[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2630":3,"related-2630":100},{"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":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":99},2630,"Effects of microwave heating on concentration properties and quality attributes of blueberry–grape blended pulp","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1920261","Blended blueberry–grape pulp (BBGP) was concentrated using microwave heating concentration (MHC) to evaluate the effects of microwave intensity (2–6 W\u002Fg), blueberry-to-grape mass ratio (1:1–1:7), citric acid addition (0.2%–0.4%), and granulated sugar addition (10%–15%) on temperature, moisture content, anthocyanin retention ratio, soluble solids content, and sensory quality. Microwave intensity and blueberry-to-grape mass ratio each exhibited a biphasic effect on anthocyanin retention and sensory scores, with optimal levels around 4 W\u002Fg and 1:3 mass ratio, whereas soluble solids content remained unaffected by variations in these parameters or citric acid concentration. Citric acid addition enhanced anthocyanin stability but impaired palatability at excessive levels, while granulated sugar addition did not significantly influence anthocyanin retention yet effectively modulated soluble solids and sweet-sour balance, with 12.5% yielding the highest sensory performance. The recommended processing parameters for MHC of BBGP are 4–6 W\u002Fg, 1:3 blueberry-to-grape mass ratio, 0.3% citric acid, and 12.5% granulated sugar. These findings provide practical guidance for optimizing MHC processes to produce high-quality blended fruit concentrates such as BBGP.","采用微波加热浓缩（MHC）对蓝莓-葡萄混合果浆（BBGP）进行浓缩，以评估微波强度（2–6 W\u002Fg）、蓝莓与葡萄质量比（1:1–1:7）、柠檬酸添加量（0.2%–0.4%）和砂糖添加量（10%–15%）对温度、水分含量、花青素保留率、可溶性固形物含量和感官品质的影响。微波强度和蓝莓与葡萄质量比均对花青素保留率和感官评分表现出双相效应，最佳水平分别约为4 W\u002Fg和质量比1:3，而可溶性固形物含量不受这些参数或柠檬酸浓度变化的影响。添加柠檬酸增强了花青素稳定性，但过量会损害适口性；添加砂糖对花青素保留率无显著影响，但能有效调节可溶性固形物和酸甜平衡，其中12.5%的添加量获得最佳感官表现。BBGP微波加热浓缩的推荐工艺参数为：微波强度4–6 W\u002Fg、蓝莓与葡萄质量比1:3、柠檬酸0.3%、砂糖12.5%。这些发现为优化微波加热浓缩工艺以生产BBGP等高品质混合果汁浓缩液提供了实践指导。",null,"Frontiers in Sustainable Food Systems","2026-09-15T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,13,9,1,"蓝莓葡萄复合果浆微波浓缩工艺优化的实证研究，给出可落地的参数组合，对果蔬加工与品质保持有参考价值，但属细分领域技术论文，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农产品加工","花青素","葡萄","蓝莓","微波浓缩",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},16,true,[49],"grape",[29,49],[29,30],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"E1",[],[],[27],[58,59],"农产品加工 微波浓缩 花青素 葡萄","农产品加工 微波浓缩","农产品加工微波浓缩花青素葡萄-2630",3,"10.3389\u002Ffsufs.2026.1920261",{"doi":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":91,"oa_url":6,"card":92,"direction":96,"ingested_from":98},"W7213252471",[66,68,70,73,75,77,79,81,83,85,87,89],{"name":67,"orcid":9},"Xinglong Zhao",{"name":69,"orcid":9},"A. Wangze",{"name":71,"orcid":72},"Yuhan Zhang","https:\u002F\u002Forcid.org\u002F0009-0009-0505-6322",{"name":74,"orcid":9},"Chunhao Kan",{"name":76,"orcid":9},"Yuedong Wang",{"name":78,"orcid":9},"Chenghai Liu",{"name":80,"orcid":9},"Chai Liu",{"name":82,"orcid":9},"Wei Zhao",{"name":84,"orcid":9},"Gang Shi",{"name":86,"orcid":9},"Chao Ma",{"name":88,"orcid":9},"Li Xi",{"name":90,"orcid":9},"Jie Jiang",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"研究微波加热浓缩蓝莓-葡萄混合果浆的工艺参数对品质的影响。","微波加热浓缩，考察微波强度、配比、柠檬酸和糖添加量。","最佳工艺为4-6 W\u002Fg、1:3配比、0.3%柠檬酸、12.5%糖，花青素保留和感官最佳。","其他","可探索微波浓缩中花青素降解动力学与在线监测，结合智能控制优化品质。","openalex","2026-09-16T23:30:07.980146Z",{"total":101,"page":22,"page_size":101,"items":102},6,[103,149,212,292,349,390],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":9,"content":108,"source_name":109,"source_url":9,"published_at":110,"category":111,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":116,"tags":118,"primary_topic":123,"secondary_topics":124,"classification":125,"search_phrases":144,"slug":147,"view_count":91,"doi":9,"paper":9,"created_at":148},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",65,{"impact":46,"substance":114,"depth":20,"authority":20,"freshness":101,"relevant":22,"comment":115},17,"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[117],{"name":109,"url":106},[119,120,29,121,122],"智慧农业","智能农机","栽培技术","国际交流",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":126},{"candidates":127,"agricultural_evidence":47},[128,133,139],{"code":33,"score":129,"supported":47,"title":130,"abstract":131,"tags":132},12,[29],[29],[29],{"code":134,"score":135,"supported":47,"title":136,"abstract":137,"tags":138},"I1",4,[],[119],[119],{"code":140,"score":22,"supported":14,"title":141,"abstract":142,"tags":143},"F5",[],[],[120],[145,146],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":163,"tags":165,"primary_topic":169,"secondary_topics":170,"classification":171,"search_phrases":188,"slug":191,"view_count":61,"doi":192,"paper":193,"created_at":211},3255,"Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112395","Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m\u002Fs (3.7 km\u002Fh), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.","准确的采前葡萄产量估测是精准葡萄栽培中的一项关键挑战，然而当前的光学方法（RGB相机、LiDAR）从根本上受到冠层遮挡和对环境光照条件敏感性的限制。本文提出了一种地基葡萄产量估测系统，采用双77 GHz多输入多输出（MIMO）调频连续波（FM-CW）雷达，搭载于以平均速度1.0 m\u002Fs（3.7 km\u002Fh）行驶的移动平台上，与实际葡萄园拖拉机作业速度相匹配。与先前在静态或慢速移动装置中进行的雷达研究不同，所提出的处理流程通过仰角平面上的数字波束成形实现葡萄行连续三维重建，并结合IMU融合卡尔曼滤波器在不规则地形上实现点云稳定。在法国加斯科尼的一个商业葡萄园中，对七个葡萄品种和三个物候生长期（BBCH 75–89）的136 km扫描葡萄行进行了实验。从三维雷达回波强度图像中提取的统计特征用于训练支持向量回归器（SVR），并通过交叉验证进行评估。主要样本外结果为：在完整多品种数据集上、未进行任何去叶处理的情况下，交叉验证平均绝对误差（CV MAE）为31.2%，以认证酒罐散装重量作为地面真值参考。该数值应结合以下背景加以理解：在同一数据集上，便携式田间称重板与酒罐认证重量之间观察到约24–25%的差异，而葡萄栽培实践中人工采前估测误差通常超过30%。对物理参数的敏感性分析表明，葡萄品种是性能异质性的主要——也是限制性——来源：CV MAE从Tannat的7%到Baco的69%不等，表明单一全局模型不足以支持多品种部署，品种特异性校准是实际应用的必要条件。这些结果最好被理解为一种架构层面的概念验证：主要贡献在于证明了MIMO数字波束成形消除了先前机械扫描雷达系统的速度瓶颈，从而能够在实际田间速度下实现无间隙三维葡萄行重建。产量估测结果首次定量表征了该系统的感知能力及其品种依赖性局限，并为未来","Computers and Electronics in Agriculture","2026-09-23T00:00:00Z",84,{"impact":18,"substance":159,"depth":160,"authority":161,"freshness":13,"relevant":22,"comment":162},23,19,14,"首次在实用田间车速下用MIMO毫米波雷达实现葡萄产量预估，136公里商业园数据与品种依赖性结论具参考价值。",[164],{"name":155,"url":152},[119,166,29,167,168],"精准农业","产量预估","毫米波雷达",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":172},{"candidates":173,"agricultural_evidence":47},[174,178,183],{"code":33,"score":19,"supported":47,"title":175,"abstract":176,"tags":177},[49],[29,49],[29],{"code":140,"score":61,"supported":47,"title":179,"abstract":180,"tags":182},[],[181],"tractor",[],{"code":134,"score":184,"supported":14,"title":185,"abstract":186,"tags":187},2,[],[],[119,166],[189,190],"MIMO 毫米波雷达 葡萄 产量预估","葡萄园 雷达 三维重建","MIMO毫米波雷达葡萄产量预估-3255","10.1016\u002Fj.compag.2026.112395",{"doi":192,"openalex_id":194,"authors":195,"venue":155,"cited_by_count":91,"oa_url":152,"card":205,"direction":209,"ingested_from":98},"W7214043753",[196,199,202],{"name":197,"orcid":198},"Etienne Dedic","https:\u002F\u002Forcid.org\u002F0000-0002-1414-019X",{"name":200,"orcid":201},"Dominique Henry","https:\u002F\u002Forcid.org\u002F0000-0002-6016-140X",{"name":203,"orcid":204},"H. Aubert","https:\u002F\u002Forcid.org\u002F0000-0002-6113-0648",{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"用双77GHz MIMO雷达在1m\u002Fs车速下重建葡萄藤行并估算产量，验证品种依赖性。","双77GHz MIMO FMCW雷达、数字波束成形、IMU卡尔曼滤波、SVR，1","全品种交叉验证MAE为31.2%，但品种间差异大（Tannat 7%至Baco 69%），需按品种校","农业遥感与作物表型","可研究品种自适应建模与迁移学习，解决多品种部署时单一模型失效问题，并融合光学与雷达数据。","2026-09-23T23:30:01.516727Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":224,"tags":226,"primary_topic":230,"secondary_topics":231,"classification":238,"search_phrases":270,"slug":273,"view_count":184,"doi":274,"paper":275,"created_at":291},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。","Engineering Research Express","2026-09-17T00:00:00Z",66,{"impact":17,"substance":222,"depth":114,"authority":20,"freshness":17,"relevant":22,"comment":223},20,"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[225],{"name":218,"url":215},[119,227,29,228,229],"农业人工智能","多光谱检测","水果品质分级",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[232],{"code":233,"slug":234,"name":235,"path":236,"parent_code":237},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":239},{"candidates":240,"agricultural_evidence":47},[241,245,251,259,266],{"code":33,"score":19,"supported":47,"title":242,"abstract":243,"tags":244},[49],[29,49],[29],{"code":233,"score":246,"supported":47,"title":247,"abstract":249,"tags":250},11,[248],"multispectral",[248],[],{"code":252,"score":253,"supported":47,"title":254,"abstract":255,"tags":258},"I2",7,[],[256,257],"机器学习","machine learning",[227],{"code":260,"score":101,"supported":47,"title":261,"abstract":262,"tags":265},"F6",[],[263,264],"农业机器人","robotic harvesting",[],{"code":134,"score":22,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[119],[271,272],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874","10.1088\u002F2631-8695\u002Faea952",{"doi":274,"openalex_id":276,"authors":277,"venue":218,"cited_by_count":91,"oa_url":215,"card":285,"direction":290,"ingested_from":98},"W7213509882",[278,280,282],{"name":279,"orcid":9},"Guodong Xiu",{"name":281,"orcid":9},"Sára Strakošová",{"name":283,"orcid":284},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":286,"method":287,"finding":288,"direction":209,"opportunity":289},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","农业人工智能与决策模型","2026-09-18T23:30:52.790038Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":9,"content":9,"source_name":297,"source_url":9,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":307,"secondary_topics":308,"classification":309,"search_phrases":336,"slug":339,"view_count":61,"doi":9,"paper":340,"created_at":348},2490,"Lightweight Detection of Blueberries at Different Maturity Stages in Complex Orchard Environments(YOLOv12n+SHSA2C2f+知识蒸馏)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1949","云南农业大学大数据学院联合云南省农业大数据工程技术研究中心 2026 年 9 月 10 日在《Agriculture》发表。针对小果、密集、遮挡、色彩过渡细微等复杂田间条件下的蓝莓成熟期检测难题,构建 4680 图像 60573 标注框田间数据集,重新设计 YOLOv12n 的 P3\u002FP4\u002FP5 检测结构为 P2\u002FP3\u002FP4 架构,引入轻量化 SHSA2C2f 单头自注意力特征增强模块,DINOv3 (ViT-S\u002F16) 引导的非对称知识蒸馏。精度导向 M4 模型 mAP@0.5 达 92.68%±0.04%、mAP@0.5:0.95 86.38%±0.10%,参数量仅 0.79 M;经 TensorRT FP16 转换后保留 92.75% mAP@0.5,可在 Jetson Orin Nano 上以 15.9 FPS 运行。","MDPI Agriculture 2026-09-10","2026-09-09T16:00:00Z",80,{"impact":18,"substance":159,"depth":18,"authority":20,"freshness":17,"relevant":22,"comment":301},"面向复杂果园的蓝莓成熟期轻量化检测研究，方法新颖、数据规模扎实且给出边缘部署实测性能，具备较高专业参考价值。",[303],{"name":297,"url":295},[119,227,305,306,30],"边缘计算","目标检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":310},{"candidates":311,"agricultural_evidence":47},[312,317,323,327,331],{"code":33,"score":21,"supported":47,"title":313,"abstract":315,"tags":316},[314],"orchard",[],[30],{"code":318,"score":61,"supported":47,"title":319,"abstract":320,"tags":322},"I6",[],[321],"农业大数据",[],{"code":252,"score":184,"supported":14,"title":324,"abstract":325,"tags":326},[],[],[227,306],{"code":134,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[119],{"code":332,"score":22,"supported":14,"title":333,"abstract":334,"tags":335},"I5",[],[],[305],[337,338],"农业人工智能 智慧农业 目标检测 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业目标检测边缘计算-2490",{"doi":9,"openalex_id":9,"authors":341,"venue":9,"cited_by_count":91,"oa_url":9,"card":342,"direction":290,"ingested_from":347},[],{"tldr":343,"method":344,"finding":345,"direction":290,"opportunity":346},"提出轻量YOLOv12n改进模型，实现复杂果园中多成熟期蓝莓的实时检测。","4680张田间图像数据集，P2\u002FP3\u002FP4结构、SHSA2C2f模块与DINOv","M4模型mAP@0.5达92.68%，仅0.79M参数，Jetson Orin Nano上15.9 ","可探索多作物通用轻量检测框架，或结合成熟度计数用于产量预测与采摘决策。","agent","2026-09-15T00:04:27.061523Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":9,"content":354,"source_name":355,"source_url":9,"published_at":356,"category":111,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":357,"sources":359,"tags":361,"primary_topic":366,"secondary_topics":367,"classification":368,"search_phrases":385,"slug":388,"view_count":61,"doi":9,"paper":9,"created_at":389},2466,"山东省农科院'蓝辰'蓝莓新品种生产经营权许可公告(截止 9-12)","http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fdetail.html?id=946","山东省农科院果树研究所选育的'蓝辰'蓝莓新品种生产经营权许可公告,面向社会征寻实施合作伙伴,截止 2026 年 9 月 12 日。'蓝辰'树姿直立,株型紧凑,生长势强;果实扁圆形,果皮蓝黑色,果蒂痕小,萼洼中大,果粉量少。平均单果重 2.11g,最大单果重 3.18g;果肉淡绿色,可溶性固形物含量 15.3%,风味好,品质优良。","欢迎来到山东省农业科技转化促进会!\n\n[登录](javascript:zh_tran('n');)[|](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fdetail.html?id=946)[注册](javascript:zh_tran('t');)\n\n![Image 2](http:\u002F\u002Fsdnch.org.cn\u002Fimg\u002Flogo.png)\n\n![Image 3](http:\u002F\u002Fsdnch.org.cn\u002Fimg\u002Findex\u002Fss.png)\n\n*   [首页](http:\u002F\u002Fsdnch.org.cn\u002Findex.html)\n*   [单位概况](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fgaikuang.html)\n    *   [单位简介](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fgaikuang.html)\n    *   [本会章程](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fgaikuang.html?type=6)\n    *   [现任负责人](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fgaikuang.html?type=5)\n    *   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[青岛农业大学](https:\u002F\u002Fwww.qau.edu.cn\u002F \"青岛农业大学\")\n*   [山东农业大学](http:\u002F\u002Fwww.sdau.edu.cn\u002F \"山东农业大学\")\n*   [山东省农业科学院](http:\u002F\u002Fwww.saas.ac.cn\u002F \"山东省农业科学院\")\n\n其他单位\n\n![Image 7](http:\u002F\u002Fsdnch.org.cn\u002Fimg\u002Fsjx.png)\n\n*   [山东省技术市场协会](http:\u002F\u002Fsdnch.org.cn\u002F \"山东省技术市场协会\")\n\nCopyright 2016 山东省农业科技转化促进会 鲁公网安备 37011202001782号\n\n免责声明 济南市历城区工业北路23788号 0531-66655117 qilujinqiao@163.com\n\n![Image 8](http:\u002F\u002Fsdnch.org.cn\u002Fpages\u002Fdetail.html?id=946)扫码关注微信公众号","山东省农业科技成果转化促进会 2026-09-08","2026-09-07T16:00:00Z",{"impact":46,"substance":18,"depth":129,"authority":20,"freshness":135,"relevant":22,"comment":358},"省级科研机构蓝莓新品种生产经营权许可公告，含审定编号与品质数据，对种业转化有参考价值，但截止日期已过、时效性偏弱。",[360],{"name":355,"url":352},[362,363,30,364,365],"种业振兴","山东省农科院","科技成果转化","品种权许可",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":369},{"candidates":370,"agricultural_evidence":47},[371,375,380],{"code":33,"score":135,"supported":47,"title":372,"abstract":373,"tags":374},[],[35],[30],{"code":376,"score":22,"supported":14,"title":377,"abstract":378,"tags":379},"F1",[],[],[362],{"code":381,"score":22,"supported":14,"title":382,"abstract":383,"tags":384},"G1",[],[],[364],[386,387],"山东省农科院 科技成果转化 品种权许可 种业振兴","山东省农科院 科技成果转化","山东省农科院科技成果转化品种权许可种业振兴-2466","2026-09-15T00:04:22.852696Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":9,"content":9,"source_name":395,"source_url":9,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":397,"sources":399,"tags":401,"primary_topic":405,"secondary_topics":406,"classification":407,"search_phrases":418,"slug":421,"view_count":61,"doi":9,"paper":422,"created_at":429},2396,"[预印本]Multi-Task Learning for Sparsely-Labeled Time Series: A Case Study on Cold-Hardiness Modeling","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.09062","arXiv 2609.09062（2026-09-10）。研究针对葡萄抗寒性的时间序列过程建模问题（数据有限且标签时序稀疏），提出基于RNN的多任务学习方法。实验证明某些MTL架构优于单任务学习和现有科学模型，并在相关萌芽期预测任务上展现良好潜力。","arXiv | 2026-09-10","2026-09-10T00:00:00Z",{"impact":129,"substance":18,"depth":46,"authority":129,"freshness":17,"relevant":22,"comment":398},"预印本提出RNN多任务学习用于葡萄抗寒性稀疏标签时序建模，方法新颖但尚属早期研究，产业影响有限，可作为农业AI前沿动态收录。",[400],{"name":395,"url":393},[227,402,29,403,404],"预印本","抗寒育种","时间序列",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":408},{"candidates":409,"agricultural_evidence":47},[410,414],{"code":33,"score":135,"supported":47,"title":411,"abstract":412,"tags":413},[],[29],[29],{"code":252,"score":22,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[227],[419,420],"农业人工智能 抗寒育种 时间序列 预印本","农业人工智能 抗寒育种","农业人工智能抗寒育种时间序列预印本-2396",{"doi":9,"openalex_id":9,"authors":423,"venue":9,"cited_by_count":91,"oa_url":9,"card":424,"direction":290,"ingested_from":347},[],{"tldr":425,"method":426,"finding":427,"direction":290,"opportunity":428},"提出RNN多任务学习方法，用于标签稀疏的葡萄抗寒时间序列建模。","基于RNN的多任务学习，处理有限且时序稀疏的标签数据。","部分MTL架构优于单任务学习和现有科学模型，并利于萌芽期预测。","可探索多任务学习在更多稀疏标签农业时序场景（如病虫害、物候）中的迁移与泛化。","2026-09-14T00:06:33.014967Z"]