[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3067":3,"related-3067":62},{"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":61},3067,"Automated detection of visible venation patterns in cowpea leaves under agricultural engineering","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sasc.2026.200636","Accurate assessment of crop leaf characteristics is important for automated crop phenotyping and precision agriculture. However, manual assessment of leaf characteristics is time-consuming and may be affected by subjective interpretation. This study proposes an RGB-based convolutional neural network (CNN) framework for automated classification of surface-visible venation patterns in cowpea (Vigna unguiculata) leaves. A dataset of 2500 RGB cowpea leaf images, comprising Healthy and Structurally Stressed samples, was used to develop and evaluate the proposed framework. The images were preprocessed using standard image-processing operations and subsequently analyzed using a CNN for binary classification of the two visible-venation categories. Canny edge detection was additionally employed as a supplementary visualization technique to highlight surface-visible venation patterns and support qualitative interpretation; it was not used as the primary classification mechanism. The proposed CNN achieved 95.4% accuracy, 94.8% precision, 95.1% recall, and 95.0% F1-score on the evaluated test dataset. Comparative experiments with conventional feature-based approaches, including GLCM, SIFT, and HOG, showed lower observed classification performance. The results demonstrate the potential of RGB-based deep learning for automated analysis of surface-visible cowpea leaf venation patterns and provide a non-destructive image-based approach for crop phenotyping. The proposed framework can support scalable agricultural image analysis while avoiding claims of direct Surface-visible vein pattern anatomical characterization. The dataset is available at this link: “ https:\u002F\u002Fgithub.com\u002Fvijayachoudharyresearcher\u002FComputer-Vision–Cowpea-Leaves-Internal-Structure “.","准确评估作物叶片特征对于自动化作物表型分析和精准农业具有重要意义。然而，人工评估叶片特征耗时较长，且可能受到主观解释的影响。本研究提出了一种基于RGB的卷积神经网络（CNN）框架，用于对豇豆（Vigna unguiculata）叶片表面可见脉纹模式进行自动分类。研究使用包含健康和结构胁迫样本的2500张RGB豇豆叶片图像数据集来开发和评估所提出的框架。图像经标准图像处理操作预处理后，利用CNN对两个可见脉纹类别进行二分类分析。此外，研究还采用Canny边缘检测作为补充可视化技术，以突出表面可见脉纹模式并支持定性解释；其并未被用作主要分类机制。所提出的CNN在评估测试数据集上取得了95.4%的准确率、94.8%的精确率、95.1%的召回率和95.0%的F1分数。与传统基于特征的方法（包括GLCM、SIFT和HOG）的比较实验显示，其分类性能较低。结果表明，基于RGB的深度学习在自动化分析豇豆叶片表面可见脉纹模式方面具有潜力，并为作物表型分析提供了一种基于图像的非破坏性方法。所提出的框架可支持可扩展的农业图像分析，同时避免声称对表面可见脉纹模式进行直接的解剖学表征。数据集可通过以下链接获取：“https:\u002F\u002Fgithub.com\u002Fvijayachoudharyresearcher\u002FComputer-Vision–Cowpea-Leaves-Internal-Structure”。",null,"Systems and Soft Computing","2026-09-18T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,16,13,8,1,"基于RGB与CNN的豇豆叶脉自动识别研究，方法清晰、数据规模可观，对作物表型与精准农业有参考价值，但属细分领域技术进展，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","作物表型","图像识别","豇豆",[33,34],"豇豆叶片 叶脉 识别","RGB CNN 作物表型","豇豆叶片叶脉识别-3067",0,"10.1016\u002Fj.sasc.2026.200636",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7213555214",[41,44,47,49,51],{"name":42,"orcid":43},"Vijaya Choudhary","https:\u002F\u002Forcid.org\u002F0000-0003-3417-1943",{"name":45,"orcid":46},"Paramita Guha","https:\u002F\u002Forcid.org\u002F0000-0001-6696-4812",{"name":48,"orcid":9},"Jai Prakash Mishra",{"name":50,"orcid":9},"Himanshu Sharma",{"name":52,"orcid":53},"Munish Sabharwal","https:\u002F\u002Forcid.org\u002F0000-0002-7338-6982",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"用RGB卷积神经网络自动分类豇豆叶片可见脉纹，区分健康与结构胁迫样本。","2500张RGB豇豆叶图像，CNN二分类，Canny边缘检测辅助可视化，对比GL","CNN达95.4%准确率，优于传统特征方法，可实现无损作物表型分析。","农业遥感与作物表型","可扩展至多作物、多胁迫类型及田间实时检测，并结合多光谱提升脉纹表型解析。","openalex","2026-09-21T23:30:18.575016Z",{"total":63,"page":22,"page_size":63,"items":64},6,[65,86,118,159,206,233],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":9,"content":70,"source_name":71,"source_url":9,"published_at":72,"category":73,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":77,"tags":79,"search_phrases":81,"slug":84,"view_count":36,"doi":9,"paper":9,"created_at":85},3103,"山东省农科院举办人工智能专题舜耕论坛暨培训交流会——浙江大学数字农业农村研究中心主任何勇教授作\"作物表型多源多尺度智能感知技术与装备\"专题报告","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml","9-16 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智慧引领高质量发展”专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合，激活科研创新内生动力。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，院党委副书记、院长李向东主持会议并讲话。\n\n报告题为《作物表型多源多尺度智能感知技术与装备》，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞、组织器官、表型获取装备三个层级，系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻、草莓、茶叶等作物的示范应用，解读无人机、作物智慧管理装备关键技术及应用路径，提出农业科技创新要加速人工智能深度融入，以多技术交叉融合助推智慧农业高质量发展，为我院农业人工智能科研布局提供重要参考。\n\n李向东指出，报告紧扣智慧农业发展前沿，兼具理论深度和实践价值，对我院科研迭代升级具有重要指导意义。他强调，要提高政治站位，把握战略导向。深入学习贯彻习近平总书记关于人工智能创新发展的重要指示精神，把智慧农业摆在全院科技创新突出位置，强化机遇意识，开辟农业科研新赛道。要聚焦主责主业，精准靶向攻坚。各创新团队依托现有科研基础，推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合，坚持问题导向，紧扣产业瓶颈凝练攻关方向，推动智能技术赋能科研实践。要压实闭环管理，推动落地见效。细化攻关任务清单，强化项目、平台、人才、经费要素保障，健全调度考核机制，将人工智能攻关及成果产出纳入评价体系，力争产出高水平科研成果、实用技术与智能装备，形成可复制推广的农业人工智能应用模式。全院科研人员要以此次论坛为契机，拓宽科研视野，聚力攻关，推动我院智慧农业科技创新再上新台阶。\n\n会议设主会场和视频分会场，院属各单位主要负责人、科研分管负责人，拟组建创新团队首席、副首席及45岁以下青年科研人员代表500余人参加会议。\n\n（撰写：陈英凯 核稿：张文君）\n\n分享\n\n分享到\n\n[微信](http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml 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25](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F3fcb1043-85f2-4dca-958d-b207876c780b.png)\n\n![Image 26](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F219d5bb3-cb2b-4115-a146-fc799b429998.png)","山东省农业科学院","2026-09-16T00:00:00Z","报道",55,{"impact":17,"substance":20,"depth":17,"authority":17,"freshness":63,"relevant":22,"comment":76},"省级农科院举办的AI专题学术交流活动，内容聚焦作物表型智能感知，有一定专业价值但属会议报道，信息增量有限。",[78],{"name":71,"url":68},[27,28,29,80],"学术交流",[82,83],"山东省农科院 舜耕论坛 人工智能","何勇 作物表型 智能感知","山东省农科院舜耕论坛人工智能-3103","2026-09-22T00:05:35.424987Z",{"id":87,"title":88,"url":89,"summary":90,"summary_zh":9,"content":9,"source_name":91,"source_url":9,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":99,"tags":101,"search_phrases":104,"slug":107,"view_count":36,"doi":9,"paper":108,"created_at":117},3047,"基于CNN\u002FLR\u002FGA-BP的辣椒叶病识别稳定性与精度多维度视觉特征比较","https:\u002F\u002Fwww.mdpi.com\u002F2311-7524\u002F12\u002F9\u002F1176","塔里木大学Xueting Ma、Yifei Li等联合东北林业大学、南京农业大学，构建1260份辣椒叶数据集（健康、细菌性斑点病、黄化卷叶病），采用Lab b-channel、RGB super-green、Otsu-ACWE三种分割算法系统评估最优预处理方案，提取32个融合视觉特征并以随机森林消除7个低贡献冗余特征保留25个判别变量。20次独立重复试验表明：CNN测试平均精度97.67%、平均AUC 0.999，稳定性最佳；LR计算成本低适合资源受限场景；GA-BP非线性拟合能力弱、预测波动严重。该研究为辣椒叶病识别提供标准化实验框架。","MDPI Horticulturae 12(9):1176","2026-09-19T00:00:00Z",78,{"impact":19,"substance":95,"depth":96,"authority":97,"freshness":21,"relevant":22,"comment":98},22,18,14,"基于1260份辣椒叶数据集系统比较CNN\u002FLR\u002FGA-BP三种模型，方法规范、结论可靠，对作物病害智能识别有参考价值。",[100],{"name":91,"url":89},[27,28,30,102,103],"辣椒叶病","病害诊断",[105,106],"塔里木大学 辣椒叶病 识别","CNN 辣椒叶病 视觉特征","塔里木大学辣椒叶病识别-3047",{"doi":9,"openalex_id":9,"authors":109,"venue":9,"cited_by_count":36,"oa_url":9,"card":110,"direction":114,"ingested_from":116},[],{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"比较CNN、LR、GA-BP对辣椒叶病的识别精度与稳定性，并评估三种分割预处理方案。","1260份辣椒叶图像，Lab\u002FRGB\u002FOtsu-ACWE分割，32特征经随机森林","CNN平均精度97.67%、AUC 0.999且最稳定；LR成本低适合资源受限；GA-BP波动严重。","农业人工智能与决策模型","可探索轻量化CNN与LR融合的田间实时识别方案，并验证多作物、多病害下的泛化稳定性。","agent","2026-09-21T00:04:39.222838Z",{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":131,"tags":133,"search_phrases":136,"slug":139,"view_count":36,"doi":140,"paper":141,"created_at":158},3017,"Automated seedling vigor estimation in cucumbers using digital image processing","https:\u002F\u002Fdoi.org\u002F10.65764\u002Ftjas.2026.267412","Background and Objective: Farmers often rely on experience rather than quantitative indicators to determine seedling quality, resulting in inefficiencies in resource allocation. This study aimed to (1) compare seedling vigor and growth characteristics between open-pollinated (OP) and F1 hybrid cucumber seed types, and (2) develop a non-destructive predictive model for seedling vigor estimation based on digital image analysis.Methodology: Seedling images were acquired under controlled LED lighting using a 5 MP digital camera positioned 30 cm above 14-day-old seedlings. A YOLOv8-based object detection model was applied to detect and isolate true leaf regions, from which pixel count and RGB color values were extracted as input features for a multiple linear regression model to predict the seedling vigor index (SVI).Main Results: The YOLOv8 object detection model achieved a mean average precision (mAP@0.5) of 96.00%, precision of 93.40%, and recall of 94.40% in detecting true leaves. Multiple linear regression analysis was conducted using the Scikit-learn library in Python. Scikit-learn provides regression-based machine learning algorithms, including multiple linear regression. The equation was then applied to the training and test sets, using the pixel values of true leaves as the independent variable and SVI as the dependent variable. Using multiple regression analysis, the trained model generated the equation SVI = -2.27 + (2.84 × 10-5 × pixel) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B), R2 = 0.57 and RMSE = 1.10. The model achieved the test set, r = 0.79 and RMSE = 1.34. The model performance showed a correlation coefficient of 0.85 for OP data and 0.91 for F1 hybrid data.Conclusions: The results demonstrate that digital image processing combined with object detection provides a non-destructive and effective approach to estimate seedling vigor quality. The predictive models developed from OP and F1 hybrid datasets indicate potential application in precision agriculture for automated seedling quality assessment and transplanting decision support.","背景与目标：农民通常依赖经验而非定量指标来判断幼苗质量，导致资源配置效率低下。本研究旨在（1）比较开放授粉（OP）与F1杂交黄瓜种子类型之间的幼苗活力和生长特性，（2）基于数字图像分析开发一种用于幼苗活力评估的无损预测模型。方法：使用500万像素数码相机置于14日龄幼苗上方30 cm处，在受控LED光照下获取幼苗图像。应用基于YOLOv8的目标检测模型检测并分离真叶区域，从中提取像素计数和RGB颜色值作为多元线性回归模型的输入特征，以预测幼苗活力指数（SVI）。主要结果：YOLOv8目标检测模型在检测真叶时达到了96.00%的平均精度均值（mAP@0.5）、93.40%的精确率和94.40%的召回率。使用Python中的Scikit-learn库进行多元线性回归分析。Scikit-learn提供基于回归的机器学习算法，包括多元线性回归。随后将该方程应用于训练集和测试集，以真叶像素值作为自变量，SVI作为因变量。通过多元回归分析，训练模型生成的方程为SVI = -2.27 + (2.84 × 10-5 × 像素) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B)，R2 = 0.57，RMSE = 1.10。该模型在测试集上达到r = 0.79，RMSE = 1.34。模型性能显示，OP数据的相关系数为0.85，F1杂交数据的相关系数为0.91。结论：结果表明，数字图像处理结合目标检测为评估幼苗活力质量提供了一种无损且有效的方法。基于OP和F1杂交数据集开发的预测模型表明，其在精准农业中具有用于自动化幼苗质量评估和移栽决策支持的潜在应用。","Thai Journal of Agricultural Science",71,{"impact":17,"substance":127,"depth":128,"authority":17,"freshness":129,"relevant":22,"comment":130},21,17,9,"基于YOLOv8与多元回归的黄瓜幼苗活力无损估测，方法具体、指标完整，对智慧育苗有参考价值，但属细分作物研究，影响范围有限。",[132],{"name":124,"url":121},[27,28,134,30,135],"无损检测","黄瓜育苗",[137,138],"黄瓜 幼苗活力 图像处理","YOLOv8 幼苗 检测","黄瓜幼苗活力图像处理-3017","10.65764\u002Ftjas.2026.267412",{"doi":140,"openalex_id":142,"authors":143,"venue":124,"cited_by_count":36,"oa_url":121,"card":152,"direction":157,"ingested_from":60},"W7213631983",[144,146,148,150],{"name":145,"orcid":9},"Thanabodee Withunchettanan",{"name":147,"orcid":9},"Raksak Sermsak",{"name":149,"orcid":9},"Pichittra Kaewsorn",{"name":151,"orcid":9},"Kriengkri Kaewtrakulpong",{"tldr":153,"method":154,"finding":155,"direction":114,"opportunity":156},"用YOLOv8检测黄瓜真叶并结合多元回归，实现幼苗活力指数无损预测。","LED下拍摄14天幼苗，YOLOv8分割真叶，提取像素与RGB做多元线性回归。","YOLOv8检测mAP@0.5达96%，模型测试r=0.79，F1杂交种相关性达0.91。","可扩展多品种、多环境数据，融合时序图像与深度学习提升活力预测泛化性。","数字乡村与农业信息化","2026-09-20T23:30:26.929607Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":72,"category":12,"cover_url":9,"hotness":166,"is_selected":14,"score":167,"score_detail":168,"sources":170,"tags":174,"search_phrases":177,"slug":180,"view_count":36,"doi":181,"paper":182,"created_at":205},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。","arXiv (Cornell University)",25,74,{"impact":17,"substance":95,"depth":96,"authority":20,"freshness":129,"relevant":22,"comment":169},"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。",[171,172],{"name":165,"url":162},{"name":165,"url":173},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.18129",[27,28,29,175,176],"多模态学习","叶片计数",[178,179],"MCLC-NET 叶片计数","多模态 持续学习 作物表型","MCLC-NET叶片计数-2868","10.48550\u002Farxiv.2609.18129",{"doi":181,"openalex_id":183,"authors":184,"venue":165,"cited_by_count":36,"oa_url":162,"card":200,"direction":58,"ingested_from":60},"W7213503285",[185,188,190,193,195,198],{"name":186,"orcid":187},"Ruchi Bhatt","https:\u002F\u002Forcid.org\u002F0009-0003-8222-5640",{"name":189,"orcid":9},"Pratibha Kumari",{"name":191,"orcid":192},"Shreya Bansal","https:\u002F\u002Forcid.org\u002F0000-0002-3135-291X",{"name":194,"orcid":9},"Vedant Agnihotri",{"name":196,"orcid":197},"Dwarikanath Mahapatra","https:\u002F\u002Forcid.org\u002F0000-0001-9749-7858",{"name":199,"orcid":9},"Mukesh Saini",{"tldr":201,"method":202,"finding":203,"direction":58,"opportunity":204},"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。","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。","2026-09-18T23:30:22.580822Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":9,"content":9,"source_name":211,"source_url":9,"published_at":212,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":213,"sources":215,"tags":217,"search_phrases":220,"slug":223,"view_count":36,"doi":9,"paper":224,"created_at":232},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",{"impact":96,"substance":18,"depth":128,"authority":20,"freshness":13,"relevant":22,"comment":214},"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。",[216],{"name":211,"url":209},[27,28,218,29,219],"农业遥感","隐私保护",[221,222],"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":225,"venue":9,"cited_by_count":36,"oa_url":9,"card":226,"direction":230,"ingested_from":116},[],{"tldr":227,"method":228,"finding":229,"direction":230,"opportunity":231},"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。","2026-09-18T00:03:31.115969Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":165,"source_url":239,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":241,"sources":243,"tags":245,"search_phrases":248,"slug":251,"view_count":36,"doi":252,"paper":253,"created_at":271},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":19,"substance":18,"depth":128,"authority":20,"freshness":21,"relevant":22,"comment":242},"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。",[244],{"name":165,"url":239},[27,28,246,29,247],"遥感","三维重建",[249,250],"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":252,"openalex_id":254,"authors":255,"venue":165,"cited_by_count":36,"oa_url":265,"card":266,"direction":58,"ingested_from":60},"W7213397688",[256,259,261,264],{"name":257,"orcid":258},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":260,"orcid":9},"Theo Morales",{"name":262,"orcid":263},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":199,"orcid":9},"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":267,"method":268,"finding":269,"direction":58,"opportunity":270},"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"]