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冬菌夏蝉 生态套种 紫金蝉茶","广东省农科院 冬菌夏蝉","广东省农科院冬菌夏蝉生态套种紫金蝉茶-1018",4,"2026-08-28T00:07:00.251296Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":8,"source_name":191,"source_url":188,"published_at":192,"category":193,"cover_url":8,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":199,"tags":201,"primary_topic":207,"secondary_topics":208,"classification":221,"search_phrases":258,"slug":261,"view_count":262,"doi":263,"paper":264,"created_at":292},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z","论文",80,{"impact":196,"substance":197,"depth":18,"authority":124,"freshness":13,"relevant":21,"comment":198},16,22,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[200],{"name":191,"url":188},[202,203,204,205,206],"智慧农业","农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[209,215],{"code":210,"slug":211,"name":212,"path":213,"parent_code":214},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":38,"locked":14,"version":39,"method":40,"evidence":222},{"candidates":223,"agricultural_evidence":44},[224,231,237,243,253],{"code":210,"score":18,"supported":44,"title":225,"abstract":227,"tags":230},[226],"hyperspectral",[228,229,226],"遥感","remote sensing",[204],{"code":31,"score":19,"supported":44,"title":232,"abstract":234,"tags":236},[233],"alfalfa",[235,233],"苜蓿",[206],{"code":216,"score":13,"supported":44,"title":238,"abstract":239,"tags":242},[],[240,235,241],"饲草","forage",[206],{"code":244,"score":13,"supported":44,"title":245,"abstract":246,"tags":252},"I2",[],[247,248,249,250,251],"深度学习","机器学习","machine learning","deep learning","decision support",[203],{"code":254,"score":21,"supported":14,"title":255,"abstract":256,"tags":257},"I1",[],[],[202],[259,260],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573",0,"10.1016\u002Fj.eja.2026.128375",{"doi":263,"openalex_id":265,"authors":266,"venue":191,"cited_by_count":262,"oa_url":188,"card":285,"direction":289,"ingested_from":291},"W7223154576",[267,269,271,273,275,278,280,283],{"name":268,"orcid":8},"Shuai Fu",{"name":270,"orcid":8},"Yunhao Li",{"name":272,"orcid":8},"Chunli Miao",{"name":274,"orcid":8},"Jie Liu",{"name":276,"orcid":277},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":279,"orcid":8},"Yongjia Hu",{"name":281,"orcid":282},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":284,"orcid":8},"吴彩霞",{"tldr":286,"method":287,"finding":288,"direction":289,"opportunity":290},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","农业遥感与作物表型","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","openalex","2026-10-22T11:20:39.622844Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":8,"source_name":299,"source_url":296,"published_at":300,"category":193,"cover_url":8,"hotness":13,"is_selected":14,"score":301,"score_detail":302,"sources":305,"tags":307,"primary_topic":310,"secondary_topics":311,"classification":313,"search_phrases":335,"slug":338,"view_count":262,"doi":339,"paper":340,"created_at":382},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":18,"substance":197,"depth":18,"authority":17,"freshness":303,"relevant":21,"comment":304},9,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[306],{"name":299,"url":296},[202,308,309,228,206],"无人机","育种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[312],{"code":210,"slug":211,"name":212,"path":213,"parent_code":214},{"status":38,"locked":14,"version":39,"method":40,"evidence":314},{"candidates":315,"agricultural_evidence":44},[316,320,325,331],{"code":31,"score":19,"supported":44,"title":317,"abstract":318,"tags":319},[233],[235,233],[206],{"code":210,"score":17,"supported":44,"title":321,"abstract":323,"tags":324},[322],"multispectral",[322],[228],{"code":254,"score":51,"supported":44,"title":326,"abstract":327,"tags":330},[],[328,329],"精准农业","precision agriculture",[202],{"code":216,"score":183,"supported":44,"title":332,"abstract":333,"tags":334},[],[235],[206],[336,337],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":339,"openalex_id":341,"authors":342,"venue":299,"cited_by_count":262,"oa_url":296,"card":377,"direction":289,"ingested_from":291},"W7223083966",[343,346,349,352,355,357,359,362,365,368,371,374],{"name":344,"orcid":345},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":347,"orcid":348},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":350,"orcid":351},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":353,"orcid":354},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":356,"orcid":8},"Julie Hansen",{"name":358,"orcid":8},"Christopher Pierce",{"name":360,"orcid":361},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":363,"orcid":364},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":366,"orcid":367},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":369,"orcid":370},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":372,"orcid":373},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":375,"orcid":376},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":378,"method":379,"finding":380,"direction":289,"opportunity":381},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":8,"content":388,"source_name":389,"source_url":8,"published_at":300,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":395,"tags":397,"primary_topic":403,"secondary_topics":404,"classification":410,"search_phrases":422,"slug":425,"view_count":262,"doi":8,"paper":8,"created_at":426},5426,"浙江农科院在景宁发布丘陵梯田稻薯轮作全程机械化与共富模式","http:\u002F\u002Fwww.came.net.cn\u002FsecondLevelPage\u002Finfo\u002F31\u002F227412","浙江省农业科学院联合浙江省自然资源集团、景宁畲族自治县政府在景宁举办丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作现场会。项目由省农科院农业装备研究所牵头，核心示范 200 亩、辐射 1000 亩。研发的小土豆移栽机作业效率较人工提升 8 倍以上，「无人机吊运+轨道运输+地面农机」体系下单次吊运 50 公斤以上，山地运输效率提升 10 倍以上、单位运输成本降低 60% 以上；病毒学与生物技术研究所攻关的茎尖脱毒组培苗移栽成活率不低于 90%，脱毒种薯出苗率不低于 98%。","近日，由浙江省农业科学院、浙江省自然资源集团有限公司、景宁畲族自治县人民政府联合主办的丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作生产技术集成与共富模式示范现场会在景宁畲族自治县顺利举办。活动系统呈现了省农科院联合省自然资源集团、浙江默梵绿芯种业有限公司等多方主体在景宁探索构建的丘陵山区稻薯轮作全程机械化与产业链共富新模式。\n\n省农科院党委委员、副院长冯冠胜致辞并宣布现场会开幕，他指出，当前浙江省深入推进共同富裕示范区建设，山区是补齐发展短板、激发内生潜力的关键区域。省农科院围绕多田套合、稻薯轮作全程机械化与产业链共富开展协同攻关、集成示范、落地推广，取得了阶段性成果。下一步，将紧贴山区农业产业发展实际需求，充分发挥多良集成优势，持续深化技术攻关、成果熟化、示范推广和落地服务，全力助力景宁农业产业提质增效，系统总结可复制、可推广的丘陵山地产业共富模式，为全国丘陵山区农业高质量发展贡献农科力量。\n\n该项目由省农科院农业装备研究所牵头，联合作物与核技术利用研究所、病毒学与生物技术研究所等共同实施。针对丘陵山区稻薯轮作茬口衔接难、专用适配农机短缺、农产品销路不稳等难题，项目集成土地、科技、产业要素，探索山区产业共富路径：依托省自然资源集团国企资金资源优势，实施“多田套合”土地整治工程，将零散“巴掌田”改造为连片宜机梯田；依托省农科院农机农艺融合科研优势，筛选“薯稻薯”菜用土豆与水稻品种，攻克茎尖脱毒组培与移栽调控茬口技术，研发小土豆移栽装备，构建“无人机吊运+轨道运输+地面农机”协同作业体系；依托浙江默梵绿芯种业有限公司市场品牌优势，推行订单保底收购、品质溢价激励机制，保障农户稳定增收。该模式核心示范200亩、辐射1000亩。\n\n现场展示的丘陵山区稻薯轮作全程机械化生产技术模式，集成精细化整地、机械化种植、轻简化管理、低空吊运+轨道运输、机械化收获等环节。研发适配山地的小土豆移栽机，作业效率较人工提升8倍以上；“无人机吊运+轨道运输+地面农机”协同作业模式中，无人机单次可吊运50公斤以上农机、农资、秧盘，山地运输效率较人工提升10倍以上，单位运输成本降低60%以上。现场还演示了智能灌溉、机械化耕整地、精准植保及收获等成套装备，并展示巡检机器狗、作物无人机监测等智能装备与技术。\n\n在农艺技术方面，省农科院作物与核技术利用研究所开展适宜“薯稻薯”模式的水稻品种筛选，评价耐寒性、早熟性、抗病性及机械化适配性；病毒学与生物技术研究所开展菜用土豆品种筛选与茎尖脱毒组培快繁技术攻关，组培苗移栽成活率不低于90%，可调控茬口5至10天；脱毒种薯出苗率不低于98%，较自留种增产20%至40%。通过品种搭配、播期衔接与密度调控，构建“薯—稻—薯”一年多熟轮作体系，提升复种指数与种薯质量。\n\n产业链运营方面，项目打造“企业+合作社+农户”铁三角模式，统一品种标准、种植技术、品质管控、产品收购全流程标准化管理，推行订单保底收购、品质溢价激励机制，并依托共富工坊、农旅融合等新业态拓宽村集体与农户增收渠道。活动还举行丘陵山地机械化应用合作签约、稻薯轮作种植推广联合体签约及农机农艺融合综合示范基地揭牌仪式。\n\n本次现场会展示的模式实现三大转变，即生产方式从“人力依赖”转向“机械智控”、种植模式从“零散粗放”转向“标准高效”、产业发展从“单一种植”转向“全链共富”，通过稳技术、稳标准、稳销路、稳收益，真正实现山地农业产业提质、农户增收、集体增效的良性共富新格局。\n\n发布时间：2026-10-20提交人：浙江省畜牧技术推广与种畜禽监测总站（浙江省农业机械试验鉴定推广总站） 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