[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2868":3,"related-2868":64},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":63},2868,"MCLC-NET: Multimodal Continual Learning for Leaf Counting","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.18129","Leaf counting is an important task in plant phenotyping for monitoring plant growth and estimating crop yield. Most existing methods rely on RGB images, but their performance is often affected by occlusion, lighting variations, and other real-world challenges. Additional modalities, such as depth and thermal images, can provide useful complementary information. However, multimodal leaf counting remains underexplored. Also, many existing methods assume that all training data are available simultaneously, which is impractical in real agricultural settings, where data is collected over time from multiple sources. To address these challenges, we propose MCLC-NET, a multimodal continual learning framework for leaf counting. It learns tasks sequentially using a memory-based strategy with a memory buffer to retain important samples from previous tasks. We also introduce MMLC, a real-world multimodal leaf-counting dataset designed for a domain incremental scenario (DIS) in CL. It contains RGB, depth, and thermal images collected across different crop types under varying environmental conditions, arranged in three orderings: crop-wise, time-wise, and mixed. Experimental results, averaged over three random seeds, demonstrate that MCLC-NET consistently outperforms existing methods across all three task orderings, achieving the lowest AMSE of 0.675$\\pm$0.027, 0.542$\\pm$0.069, and 0.745$\\pm$0.057, respectively.","叶片计数是植物表型分析中的一项重要任务，用于监测植物生长和估算作物产量。现有方法大多依赖RGB图像，但其性能往往受到遮挡、光照变化及其他现实挑战的影响。深度图像和热成像等其他模态可以提供有用的互补信息。然而，多模态叶片计数仍未被充分探索。此外，许多现有方法假设所有训练数据可同时获取，这在实际农业场景中并不现实，因为数据是随时间从多个来源收集的。为应对这些挑战，我们提出了MCLC-NET，一种用于叶片计数的多模态持续学习框架。该框架采用基于记忆的策略，通过记忆缓冲区保留先前任务中的重要样本，从而按顺序学习任务。我们还引入了MMLC，一个面向持续学习中域增量场景（DIS）设计的真实世界多模态叶片计数数据集。该数据集包含在不同环境条件下跨不同作物类型采集的RGB、深度和热成像图像，并按三种顺序排列：按作物、按时间和混合。在三个随机种子上的平均实验结果表明，MCLC-NET在所有三种任务顺序下均持续优于现有方法，分别取得了最低的AMSE，为0.675±0.027、0.542±0.069和0.745±0.057。",null,"arXiv (Cornell University)","2026-09-16T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"提出多模态持续学习叶片计数框架并发布真实农业数据集，方法新颖、实验扎实，但属细分领域学术进展，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","作物表型","多模态学习","叶片计数",[33,34],"MCLC-NET 叶片计数","多模态 持续学习 作物表型","MCLC-NET叶片计数-2868",0,"10.48550\u002Farxiv.2609.18129",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":56,"direction":60,"ingested_from":62},"W7213503285",[41,44,46,49,51,54],{"name":42,"orcid":43},"Ruchi Bhatt","https:\u002F\u002Forcid.org\u002F0009-0003-8222-5640",{"name":45,"orcid":9},"Pratibha Kumari",{"name":47,"orcid":48},"Shreya Bansal","https:\u002F\u002Forcid.org\u002F0000-0002-3135-291X",{"name":50,"orcid":9},"Vedant Agnihotri",{"name":52,"orcid":53},"Dwarikanath Mahapatra","https:\u002F\u002Forcid.org\u002F0000-0001-9749-7858",{"name":55,"orcid":9},"Mukesh Saini",{"tldr":57,"method":58,"finding":59,"direction":60,"opportunity":61},"提出多模态持续学习框架MCLC-NET用于叶片计数，并发布真实多模态数据集MMLC。","基于记忆缓冲的持续学习策略，融合RGB、深度和热成像三种模态数据。","在三种任务顺序下均优于现有方法，最低AMSE达0.542±0.069。","农业遥感与作物表型","多模态持续学习在农业表型中尚属空白，可探索更多模态融合与动态环境适应策略。","openalex","2026-09-18T23:30:22.580822Z",{"total":65,"page":22,"page_size":65,"items":66},6,[67,98,139,189,227,274],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":9,"content":9,"source_name":72,"source_url":9,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":79,"tags":81,"search_phrases":84,"slug":87,"view_count":36,"doi":9,"paper":88,"created_at":97},2858,"农业视觉感知综述:基于4D混沌和Mamba网络的新颖隐私保护框架——Frontiers in Plant Science 9月17日","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260917000338095.htm","基于冬小麦-夏玉米双季种植系统的长期定位田间试验,氮肥后效对土壤-作物协调及微生物功能的调控机制尚不明确。试验设置四个处理:CK(小麦季和玉米季均不施氮)、W1M0(仅在冬小麦季施氮)、W0M1(仅在夏玉米季施氮)、W1M1(小麦季和玉米季均施氮)。与不施氮对照(CK)相比,W1M0和W0M1分别使平均年产量提高45.7%和42.2%,而W1M1提高58.6%。PLS-SEM揭示,前茬小麦季土壤氮含量解释了后茬玉米土壤理化性质97.5%的方差。","Frontiers in Plant Science \u002F 生物通","2026-09-17T00:00:00Z",78,{"impact":19,"substance":76,"depth":77,"authority":20,"freshness":13,"relevant":22,"comment":78},20,17,"该文提出基于4D混沌与Mamba网络的农业视觉感知隐私保护框架，方法新颖且属农业人工智能前沿交叉方向，信源为核心期刊，时效性强，具备进入每日精选的价值。",[80],{"name":72,"url":70},[27,28,82,29,83],"农业遥感","隐私保护",[85,86],"农业人工智能 作物表型 农业遥感 智慧农业","农业人工智能 作物表型","农业人工智能作物表型农业遥感智慧农业-2858",{"doi":9,"openalex_id":9,"authors":89,"venue":9,"cited_by_count":36,"oa_url":9,"card":90,"direction":94,"ingested_from":96},[],{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"通过长期定位试验研究氮肥后效对冬小麦-夏玉米轮作系统土壤-作物协调及微生物功能的调控机制。","长期定位田间试验，设置四种施氮处理，结合PLS-SEM分析。","双季施氮年产量提高58.6%，前茬小麦季土壤氮解释后茬玉米土壤理化性质97.5%的方差。","农业绿色发展与碳","可探究氮肥后效对土壤微生物功能及碳氮循环的长期影响，优化轮作施氮策略。","agent","2026-09-18T00:03:31.115969Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":10,"source_url":104,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":106,"sources":110,"tags":112,"search_phrases":115,"slug":118,"view_count":36,"doi":119,"paper":120,"created_at":138},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926","2026-09-15T00:00:00Z",{"impact":107,"substance":76,"depth":77,"authority":20,"freshness":108,"relevant":22,"comment":109},16,8,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[111],{"name":10,"url":104},[27,28,113,29,114],"遥感","三维重建",[116,117],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能 三维重建","农业人工智能三维重建作物表型智慧农业-2781","10.48550\u002Farxiv.2609.16926",{"doi":119,"openalex_id":121,"authors":122,"venue":10,"cited_by_count":36,"oa_url":132,"card":133,"direction":60,"ingested_from":62},"W7213397688",[123,126,128,131],{"name":124,"orcid":125},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":127,"orcid":9},"Theo Morales",{"name":129,"orcid":130},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":55,"orcid":9},"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":134,"method":135,"finding":136,"direction":60,"opportunity":137},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":150,"tags":152,"search_phrases":155,"slug":157,"view_count":36,"doi":158,"paper":159,"created_at":188},2741,"A tomato maturity detection method against occlusion and variable illumination","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112423","A tomato maturity detection method against occlusion and variable illumination。Computers and Electronics in Agriculture","一种抗遮挡和可变光照的番茄成熟度检测方法","Computers and Electronics in Agriculture",68,{"impact":17,"substance":19,"depth":107,"authority":148,"freshness":108,"relevant":22,"comment":149},14,"核心期刊论文，针对遮挡与光照变化下的番茄成熟度检测提出新方法，属农业人工智能细分领域实质进展，但应用范围有限，未达重大突破层级。",[151],{"name":145,"url":142},[27,28,153,154,29],"目标检测","番茄",[156,86],"农业人工智能 作物表型 智慧农业 目标检测","农业人工智能作物表型智慧农业目标检测-2741","10.1016\u002Fj.compag.2026.112423",{"doi":158,"openalex_id":160,"authors":161,"venue":145,"cited_by_count":36,"oa_url":9,"card":182,"direction":186,"ingested_from":62},"W7213429689",[162,165,168,171,174,177,179],{"name":163,"orcid":164},"Hao Meng","https:\u002F\u002Forcid.org\u002F0000-0001-7511-2910",{"name":166,"orcid":167},"Wenzhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-1630-4697",{"name":169,"orcid":170},"Di Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3911-8159",{"name":172,"orcid":173},"Hui Zhao","https:\u002F\u002Forcid.org\u002F0009-0005-4192-5776",{"name":175,"orcid":176},"Ximing Li","https:\u002F\u002Forcid.org\u002F0000-0003-4022-1273",{"name":178,"orcid":9},"Dongdong Cui",{"name":180,"orcid":181},"Fernando Auat Cheein","https:\u002F\u002Forcid.org\u002F0000-0002-6347-7696",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"提出一种抗遮挡和光照变化的番茄成熟度检测方法。","基于深度学习的图像检测，针对遮挡与光照变化优化。","该方法在遮挡和变光照下仍能准确检测番茄成熟度。","农业人工智能与决策模型","可探索多模态融合与轻量化部署，提升田间复杂场景实时检测鲁棒性。","2026-09-17T23:30:01.491379Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":197,"sources":200,"tags":202,"search_phrases":205,"slug":207,"view_count":36,"doi":208,"paper":209,"created_at":226},2519,"OneGrow: Unified Temporal Plant Image and Mask Generation","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.11.750899","Image-based crop phenotyping benefits from image series that capture plant development together with organ-level labels. Such paired data are limited because organ annotation is expensive, and following the same plants over time requires repeated, registered imaging. Existing generative models for plants either synthesize temporal imagery without structural labels or generate labeled images without a temporal dimension. We introduce OneGrow, a latent flow-matching model that jointly models wheat images and their organ-segmentation masks over time. Images and masks share a single frozen image autoencoder. A reveal specifies which content is observed context, so the same model covers tasks such as mask-to-image synthesis, image-to-mask segmentation, and temporal forecasting. For sequences longer than the training window, a sliding-window roll-out generates each new image from the preceding frames, keeping long sequences temporally consistent. We train jointly on a large single-frame wheat dataset and a multi-year temporal dataset, using pseudo-labels from a pretrained segmentation model. We evaluate segmentation and image quality against held-out references and assess the multi-task and temporal behavior qualitatively.","基于图像的作物表型分析受益于能够同时捕捉植物发育过程与器官级标签的图像序列。此类配对数据较为有限，因为器官标注成本高昂，且对同一植株进行长期追踪需要重复的配准成像。现有的植物生成模型要么合成不具备结构标签的时间序列图像，要么生成不具备时间维度的标注图像。我们提出OneGrow，一种潜在流匹配模型，可联合建模小麦图像及其器官分割掩码随时间的演变。图像与掩码共享一个冻结的图像自编码器。通过指定哪些内容作为观测上下文，同一模型即可覆盖掩码到图像合成、图像到掩码分割以及时间预测等任务。对于超出训练窗口的序列，采用滑动窗口展开方式，由前序帧生成每一新图像，从而保持长序列的时间一致性。我们在一个大型单帧小麦数据集和一个多年时间数据集上进行联合训练，使用预训练分割模型生成的伪标签。我们以留出参考数据评估分割和图像质量，并定性评估多任务与时间行为。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-14T00:00:00Z",{"impact":19,"substance":198,"depth":19,"authority":20,"freshness":108,"relevant":22,"comment":199},21,"面向小麦时序图像与器官分割掩码联合生成的流匹配模型，方法新颖、数据规模可观，对作物表型自动化标注有实质推动，值得进入每日精选。",[201],{"name":195,"url":192},[27,28,203,29,204],"小麦","图像生成",[206,86],"农业人工智能 作物表型 图像生成 智慧农业","农业人工智能作物表型图像生成智慧农业-2519","10.64898\u002F2026.09.11.750899",{"doi":208,"openalex_id":210,"authors":211,"venue":195,"cited_by_count":36,"oa_url":192,"card":221,"direction":60,"ingested_from":62},"W7212556016",[212,215,218],{"name":213,"orcid":214},"Mike Boss","https:\u002F\u002Forcid.org\u002F0000-0002-7234-3796",{"name":216,"orcid":217},"Michele Volpi","https:\u002F\u002Forcid.org\u002F0000-0003-2771-0750",{"name":219,"orcid":220},"Lukas Roth","https:\u002F\u002Forcid.org\u002F0000-0003-1435-9535",{"tldr":222,"method":223,"finding":224,"direction":60,"opportunity":225},"提出OneGrow模型，联合生成小麦时序图像与器官分割掩码。","潜在流匹配模型，共享冻结自编码器，滑动窗口滚动生成。","同一模型可完成掩码到图像、图像分割和时序预测，保持长序列一致。","可探索多作物、多器官的时序联合生成，降低表型标注成本。","2026-09-15T23:30:15.715938Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":234,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":240,"search_phrases":243,"slug":245,"view_count":36,"doi":246,"paper":247,"created_at":273},2301,"A crop cultivation monitoring platform for evaluating the early growth of cucumbers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1869455","The precise temporal characterization of early growth dynamics in crops under abiotic stress is critical for stress resistance management in smart agriculture. However, traditional manual measurement methods struggle to achieve high-throughput and accurate quantification of multiple phenotypic parameters over continuous time series. To address this, we developed a full-time-series crop growth monitoring system that continuously collects fine-grained image data of cucumber seed germination and seedling growth under soil culture conditions, constructing an annotated dataset for identifying germination status and quantifying cotyledon area. The YOLOv8n-F_SGX and YOLOv8-seg models were developed and deployed, achieving detection and segmentation accuracies of 0.978 and 0.987, respectively, which enabled automatic monitoring of germination rate and precise extraction of cotyledon area. To further investigate the mitigating effects of nanomaterial priming on salt stress, we conducted germination and seedling growth experiments on cucumber seeds using a series of ZnO-NPs (particle size 30 nm) suspension concentrations at different salt stress levels. The results demonstrated that under salt stress conditions of 0–150 mmol·L −1 , priming with 100 mg·L −1 ZnO-NPs resulted in optimal germination dynamics (highest germination rate and fastest germination speed), along with the largest cotyledon area and highest growth rate in seedlings. In contrast, when the ZnO-NPs concentration increased to 400 mg·L −1 or higher, it significantly inhibited seed germination and seedling growth.","在智慧农业的抗逆管理中，精确表征作物在非生物胁迫下早期生长动态的时间特征至关重要。然而，传统的人工测量方法难以实现对连续时间序列上多个表型参数的高通量、准确量化。为解决这一问题，我们开发了一套全时间序列作物生长监测系统，持续采集土培条件下黄瓜种子萌发和幼苗生长的细粒度图像数据，构建了用于识别萌发状态和量化子叶面积的标注数据集。我们开发并部署了YOLOv8n-F_SGX和YOLOv8-seg模型，分别达到0.978和0.987的检测与分割精度，从而实现了萌发率的自动监测和子叶面积的精确提取。为进一步探究纳米材料引发对盐胁迫的缓解效应，我们在不同盐胁迫水平下，使用一系列浓度ZnO-NPs（粒径30 nm）悬浮液对黄瓜种子进行了萌发和幼苗生长实验。结果表明，在0–150 mmol·L⁻¹盐胁迫条件下，100 mg·L⁻¹ ZnO-NPs引发处理可获得最佳的萌发动态（最高萌发率和最快萌发速度），同时幼苗子叶面积最大、生长速率最高。相反，当ZnO-NPs浓度增至400 mg·L⁻¹或更高时，则显著抑制了种子萌发和幼苗生长。","Frontiers in Plant Science","2026-09-11T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":148,"freshness":108,"relevant":22,"comment":237},"构建黄瓜早期生长全时序监测系统，YOLOv8检测分割精度达0.978\u002F0.987，并给出ZnO-NPs缓解盐胁迫的最佳浓度，方法新颖、数据扎实，对智慧育种与抗逆栽培有参考价值。",[239],{"name":233,"url":230},[27,28,241,29,242],"盐胁迫","黄瓜育种",[244,86],"农业人工智能 作物表型 智慧农业 黄瓜育种","农业人工智能作物表型智慧农业黄瓜育种-2301","10.3389\u002Ffpls.2026.1869455",{"doi":246,"openalex_id":248,"authors":249,"venue":233,"cited_by_count":36,"oa_url":230,"card":267,"direction":271,"ingested_from":62},"W7212301419",[250,252,254,256,258,260,263,265],{"name":251,"orcid":9},"Deyi Lei",{"name":253,"orcid":9},"Zaibiao Zhu",{"name":255,"orcid":9},"Shen Penghong",{"name":257,"orcid":9},"Zhibo Zhong",{"name":259,"orcid":9},"Mohamed Ahmed Moustafa",{"name":261,"orcid":262},"Jieyu Xian","https:\u002F\u002Forcid.org\u002F0000-0002-2526-6644",{"name":264,"orcid":9},"Hongbin Wu",{"name":266,"orcid":9},"Xiuqing Fu",{"tldr":268,"method":269,"finding":270,"direction":271,"opportunity":272},"构建黄瓜早期生长监测平台，用YOLO模型自动量化发芽与子叶面积，并评估ZnO-NPs缓解盐胁迫效果。","连续时序图像采集与标注，YOLOv8n-F_SGX检测和YOLOv8-seg分割","100 mg·L−1 ZnO-NPs在0–150 mmol·L−1盐胁迫下促进发芽和幼苗生长，400","智慧农业 \u002F 农业物联网","可扩展至多作物多胁迫场景，融合时序表型与深度学习实现早期胁迫预警和纳米材料精准调控。","2026-09-13T23:30:09.699346Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":9,"source_name":145,"source_url":277,"published_at":280,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":281,"score_detail":282,"sources":284,"tags":286,"search_phrases":288,"slug":291,"view_count":36,"doi":292,"paper":293,"created_at":314},2280,"Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112390","Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts。Computers and Electronics in Agriculture","利用二维光谱表示和多任务学习结合多门混合专家模型预测植物叶片功能性状。","2026-09-12T00:00:00Z",75,{"impact":107,"substance":76,"depth":77,"authority":148,"freshness":108,"relevant":22,"comment":283},"核心期刊论文，提出二维光谱表征与多门专家混合多任务学习预测叶片功能性状，方法新颖、对作物表型与遥感监测有参考价值，但属细分方法进展，未达重大突破层级。",[285],{"name":145,"url":277},[27,28,113,29,287],"多任务学习",[289,290],"农业人工智能 多任务学习 作物表型 智慧农业","农业人工智能 多任务学习","农业人工智能多任务学习作物表型智慧农业-2280","10.1016\u002Fj.compag.2026.112390",{"doi":292,"openalex_id":294,"authors":295,"venue":145,"cited_by_count":36,"oa_url":9,"card":309,"direction":60,"ingested_from":62},"W7212395422",[296,298,300,302,304,306],{"name":297,"orcid":9},"Jianping Huang",{"name":299,"orcid":9},"Xin Zhang",{"name":301,"orcid":9},"Guanglai Wang",{"name":303,"orcid":9},"Chong Mo",{"name":305,"orcid":9},"Zhenghang Wang",{"name":307,"orcid":308},"Wenlong Song","https:\u002F\u002Forcid.org\u002F0000-0002-8810-532X",{"tldr":310,"method":311,"finding":312,"direction":60,"opportunity":313},"用二维光谱表示与多门混合专家多任务学习预测植物叶片功能性状。","二维光谱表示、多任务学习、多门混合专家模型。","该方法能同时准确预测多种叶片功能性状，优于单任务模型。","可探索将该多任务框架迁移到多作物、多时相的高光谱表型监测中。","2026-09-13T23:30:01.702074Z"]