[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3199":3,"related-3199":77},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":76},3199,"Recommendation of Suitable Planting Locations for Hybrid Maize Varieties Based on Graph Collaborative Filtering","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finsilicoplants\u002Fdiag026","Abstract Enhancing the precision of crop variety adaptability evaluation is critical for maximizing yields and ensuring food security under climate change. Traditional methods relying on expert knowledge or process-based models face scalability challenges and difficulties in capturing complex variety–environment interactions. This study proposes a scalable graph-based collaborative filtering framework for assessing suitable planting locations for hybrid maize varieties, reformulating adaptability evaluation as a ranking task. The framework integrates two key innovations: a multi-layer perceptron (MLP) encoding module that leverages 15 variety trait features and 24 environmental variables to address the cold-start problem, enabling recommendations for previously unseen varieties; and a BPR+ loss function that distinguishes unsuitable environments from merely unplanted ones to accommodate the unique sparsity structure of agricultural trial data. Evaluated against six state-of-the-art graph-based recommendation models on a dataset of 189 locations and 1,102 hybrid maize varieties across China's major agro-ecological zones, our method consistently outperforms existing approaches, achieving approximately 5% improvement over the strongest baseline. The MLP module attains cold-start NDCG above 0.8, and BPR+ yields significant gains in Recall@1–4 and NDCG@1–5. A case study of the widely cultivated variety ZD958, validated against both national promotion data (2019–2024) and multi-year field-trial yields from 40 locations (2017–2024), confirms that the model's rank-ordered recommendations align closely with independent yield evidence: top-ranked locations achieve both the highest productivity and the greatest yield stability. This framework provides an AI-based decision support tool for variety placement optimization and climate-resilient agriculture.","摘要 提高作物品种适应性评价的精度，对于在气候变化背景下最大化产量和保障粮食安全至关重要。依赖专家知识或过程模型的传统方法面临可扩展性挑战，且难以捕捉复杂的品种–环境互作。本研究提出了一种可扩展的基于图协同过滤框架，用于评估杂交玉米品种的适宜种植地点，将适应性评价重新表述为排序任务。该框架融合了两项关键创新：一是多层感知机（MLP）编码模块，利用15个品种性状特征和24个环境变量来缓解冷启动问题，从而能够对未见过的品种进行推荐；二是BPR+损失函数，将不适宜环境与仅未种植环境区分开来，以适应农业试验数据特有的稀疏结构。在中国主要农业生态区189个地点和1，102个杂交玉米品种的数据集上，与六种最先进的基于图的推荐模型进行对比评估，我们的方法始终优于现有方法，相较最强基线提升约5%。MLP模块的冷启动NDCG达到0.8以上，BPR+在Recall@1–4和NDCG@1–5上均带来显著提升。对广泛种植品种ZD958的案例研究，结合国家推广数据（2019–2024年）和40个地点多年田间试验产量数据（2017–2024年）进行验证，证实模型的排序推荐与独立产量证据高度一致：排名靠前的地点既具有最高生产力，也具有最大产量稳定性。该框架为品种布局优化和气候韧性农业提供了基于人工智能的决策支持工具。",null,"in silico Plants","2026-09-20T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"提出基于图协同过滤的杂交玉米品种适宜种植区推荐框架，覆盖全国189个地点、1102个品种，冷启动与排序指标均有提升，对品种布局优化有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","种业振兴","玉米","品种布局",[32,33],"杂交玉米 品种 适宜种植区","图协同过滤 玉米 推荐","杂交玉米品种适宜种植区-3199",0,"10.1093\u002Finsilicoplants\u002Fdiag026",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":69,"direction":73,"ingested_from":75},"W7213886755",[40,43,46,49,51,54,57,60,63,66],{"name":41,"orcid":42},"Yanyun Han","https:\u002F\u002Forcid.org\u002F0000-0003-4333-8565",{"name":44,"orcid":45},"Zhongqiang Liu","https:\u002F\u002Forcid.org\u002F0009-0008-4290-8718",{"name":47,"orcid":48},"Aiwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0031-5857",{"name":50,"orcid":9},"Yong Zhang",{"name":52,"orcid":53},"Shouhui Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4917-9921",{"name":55,"orcid":56},"Xiangyu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7668-1509",{"name":58,"orcid":59},"Qiusi Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5469-1802",{"name":61,"orcid":62},"Qi Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-5061-5422",{"name":64,"orcid":65},"Xinglin Piao","https:\u002F\u002Forcid.org\u002F0000-0003-3774-5789",{"name":67,"orcid":68},"Kaiyi Wang","https:\u002F\u002Forcid.org\u002F0009-0000-5365-7178",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"提出图协同过滤框架，将玉米品种适种区评估转化为排序任务，推荐适宜种植地点。","图协同过滤+MLP编码15个品种性状与24个环境变量，BPR+损失，189地点1","方法优于六个基线约5%，冷启动NDCG超0.8，推荐排名与独立产量证据高度一致。","农业人工智能与决策模型","可拓展至多作物多品种跨区域迁移推荐，并融合气候情景预测未来适种区变化。","openalex","2026-09-22T23:30:49.262877Z",{"total":78,"page":21,"page_size":78,"items":79},6,[80,112,143,190,212,268],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":9,"content":9,"source_name":85,"source_url":9,"published_at":86,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":94,"tags":96,"search_phrases":98,"slug":101,"view_count":35,"doi":102,"paper":103,"created_at":111},2492,"BiFormer-Enhanced YOLOv11n for Accurate Maize Ear Detection in Seed Production Fields(玉米穗识别 BiF-YOLO)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181780","青岛农业大学机电工程学院 2026 年 9 月 10 日在《Agronomy》(Section Precision and Digital Agriculture)发表。提出 BiF-YOLO 模型基于 YOLOv11n 和 BiFormer 架构,三项目标性增强策略:①在特征提取网络嵌入 C3k2_AdditiveBlock 模块以增强田间被遮挡和小型玉米穗的识别能力;②构建 C2BRA 双层路由注意力机制(Cross-stage partial 2 Bottleneck with Residual Attention)以抑制背景干扰;③采用 ShapeIoU 损失函数。从胶州和张掖种子生产田采集玉米穗图像。BiF-YOLO 模型有效适应复杂田间作业场景,为制种玉米收获机的实时识别与产量估计提供技术支撑。","MDPI Agronomy 2026-09-10","2026-09-09T16:00:00Z",74,{"impact":89,"substance":90,"depth":91,"authority":19,"freshness":92,"relevant":21,"comment":93},16,21,17,7,"面向制种玉米收获的轻量化检测模型改进，方法有针对性增强且数据来自真实制种田，对智能农机与产量估计有实用价值，但属细分技术进展，影响面有限。",[95],{"name":85,"url":83},[26,27,28,29,97],"目标检测",[99,100],"农业人工智能 智慧农业 目标检测 种业振兴","农业人工智能 智慧农业","农业人工智能智慧农业目标检测种业振兴-2492","10.3390\u002Fagronomy16181780",{"doi":102,"openalex_id":9,"authors":104,"venue":9,"cited_by_count":35,"oa_url":9,"card":105,"direction":73,"ingested_from":110},[],{"tldr":106,"method":107,"finding":108,"direction":73,"opportunity":109},"提出BiF-YOLO模型，在制种田实现玉米穗精准检测。","基于YOLOv11n，融合BiFormer、C3k2_AdditiveBlock","BiF-YOLO有效适应复杂田间场景，提升遮挡和小目标玉米穗检测精度。","可探索轻量化模型在收获机边缘设备上的实时部署与产量估计集成。","agent","2026-09-15T00:04:27.207493Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":9,"content":9,"source_name":117,"source_url":115,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":123,"tags":125,"search_phrases":128,"slug":131,"view_count":35,"doi":132,"paper":133,"created_at":142},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture","2026-09-22T00:00:00Z",69,{"impact":121,"substance":17,"depth":89,"authority":19,"freshness":13,"relevant":21,"comment":122},12,"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[124],{"name":117,"url":115},[26,27,126,127,29],"产量预测","物联网",[129,130],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":132,"openalex_id":134,"authors":135,"venue":117,"cited_by_count":35,"oa_url":115,"card":9,"direction":141,"ingested_from":75},"W7213978365",[136,139],{"name":137,"orcid":138},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":140,"orcid":9},"Yasinta Nzogera","智慧农业 \u002F 农业物联网","2026-09-22T23:30:10.178097Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":9,"source_name":149,"source_url":146,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":150,"score_detail":151,"sources":155,"tags":157,"search_phrases":160,"slug":163,"view_count":35,"doi":164,"paper":165,"created_at":189},3128,"Spatiotemporal synchronization-based seed position estimation for multi-row precision planting","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112396","Spatiotemporal synchronization-based seed position estimation for multi-row precision planting。Computers and Electronics in Agriculture","基于时空同步的多行精量播种种子位置估计。《农业计算机与电子》","Computers and Electronics in Agriculture",80,{"impact":17,"substance":152,"depth":17,"authority":153,"freshness":13,"relevant":21,"comment":154},20,14,"核心期刊论文，提出基于时空同步的多行精量播种种子位置估计方法，对智能播种机具研发有参考价值，但属细分技术进展，公共影响有限。",[156],{"name":149,"url":146},[26,27,158,29,159],"智能农机","精准播种",[161,162],"多行精准播种 种子位置估计","时空同步 播种监测","多行精准播种种子位置估计-3128","10.1016\u002Fj.compag.2026.112396",{"doi":164,"openalex_id":166,"authors":167,"venue":149,"cited_by_count":35,"oa_url":9,"card":184,"direction":141,"ingested_from":75},"W7213976849",[168,171,173,176,178,180,182],{"name":169,"orcid":170},"Lin Jia","https:\u002F\u002Forcid.org\u002F0009-0002-2422-7974",{"name":172,"orcid":9},"Qingjie Wang",{"name":174,"orcid":175},"Hongwen Li","https:\u002F\u002Forcid.org\u002F0000-0002-5536-2346",{"name":177,"orcid":9},"Chao Wang",{"name":179,"orcid":9},"Jin He",{"name":181,"orcid":9},"Caiyun Lu",{"name":183,"orcid":9},"Xinyue Zhang",{"tldr":185,"method":186,"finding":187,"direction":141,"opportunity":188},"提出基于时空同步的多行精量播种种子位置估计方法。","利用播种机多行作业的时空同步信号估计种子落点位置。","该方法能实现多行精量播种的种子位置估计，提升播种质量监测精度。","可结合机器视觉与GNSS实时校正，发展播种质量在线评估与变量播种闭环控制。","2026-09-22T23:30:01.680027Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":9,"content":195,"source_name":196,"source_url":9,"published_at":197,"category":198,"cover_url":9,"hotness":13,"is_selected":14,"score":87,"score_detail":199,"sources":202,"tags":204,"search_phrases":207,"slug":210,"view_count":35,"doi":9,"paper":9,"created_at":211},3111,"江苏省农科院院长朱艳出席 2026 世界农业科技创新大会作\"建设气候韧性农业：中国江苏的经验与策略\"主旨演讲","https:\u002F\u002Fhome.jaas.ac.cn\u002Fznbmxwlsml\u002Fgjhzc\u002Fart\u002F2026\u002Fart_a07fcd60380d494c9ac02c215708d7ea.html","9-15 至 19 2026 年世界农业科技创新大会（WAFI）在北京举行，江苏农科院院长朱艳应邀出席世界农业科学家论坛作主旨演讲。朱艳以\"建设气候韧性农业：中国江苏的经验与策略\"为题分享\"前沿育种—良法配套—智慧赋能\"路径：精准培育气候适应型品种重点创制耐高温耐盐碱节水型优质耐逆种质；精细化开展农艺管理集成示范\"麦—稻周年大面积丰产增效技术体系\"；AI 融入全环节智能管控，构建作物系统模拟模型研发粮食作物逆境灾害动态预警与定量评估技术；分享与 CGIAR、AgMIP 等海外科教机构和国际组织联合开展应对气候变化国际合作进展。","9月15日至19日，2026年世界农业科技创新大会（WAFI）在北京举行。大会以“农食系统绿色健康转型”为主题，由中国农业大学、平谷区人民政府、北京市农业农村局和国际农业研究磋商组织（CGIAR）共同主办。会议期间，院长、党委副书记朱艳应邀出席世界农业科学家论坛并作主旨演讲。\n\n[![Image 1: A3017F2F331F7F14DEFC59D29D081BB5.jpg](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002FSa52f10e390324c7b9184546c22090621-800.jpg)](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002Fa52f10e390324c7b9184546c22090621.jpg)\n\n朱艳以“建设气候韧性农业：中国江苏的经验与策略”为题重点分享了我省在应对气候变化挑战探索出的“前沿育种—良法配套—智慧赋能”路径。一是精准培育气候适应型品种，重点创制耐高温、耐盐碱、节水型优质耐逆种质，育成了一批高产抗逆的水稻、小麦及大豆等新品种，并加快建设江苏省农业种质资源综合基因库，筑牢应对气候变化的“种质”防线。二是精细化开展农艺管理，集成示范“麦—稻周年大面积丰产增效技术体系”，推广水稻集中育秧、小麦适期晚播等避灾技术，同时落实轮作休耕等绿色制度，打好可持续的气候适应“组合拳”。三是AI融入全环节智能管控，构建作物系统模拟模型，研发粮食作物逆境灾害动态预警与定量评估技术，覆盖稻麦全链条生产管理，形成应对气候变化的智慧体系。她还分享了我院与国际农业研究磋商组织（CGIAR）、国际农业模型比较与改进项目（AgMIP）等海外科教机构和国际组织联合开展应对气候变化的国际合作进展。\n\n朱艳表示，江苏省农业科学院愿与全球农业科学家一道，推动建立应对气候变化的全球知识共享平台，贡献更多可复制、可推广的“江苏方案”。\n\n[![Image 2: 朱院-世界农业科技创新大会2.jpg](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002FS4a20548ff6c34efe97426fb6933e1a41-800.jpg)](https:\u002F\u002Fhome.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F191335345\u002Fpicture\u002F20268\u002F4a20548ff6c34efe97426fb6933e1a41.jpg)\n\n世界农业科技创新大会（WAFI）由北京市委市政府发起，以“创新农业 共享未来”为宗旨，聚焦促进全球农食系统转型的前沿创新，深入推动产学研互动和国际交流合作。自2023年启动以来，大会已成功举办4届，联合国粮农组织、世界粮食计划署等国际机构深度参与，赢得国内外同仁的高度认可，已成为全球农业科技领域具有实质影响力的高端对话平台之一。","江苏省农业科学院","2026-09-18T06:56:00Z","报道",{"impact":18,"substance":17,"depth":200,"authority":19,"freshness":78,"relevant":21,"comment":201},15,"省级农科院院长在国际大会分享气候韧性农业的江苏路径，内容具体、有国际合作信息，但属会议报道，时效略过，可作主题聚合素材而非头条精选。",[203],{"name":196,"url":193},[26,27,28,205,206],"国际合作","气候韧性农业",[208,209],"江苏省农科院 气候韧性农业","朱艳 WAFI 主旨演讲","江苏省农科院气候韧性农业-3111","2026-09-22T00:05:36.089846Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":149,"source_url":215,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":223,"tags":225,"search_phrases":228,"slug":231,"view_count":35,"doi":232,"paper":233,"created_at":267},3006,"Explainable spectral–image fusion multi-task learning for maize canopy biochemical and structural trait retrieval","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112450","Accurate and interpretable estimation of multiple maize canopy traits is important for proximal crop monitoring and high-throughput phenotyping. This study developed a spectral–image fusion multi-task learning framework (MM-MTL) for the simultaneous retrieval of chlorophyll index (Chl index), leaf area index (LAI), nitrogen balance index (NBI), and anthocyanin index (Anth index). Proximal hyperspectral observations were collected using a Specim IQ camera covering 400–1000 nm with 204 spectral bands. For each observation, an ROI-mean spectral vector was extracted to retain fine-grained canopy reflectance information, while a co-registered pseudo-RGB image derived from visible bands of the same hyperspectral cube preserved two-dimensional canopy structure and visible appearance. MM-MTL integrates spectral and image feature extraction, task-specific fusion, and uncertainty-weighted multi-task learning to jointly estimate the four traits. A total of 1,023 valid samples collected from nine field campaigns across the 2024 and 2025 growing seasons were used for model development and evaluation. Under plot-grouped five-fold cross-validation, MM-MTL achieved R 2 values of 0.872, 0.885, 0.722, and 0.807 for Chl index, LAI, NBI, and Anth index, respectively, and consistently outperformed the single-task, single-representation, and conventional regression baselines. Performance decreased under more challenging generalization settings, with R 2 values ranging from 0.543 to 0.680 under leave-one-campaign-out validation and from 0.425 to 0.640 under bidirectional cross-year validation. Ablation and post-hoc analyses further showed that preserving image spatial organization improved prediction, while task-wise routing, input-representation masking, wavelength perturbation, and cross-fold stability analyses revealed trait-dependent use of spectral and spatial information. These results demonstrate that complementary spectral and spatial representations derived from the same hyperspectral observation can improve multi-trait maize canopy retrieval, while transfer across acquisition campaigns, years, and field environments remains an important direction for further improvement.","准确且可解释地估算多个玉米冠层性状，对于近地作物监测和高通量表型分析具有重要意义。本研究开发了一种光谱—图像融合多任务学习框架（MM-MTL），用于同时反演叶绿素指数（Chl index）、叶面积指数（LAI）、氮平衡指数（NBI）和花青素指数（Anth index）。近地高光谱观测使用Specim IQ相机采集，覆盖400–1000 nm，共204个光谱波段。对于每次观测，提取ROI均值光谱向量以保留细粒度冠层反射率信息，同时从同一高光谱立方体的可见光波段生成配准的伪RGB图像，以保留二维冠层结构和可见外观。MM-MTL集成了光谱与图像特征提取、任务特定融合以及不确定性加权多任务学习，以联合估算这四种性状。研究使用2024年和2025年生长季9次田间试验采集的共计1,023个有效样本进行模型开发与评估。在按小区分组的五折交叉验证下，MM-MTL对Chl index、LAI、NBI和Anth index的R²分别为0.872、0.885、0.722和0.807，且持续优于单任务、单表征和传统回归基线。在更具挑战性的泛化设置下，模型性能有所下降，留一试验验证的R²范围为0.543–0.680，双向跨年验证的R²范围为0.425–0.640。消融分析和事后分析进一步表明，保留图像空间组织可提升预测性能，而任务路由、输入表征掩蔽、波长扰动和跨折稳定性分析揭示了光谱与空间信息的性状依赖性利用方式。这些结果表明，从同一高光谱观测中提取的互补光谱与空间表征可改善多性状玉米冠层反演，而跨采集试验、年份和田间环境的迁移仍是未来改进的重要方向。","2026-09-19T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":153,"freshness":221,"relevant":21,"comment":222},9,"提出光谱-图像融合多任务学习框架，1023份样本跨两年验证，方法新颖且结论可靠，对作物高通量表型研究有实质参考价值。",[224],{"name":149,"url":215},[26,27,29,226,227],"遥感","高通量表型",[229,230],"玉米冠层 多任务学习 高光谱","Specim IQ 玉米 表型","玉米冠层多任务学习高光谱-3006","10.1016\u002Fj.compag.2026.112450",{"doi":232,"openalex_id":234,"authors":235,"venue":149,"cited_by_count":35,"oa_url":215,"card":261,"direction":265,"ingested_from":75},"W7213658705",[236,238,240,242,244,247,249,252,254,256,258],{"name":237,"orcid":9},"Penglei Zhang",{"name":239,"orcid":9},"Tianbo Hao",{"name":241,"orcid":9},"Zhuoyuan Zhao",{"name":243,"orcid":9},"Hong Sun",{"name":245,"orcid":246},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":248,"orcid":9},"Zheng Cui",{"name":250,"orcid":251},"Ta Na","https:\u002F\u002Forcid.org\u002F0000-0002-1348-5655",{"name":253,"orcid":9},"Lang Qiao",{"name":255,"orcid":9},"Durval Dourado Neto",{"name":257,"orcid":9},"Feng Yang",{"name":259,"orcid":260},"Jingzhu Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":262,"method":263,"finding":264,"direction":265,"opportunity":266},"提出光谱-图像融合多任务学习框架，同时反演玉米冠层四个生化与结构性状。","Specim IQ高光谱与伪RGB融合，不确定性加权多任务学习，1023样本交叉","融合模型精度优于单任务基线，但跨年份与跨环境泛化性能明显下降。","农业遥感与作物表型","可研究跨年份\u002F跨环境域适应与迁移学习，提升多性状反演泛化能力。","2026-09-20T23:30:01.739142Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":9,"content":9,"source_name":273,"source_url":9,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":279,"tags":281,"search_phrases":285,"slug":288,"view_count":35,"doi":9,"paper":289,"created_at":296},2900,"Crop-GPA 2.0:安徽农业大学团队发布跨物种基因-表型预测深度学习工具,登The Crop Journal","https:\u002F\u002Fwww.global-agriculture.com\u002Fag-tech-research-news\u002Fchinese-researchers-launch-ai-tool-that-predicts-crop-gene-trait-links-across-species\u002F","安徽农业大学岳振宇教授团队(第一作者高玉佳)开发的Crop-GPA 2.0跨物种基因-表型预测深度学习工具于2026年9月10日发表在The Crop Journal。该工具通过分层基因组表征、跨物种预训练、性状感知学习三种联动技术,在水稻、玉米、小麦测试中跨性状(产量、抗病性、抗逆性、籽粒品质)均优于现有方法,且能在新物种或训练数据有限时保持较好性能,在线平台为育种者提供免费SNP排名服务,极大降低标记辅助选择门槛。","Global Agriculture \u002F The Crop Journal","2026-09-14T00:00:00Z",83,{"impact":18,"substance":277,"depth":17,"authority":153,"freshness":78,"relevant":21,"comment":278},23,"跨物种基因-表型预测工具登核心期刊，方法新颖、结论可靠，对智能育种有实质推动，值得进入每日精选。",[280],{"name":273,"url":271},[26,27,282,283,29,284],"水稻","智能育种","基因-表型预测",[286,287],"安徽农业大学 Crop-GPA 2.0","Crop-GPA 2.0 基因-表型预测","安徽农业大学Crop-GPA2.0-2900",{"doi":9,"openalex_id":9,"authors":290,"venue":9,"cited_by_count":35,"oa_url":9,"card":291,"direction":73,"ingested_from":110},[],{"tldr":292,"method":293,"finding":294,"direction":73,"opportunity":295},"安徽农业大学团队发布跨物种基因-表型预测深度学习工具Crop-GPA 2.0，并在水稻、玉米、小麦中","分层基因组表征、跨物种预训练、性状感知学习，基于多物种SNP与表型数据。","跨性状预测均优于现有方法，新物种或小样本下仍表现良好，并提供免费在线SNP排名服务。","可探索将跨物种预训练模型与田间表型组、环境数据融合，提升复杂性状预测与育种决策的可解释性。","2026-09-19T00:06:08.595080Z"]