[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2123":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":57},2123,"Determining blooming-phase dynamics through intra-day hive-weight analysis","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102559","This study analyses intra-day beehive weight dynamics across previously delimited blooming periods, using the remote temporal analysis of hive weight data to focus on weight variations throughout the day. The data were collected at the University of Córdoba’s experimental apiary using the Wbee remote monitoring system. Hive weights were recorded every 5 minutes over two consecutive beekeeping seasons from fifteen hives (six in 2016, nine in 2017). The analysis examined temporal intra-day weight-variation patterns associated with pre-bloom, bloom and post-bloom phases and related daily weight variation to environmental conditions and bee activity. Daily hive-weight amplitude differed significantly between blooming phases (Kruskal–Wallis 𝐻 = 1 8 5 . 6 0 6 , 𝑝 = 4 . 9 6 8 × 1 0 − 4 1 ), and was highest during the Bloom phase (0.76 ± 0.55 kg) compared with Post-bloom (0.50 ± 0.98 kg) and Pre-bloom (0.47 ± 0.26 kg), with the timing of daily maximum weight also differing significantly between phases (Kruskal–Wallis 𝐻 = 1 5 7 . 1 2 2 , 𝑝 = 7 . 6 1 1 × 1 0 − 3 5 ). These results demonstrate the value of hive weight as one key metric for precision beekeeping. The observed temporal patterns may contribute to the development of future monitoring frameworks for colony management and environmental assessment. The findings also highlight the potential utility of continuous hive-weight monitoring for studying colony responses to environmental variability, while the small sample size and single-apiary design of this exploratory study call for validation across additional sites and seasons.","本研究基于先前界定的开花期，分析蜂箱日内重量动态，利用蜂箱重量数据的远程时间序列分析，重点考察一天内的重量变化。数据通过Wbee远程监测系统在科尔多瓦大学实验蜂场采集。在连续两个养蜂季节中，对15个蜂箱（2016年6个，2017年9个）每5分钟记录一次蜂箱重量。分析考察了与花前期、开花期和花后期相关的时间日内重量变化模式，并将每日重量变化与环境条件和蜜蜂活动联系起来。蜂箱每日重量振幅在开花阶段之间差异显著（Kruskal–Wallis 𝐻 = 185.606，𝑝 = 4.968 × 10⁻⁴¹），且在开花期最高（0.76 ± 0.55 kg），高于花后期（0.50 ± 0.98 kg）和花前期（0.47 ± 0.26 kg）；每日最大重量的出现时间在不同阶段之间也差异显著（Kruskal–Wallis 𝐻 = 157.122，𝑝 = 7.611 × 10⁻³⁵）。这些结果表明，蜂箱重量是精准养蜂的一项关键指标。所观察到的时间模式可能有助于未来用于蜂群管理和环境评估的监测框架的开发。研究结果还凸显了连续蜂箱重量监测在研究蜂群对环境变异性响应方面的潜在效用，同时这项探索性研究样本量小且仅涉及单一蜂场，因此需要在更多地点和季节进行验证。",null,"Smart Agricultural Technology","2026-09-09T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,13,7,1,"基于蜂箱重量日内动态识别开花期的探索性研究，方法新颖、数据连续，但样本小、单站点，属细分领域进展。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","物联网","精准养蜂","蜂群监测","授粉管理",0,"10.1016\u002Fj.atech.2026.102559",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":49,"card":50,"direction":54,"ingested_from":56},"W7212032505",[37,40,42,45,47],{"name":38,"orcid":39},"José Luis Ávila-Jiménez","https:\u002F\u002Forcid.org\u002F0000-0001-8006-8256",{"name":41,"orcid":9},"Francisco J. Rodriguez-Lozano",{"name":43,"orcid":44},"Manuel Ortíz-López","https:\u002F\u002Forcid.org\u002F0000-0001-8312-1729",{"name":46,"orcid":9},"Hector Martínez",{"name":48,"orcid":9},"Jose M. Flores","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007847\u002Fpdf",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"通过日内蜂箱重量动态分析，识别开花前中后阶段的蜂群活动差异。","Wbee系统每5分钟监测15个蜂箱重量，跨两个养蜂季，用Kruskal-Wall","开花期日内重量振幅最大（0.76kg），且日最大重量出现时间在阶段间显著不同。","智慧农业 \u002F 农业物联网","可扩展多站点多季节验证，并融合环境传感器与机器学习构建精准蜂群管理预警模型。","openalex","2026-09-11T23:30:03.883635Z"]