duarte","https:\u002F\u002Forcid.org\u002F0000-0003-2494-448X",{"name":535,"orcid":536},"Alan Mário Zuffo","https:\u002F\u002Forcid.org\u002F0000-0001-9704-5325",{"name":538,"orcid":539},"César Augusto Masgo Soto","https:\u002F\u002Forcid.org\u002F0000-0003-0720-0833",{"name":541,"orcid":542},"Carlos Genaro Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-4184-2365",{"name":544,"orcid":9},"María Paulina Aliaga Martínez",{"name":546,"orcid":9},"Nataniel Linares-Gutiérrez",{"name":548,"orcid":549},"Luis Fortunato Morales Aranibar","https:\u002F\u002Forcid.org\u002F0000-0002-9421-9833",{"tldr":551,"method":552,"finding":553,"direction":248,"opportunity":554},"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"]