[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3227":3,"related-3227":37},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":8,"created_at":36},3227,"国家重点研发计划项目\"粮食生产大数据平台研发与应用\"玉米智慧生产现场观摩会在北大荒集团红星农场召开","https:\u002F\u002Fhlj.chinadaily.com.cn\u002Fa\u002F202609\u002F19\u002FWS6aae78eae4b09a165c78b1e3.html","9-19 中国日报黑龙江报道：国家重点研发计划项目\"粮食生产大数据平台研发与应用\"玉米智慧生产现场观摩会在北大荒集团红星农场举行。会议由中国农业科学院农业资源与农业区划研究所、北京市农林科学院信息技术研究中心共同主办，北大荒集团北安分公司、红星农场协办。3000多亩玉米示范田连片成海，项目团队聚焦玉米生产全程大数据智能决策技术示范应用，集中展示了智能分析算法在农机作业调度、无人机出苗率调查、变量精准施肥、苗情长势、病虫害监测和预警及估产、种植规划和适宜收获期等场景的应用。据项目首席科学家杨贵军介绍，无人机出苗率调查精度达到98%以上，精准变量施肥效益达到114元\u002F亩。",null,"![Image 4](https:\u002F\u002Fcn.chinadaily.com.cn\u002Fimage\u002F2025\u002Fsharelogo2.jpg)\n# 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国家重点研发计划项目“粮食生产大数据平台研发与应用”玉米智慧生产现场观摩会在北大荒集团红星农场召开\n\n 来源：中国日报网 2026-09-19 19:58\n\n*   [![Image 13: weixin](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fstatic\u002F2024cn_articleandcolumn\u002Fimg\u002FArtical_Share_Icon1.png)](https:\u002F\u002Fhlj.chinadaily.com.cn\u002Fa\u002F202609\u002F19\u002FWS6aae78eae4b09a165c78b1e3.html#)\n*   [![Image 14: 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16](https:\u002F\u002Fwww.chinadaily.com.cn\u002Fimage_e\u002F2020\u002Ftimg.jpg)\n\n秋色初染，沃野铺金。9月19日，国家重点研发计划项目“粮食生产大数据平台研发与应用”玉米智慧生产现场观摩会在北大荒集团红星农场举行。\n\n会议由中国农业科学院农业资源与农业区划研究所、北京市农林科学院信息技术研究中心共同主办，北大荒集团北安分公司、红星农场协办。来自农业农村部种植业管理司、农业农村部大数据发展中心、黑龙江省农垦科学院、中国科学院东北地理与农业生态研究所、东北农业大学、黑龙江省农业科学院、北大荒集团北安分公司、红星农场等单位的领导和专家到会指导，连同项目参加单位代表共70余人参会。\n\n![Image 17](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e62f.jpeg)\n\n此时，在红星农场示范区，3000多亩玉米示范田连片成海，挺拔的玉米秆上，饱满的玉米棒透着金黄，秋风拂过，沙沙作响，一派喜人的丰收景象。项目团队聚焦玉米生产全程大数据智能决策技术示范应用，围绕玉米产前、产中、产后各关键环节，集中展示了智能分析算法在农机作业调度、无人机出苗率调查、变量精准施肥、苗情长势、病虫害监测和预警及估产、种植规划和适宜收获期等场景的应用。\n\n![Image 18](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e631.jpeg)\n\n田间观摩现场科技感十足，技术人员并通过电脑终端远程操控无人机开展巡田作业，腾空而起的无人机对田间苗情、长势进行全方位、立体化勘查，无人机自动规划好田间作业方案。依托粮食生产大数据平台，田间玉米长势实现实时动态监测、精准智能分析，精准变量施肥设备按需下料、科学供肥，让玉米精准吸收养分、吃上“专属营养餐”。\n\n据项目首席科学家杨贵军介绍，无人机出苗率调查精度达到98%以上，精准变量施肥效益达到114元\u002F亩。通过大数据平台与田间生产管理深度融合，玉米生产全过程实现可视化、精准化、智能化管控，智能决策技术在玉米生产管理中的应用水平进一步提升。\n\n![Image 19](https:\u002F\u002Fimg3.chinadaily.com.cn\u002Fimages\u002F202609\u002F19\u002F6aae78eae4b09a167285e633.jpeg)\n\n此次现场观摩会的成功举办，标志着“粮食生产大数据平台研发与应用”项目在玉米生产智能化领域取得重要阶段性进展。项目成果将为黑龙江地区数字化转型提供有力技术支撑，助力国家粮食安全保障能力持续提升。\n\n从种到收，大数据平台像给玉米生产装上了“智慧大脑”，让管理更精细、种植更省心。黑土地上，丰收的粮食与奔涌的数据流交相辉映，一幅科技赋能粮食安全的现代农业图景正徐徐展开。（中国日报黑龙江记者站 编辑：周慧颖 通讯员：岳文 张洪溪）\n\n 【责任编辑：舒靓】 \n\nCOMPO\n\nWS6aae78eae4b09a165c78b1e3\n\nhttps:\u002F\u002Fhlj.chinadaily.com.cn\u002Fa\u002F202609\u002F19\u002FWS6aae78eae4b09a165c78b1e3.html\n\n[![Image 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\n\n×","中国日报网","2026-09-19T00:00:00Z","报道",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},22,16,13,7,1,"国家重点研发计划项目在北大荒红星农场的玉米智慧生产现场观摩，属全国性科研项目落地示范，有实质内容但偏会议通稿，时效稍滞后。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","玉米","国家重点研发计划","北大荒","粮食生产大数据",[32,33],"北大荒 红星农场 玉米智慧生产","粮食生产大数据平台 观摩会","北大荒红星农场玉米智慧生产-3227",0,"2026-09-23T00:04:30.284365Z",{"total":38,"page":21,"page_size":38,"items":39},6,[40,105,148,178,224,277],{"id":41,"title":42,"url":43,"summary":44,"summary_zh":45,"content":8,"source_name":46,"source_url":43,"published_at":47,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":49,"score_detail":50,"sources":54,"tags":56,"search_phrases":60,"slug":63,"view_count":35,"doi":64,"paper":65,"created_at":104},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。","in silico Plants","2026-09-20T00:00:00Z","论文",79,{"impact":51,"substance":17,"depth":51,"authority":19,"freshness":52,"relevant":21,"comment":53},18,8,"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。",[55],{"name":46,"url":43},[26,57,58,27,59],"农业人工智能","种业振兴","品种布局",[61,62],"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","10.1093\u002Finsilicoplants\u002Fdiag026",{"doi":64,"openalex_id":66,"authors":67,"venue":46,"cited_by_count":35,"oa_url":43,"card":97,"direction":101,"ingested_from":103},"W7213886755",[68,71,74,77,79,82,85,88,91,94],{"name":69,"orcid":70},"Yanyun Han","https:\u002F\u002Forcid.org\u002F0000-0003-4333-8565",{"name":72,"orcid":73},"Zhongqiang Liu","https:\u002F\u002Forcid.org\u002F0009-0008-4290-8718",{"name":75,"orcid":76},"Aiwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0031-5857",{"name":78,"orcid":8},"Yong Zhang",{"name":80,"orcid":81},"Shouhui Pan","https:\u002F\u002Forcid.org\u002F0000-0003-4917-9921",{"name":83,"orcid":84},"Xiangyu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7668-1509",{"name":86,"orcid":87},"Qiusi Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5469-1802",{"name":89,"orcid":90},"Qi Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-5061-5422",{"name":92,"orcid":93},"Xinglin Piao","https:\u002F\u002Forcid.org\u002F0000-0003-3774-5789",{"name":95,"orcid":96},"Kaiyi Wang","https:\u002F\u002Forcid.org\u002F0009-0000-5365-7178",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"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",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":8,"source_name":111,"source_url":108,"published_at":112,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":117,"tags":119,"search_phrases":123,"slug":126,"view_count":35,"doi":127,"paper":128,"created_at":147},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research","2026-09-21T00:00:00Z",65,{"impact":52,"substance":115,"depth":18,"authority":19,"freshness":52,"relevant":21,"comment":116},20,"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[118],{"name":111,"url":108},[26,120,27,121,122],"产量预测","遥感","NDVI",[124,125],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":127,"openalex_id":129,"authors":130,"venue":111,"cited_by_count":35,"oa_url":108,"card":141,"direction":145,"ingested_from":103},"W7213918280",[131,133,135,137,139],{"name":132,"orcid":8},"N Mohammad",{"name":134,"orcid":8},"MA Islam",{"name":136,"orcid":8},"MG Mahboob",{"name":138,"orcid":8},"MM Rahman",{"name":140,"orcid":8},"I Ahmed",{"tldr":142,"method":143,"finding":144,"direction":145,"opportunity":146},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","农业遥感与作物表型","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":8,"content":8,"source_name":153,"source_url":151,"published_at":154,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":159,"tags":161,"search_phrases":163,"slug":166,"view_count":35,"doi":167,"paper":168,"created_at":177},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":157,"substance":51,"depth":18,"authority":19,"freshness":13,"relevant":21,"comment":158},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。",[160],{"name":153,"url":151},[26,57,120,162,27],"物联网",[164,165],"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":167,"openalex_id":169,"authors":170,"venue":153,"cited_by_count":35,"oa_url":151,"card":8,"direction":176,"ingested_from":103},"W7213978365",[171,174],{"name":172,"orcid":173},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":175,"orcid":8},"Yasinta Nzogera","智慧农业 \u002F 农业物联网","2026-09-22T23:30:10.178097Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":8,"source_name":184,"source_url":181,"published_at":154,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"search_phrases":194,"slug":197,"view_count":35,"doi":198,"paper":199,"created_at":223},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":51,"substance":115,"depth":51,"authority":187,"freshness":13,"relevant":21,"comment":188},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。",[190],{"name":184,"url":181},[26,57,192,27,193],"智能农机","精准播种",[195,196],"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":198,"openalex_id":200,"authors":201,"venue":184,"cited_by_count":35,"oa_url":8,"card":218,"direction":176,"ingested_from":103},"W7213976849",[202,205,207,210,212,214,216],{"name":203,"orcid":204},"Lin Jia","https:\u002F\u002Forcid.org\u002F0009-0002-2422-7974",{"name":206,"orcid":8},"Qingjie Wang",{"name":208,"orcid":209},"Hongwen Li","https:\u002F\u002Forcid.org\u002F0000-0002-5536-2346",{"name":211,"orcid":8},"Chao Wang",{"name":213,"orcid":8},"Jin He",{"name":215,"orcid":8},"Caiyun Lu",{"name":217,"orcid":8},"Xinyue Zhang",{"tldr":219,"method":220,"finding":221,"direction":176,"opportunity":222},"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":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":8,"source_name":184,"source_url":227,"published_at":11,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":234,"tags":236,"search_phrases":238,"slug":241,"view_count":35,"doi":242,"paper":243,"created_at":276},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。",81,{"impact":51,"substance":17,"depth":51,"authority":187,"freshness":232,"relevant":21,"comment":233},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。",[235],{"name":184,"url":227},[26,57,27,121,237],"高通量表型",[239,240],"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":242,"openalex_id":244,"authors":245,"venue":184,"cited_by_count":35,"oa_url":227,"card":271,"direction":145,"ingested_from":103},"W7213658705",[246,248,250,252,254,257,259,262,264,266,268],{"name":247,"orcid":8},"Penglei Zhang",{"name":249,"orcid":8},"Tianbo Hao",{"name":251,"orcid":8},"Zhuoyuan Zhao",{"name":253,"orcid":8},"Hong Sun",{"name":255,"orcid":256},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":258,"orcid":8},"Zheng Cui",{"name":260,"orcid":261},"Ta Na","https:\u002F\u002Forcid.org\u002F0000-0002-1348-5655",{"name":263,"orcid":8},"Lang Qiao",{"name":265,"orcid":8},"Durval Dourado Neto",{"name":267,"orcid":8},"Feng Yang",{"name":269,"orcid":270},"Jingzhu Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":272,"method":273,"finding":274,"direction":145,"opportunity":275},"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。","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":278,"title":279,"url":280,"summary":281,"summary_zh":8,"content":8,"source_name":282,"source_url":8,"published_at":283,"category":48,"cover_url":8,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":288,"tags":290,"search_phrases":294,"slug":297,"view_count":35,"doi":8,"paper":298,"created_at":306},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":17,"substance":286,"depth":51,"authority":187,"freshness":38,"relevant":21,"comment":287},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。",[289],{"name":282,"url":280},[26,57,291,292,27,293],"水稻","智能育种","基因-表型预测",[295,296],"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":8,"openalex_id":8,"authors":299,"venue":8,"cited_by_count":35,"oa_url":8,"card":300,"direction":101,"ingested_from":305},[],{"tldr":301,"method":302,"finding":303,"direction":101,"opportunity":304},"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。","agent","2026-09-19T00:06:08.595080Z"]