[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3323":3,"related-3323":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":60,"slug":63,"view_count":64,"doi":8,"paper":65,"created_at":75},3323,"基于空间分辨光谱的鲜食玉米含水率和硬度MaMoNet检测模型——融合Mamba状态空间模型与多门专家混合机制","https:\u002F\u002Fk.sina.com.cn\u002Farticle_5953466437_162dab0450670bdama.html","《智慧农业（中英文）》2026年第4期。许敏、赵鑫、陈艳萍、朱启兵、黄敏（江南大学\u002F江苏省农科院）以带苞叶鲜食玉米为研究对象，构建多通道可见光-近红外空间分辨光谱采集系统，获取玉米样本的多通道光谱数据，并提出一种融合Mamba状态空间模型与多门专家混合(MMoE)机制的多任务预测网络——MaMoNet(Mamba-MMoE Network)。MaMoNet在测试集上玉米籽粒含水率预测的决定系数R²达到了0.91，硬度预测R²达到了0.89，均优于对比模型。消融实验进一步证明Mamba模块在建模光谱长程依赖关系方面具有显著优势，以及MMoE机制能够有效缓解多任务学习中任务间特征竞争问题。",null,"《智慧农业（中英文）》2026年第4期","2026-09-20T00:00:00Z","论文",10,false,81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,9,1,"核心期刊论文，方法新颖且指标可靠，对农产品无损检测有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","鲜食玉米","光谱检测","多任务学习",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,55],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[33],[33],[27],{"code":50,"score":51,"supported":45,"title":52,"abstract":53,"tags":54},"I1",4,[],[25],[25],{"code":56,"score":20,"supported":13,"title":57,"abstract":58,"tags":59},"I2",[],[],[26],[61,62],"江南大学 鲜食玉米 含水率","MaMoNet 玉米 硬度","江南大学鲜食玉米含水率-3323",5,{"doi":8,"openalex_id":8,"authors":66,"venue":8,"cited_by_count":67,"oa_url":8,"card":68,"direction":72,"ingested_from":74},[],0,{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"提出MaMoNet网络，用空间分辨光谱检测鲜食玉米含水率和硬度。","多通道可见光-近红外空间分辨光谱，融合Mamba与MMoE多任务网络。","含水率R²达0.91、硬度R²达0.89，优于对比模型，Mamba与MMoE均有效。","农业遥感与作物表型","可探索Mamba在多作物多品质指标无损检测中的泛化性及田间在线部署。","agent","2026-09-24T00:04:02.557420Z",{"total":77,"page":20,"page_size":77,"items":78},6,[79,127,216,274,348,423],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":84,"source_name":85,"source_url":8,"published_at":86,"category":87,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":93,"tags":95,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":122,"slug":125,"view_count":67,"doi":8,"paper":8,"created_at":126},5518,"河北农林科学院玉米抗病性鉴定开放日 AR智能眼镜助力分级定级","https:\u002F\u002Fwww.cfsn.cn\u002Fnews\u002Fdetail\u002F345\u002F371985.html","河北省农林科学院植物保护研究所主办2026年黄淮海玉米抗病性鉴定田间开放日。作为全国玉米抗病性鉴定牵头单位，该所推进智能化技术嫁接鉴定工作，引入AR智能眼镜拍照自动标注叶片病斑率并判定严重度等级，支持语音控制与后台批量导出，另探索无人机悬停拍照系统开展规模化快速调查。","金秋时节，冀中平原玉米长势喜人、丰收在望。近日，由河北省农林科学院植物保护研究所主办的“2026年黄淮海玉米抗病性鉴定田间开放日”活动在河北省保定市顺利举行。来自玉米育种、植保、种业企业等领域的领导、专家和同仁齐聚一堂，围绕玉米抗病性鉴定标准化、智能化与精准化展开深入交流与现场观摩。\n\n河北省农林科学院植物保护研究所副所长吴玉星表示，玉米抗病性鉴定是育种升级和品种审定推广的必要环节，对保障粮食安全具有重要意义。河北省农林科学院植物保护研究所作为全国玉米抗病性鉴定的牵头单位，严格按照国家标准规范布局、统一试验，全程实行标准化操作程序，确保每一项数据真实、准确、公正。他强调，团队将继续恪守公平、公正、科学、严谨的工作原则，不断优化试验方案，为各试验单位提供优质高效的抗病性鉴定服务，助力优良玉米品种替代升级和高质量发展。\n\n活动期间，河北省农林科学院植物保护研究所研究员石洁结合多年田间鉴定实践，介绍了当前玉米抗病性鉴定面临的现实挑战。她指出，黄淮海地区玉米病害种类多、调查面积大、工作强度高，仅靠人工逐品种、逐病害调查，骨干人员长期在田间超负荷工作，难以满足规模化、精准化鉴定需求。为此，团队积极推动智能化技术嫁接到抗病性鉴定工作中，着力实现数据实时采集、结果直接反馈和精准分级。\n\n北京金禾天成科技有限公司有关负责人介绍了通过AR智能眼镜识别玉米病害及判定严重度等级技术。该技术通过拍照识别，自动标注每片叶片的病斑率和整张图片的病斑率，并给出严重度等级判定，拍照后自动保存识别结果和原图，支持现场快速呈现结果和后台批量导出。系统还可按品种编号自动对应品种名称，支持人工校正和后续复核，有效减轻调查人员劳动强度，提升鉴定效率和标准化水平。\n\n石洁告诉记者，团队正在积极协助研发和应用AR智能眼镜技术，用于玉米叶斑病、穗腐病等病害的精准分级。该设备支持语音控制，可解放双手，在田间行走过程中即可完成“所见即所得”的病情定级，并实时反馈数据。相比传统人眼判断，智能眼镜能够提供更精细的数量化级别，满足抗性基因定位等研究对定量数据的需求。此外，团队还在积极探索无人机高空悬停拍照系统，用于茎腐病、矮花叶病等病害的规模化快速调查，进一步提升鉴定效率。\n\n活动现场还展示了快速检测技术在玉米病害防控中的应用。石洁介绍，该技术可在病害尚未发病的叶片上，通过棉签擦拭、裂解液处理和便携设备检测，30分钟内常温下判断叶片是否携带孢子或已被病原菌侵入。目前可检测病理上几乎所有病害（病毒病除外），包括南方锈病、穗腐病、弯孢叶斑病、瘤黑粉病等。荧光定量PCR版本一次可同时检测8种病原菌，帮助种植大户和种业企业据检测结果用药，实现“检防联动”，从普惠性预防用药转向按需防控、对靶施药、早防早治，大幅节省农药和飞防成本。\n\n本次开放日活动全面开放观摩现场，与会代表近距离了解了玉米抗病性鉴定从试验设计、田间管控、症状跟踪到数据调查的全流程。大家一致认为，智能化鉴定技术和快速检测技术的推广应用，将有力推动黄淮海玉米抗病性鉴定工作向精准化、标准化、高效化迈进，为玉米产业高质量发展保驾护航。（王田）","中国食品安全网 2026-10-21","2026-10-21T00:00:00Z","报道",73,{"impact":16,"substance":90,"depth":91,"authority":12,"freshness":19,"relevant":20,"comment":92},20,16,"省级科研机构开放日，AR智能眼镜与快速检测技术有实质信息增量，属细分领域智能化进展，值得精选。",[94],{"name":85,"url":82},[25,26,96,97,98],"智能装备","玉米病害","抗病性鉴定",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":102},{"candidates":103,"agricultural_evidence":45},[104,109,114,118],{"code":31,"score":105,"supported":45,"title":106,"abstract":107,"tags":108},11,[33],[33],[],{"code":110,"score":20,"supported":13,"title":111,"abstract":112,"tags":113},"F5",[],[],[96],{"code":50,"score":20,"supported":13,"title":115,"abstract":116,"tags":117},[],[],[25],{"code":56,"score":20,"supported":13,"title":119,"abstract":120,"tags":121},[],[],[26],[123,124],"河北农科院 玉米抗病性鉴定 AR智能眼镜","黄淮海 玉米 病害 快速检测","河北农科院玉米抗病性鉴定AR智能眼镜-5518","2026-10-22T00:07:04.176689Z",{"id":128,"title":129,"url":130,"summary":131,"summary_zh":132,"content":8,"source_name":133,"source_url":130,"published_at":134,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":135,"score_detail":136,"sources":138,"tags":140,"primary_topic":143,"secondary_topics":144,"classification":150,"search_phrases":175,"slug":178,"view_count":20,"doi":179,"paper":180,"created_at":215},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":44,"substance":90,"depth":91,"authority":44,"freshness":19,"relevant":20,"comment":137},"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[139],{"name":133,"url":130},[25,26,141,142,97],"边缘计算","深度学习",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[145],{"code":56,"slug":146,"name":147,"path":148,"parent_code":149},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":151},{"candidates":152,"agricultural_evidence":45},[153,157,163,170],{"code":31,"score":18,"supported":45,"title":154,"abstract":155,"tags":156},[32],[33,32],[],{"code":56,"score":158,"supported":45,"title":159,"abstract":160,"tags":162},8,[],[142,161],"deep learning",[26,142],{"code":50,"score":164,"supported":45,"title":165,"abstract":166,"tags":169},7,[],[167,168],"精准农业","precision agriculture",[25],{"code":171,"score":20,"supported":13,"title":172,"abstract":173,"tags":174},"I5",[],[],[141],[176,177],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":179,"openalex_id":181,"authors":182,"venue":133,"cited_by_count":67,"oa_url":130,"card":208,"direction":72,"ingested_from":214},"W7222602850",[183,186,189,191,193,196,198,200,203,205],{"name":184,"orcid":185},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":187,"orcid":188},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":190,"orcid":8},"Bhavani K",{"name":192,"orcid":8},"Sailee N. Panchbhai",{"name":194,"orcid":195},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":197,"orcid":8},"Manoj Kumar S B",{"name":199,"orcid":8},"Dharamvir",{"name":201,"orcid":202},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":204,"orcid":8},"Rani Chhagan Dalvi",{"name":206,"orcid":207},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":209,"method":210,"finding":211,"direction":212,"opportunity":213},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","农业人工智能与决策模型","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","openalex","2026-10-17T23:30:28.248539Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":8,"source_name":222,"source_url":219,"published_at":223,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":224,"score_detail":225,"sources":227,"tags":229,"primary_topic":232,"secondary_topics":233,"classification":235,"search_phrases":254,"slug":257,"view_count":67,"doi":258,"paper":259,"created_at":273},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":158,"substance":16,"depth":91,"authority":44,"freshness":19,"relevant":20,"comment":226},"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[228],{"name":222,"url":219},[25,26,230,33,231],"机器学习","耐盐育种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[234],{"code":56,"slug":146,"name":147,"path":148,"parent_code":149},{"status":38,"locked":13,"version":39,"method":40,"evidence":236},{"candidates":237,"agricultural_evidence":45},[238,245,250],{"code":56,"score":239,"supported":45,"title":240,"abstract":242,"tags":244},19,[241],"machine learning",[230,241,243],"decision support",[26,230],{"code":31,"score":246,"supported":45,"title":247,"abstract":248,"tags":249},15,[32],[33,32],[33],{"code":50,"score":164,"supported":45,"title":251,"abstract":252,"tags":253},[],[167,168],[25],[255,256],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":258,"openalex_id":260,"authors":261,"venue":222,"cited_by_count":67,"oa_url":219,"card":268,"direction":212,"ingested_from":214},"W7221462188",[262,265],{"name":263,"orcid":264},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":266,"orcid":267},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":269,"method":270,"finding":271,"direction":212,"opportunity":272},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z",{"id":275,"title":276,"url":277,"summary":278,"summary_zh":279,"content":8,"source_name":280,"source_url":277,"published_at":281,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"classification":291,"search_phrases":313,"slug":316,"view_count":77,"doi":317,"paper":318,"created_at":347},4018,"UGV based multi-RGBD SLAM framework for high-throughput in-situ 3D phenotyping of field maize","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.08.019","Accurate 3D reconstruction of maize populations under real-world field conditions is a prerequisite for individual-level trait in-situ extraction. Conventional visual mapping techniques frequently encounter challenges in complex field environments, which are characterized by weak textures and significant occlusions. To address these limitations, we propose a novel field maize in-situ phenotyping system utilizing synchronized multi-view RGBD cameras mounted on an Unmanned Ground Vehicle (UGV) platform. Specifically, a fixed multi-camera system is first established to introduce cross-view geometric constraints. Then, a semantic-guided feature distribution regulation strategy is proposed to balance feature sampling between crop foreground regions and background areas to alleviate localization degradation caused by spatially imbalanced feature distributions. Then, multi-view observations are subsequently fused to generate a field-scale dense point cloud with high geometric consistency. Finally, an automated processing pipeline is developed by integrating point cloud density projection and three-dimensional topological analysis. This pipeline enables accurate instance segmentation of individual plants within densely planted maize populations and supports robust in-situ quantification of key morphological traits, including plant height and stem diameter. Field experiments conducted under diverse environmental conditions demonstrated a 94.55% accuracy in plant counting. For morphological traits, the pipeline estimated plant height with an R 2 of 0.85 and an RMSE of 41.29 mm. Stem diameter estimation along two orthogonal axes yields R 2 values of 0.79 and 0.76, with corresponding RMSEs of 1.21 mm and 1.03 mm, respectively. The results validate the feasibility and robustness of the proposed framework for three-dimensional reconstruction in complex agricultural environments, and demonstrate its potential as a reliable solution for high-throughput in-situ crop phenotyping.","在真实田间条件下对玉米群体进行精确三维重建，是实现个体尺度性状原位提取的前提。传统视觉建图技术在弱纹理和严重遮挡为特征的复杂田间环境中经常面临挑战。为解决这些局限，我们提出了一种新型田间玉米原位表型分析系统，利用搭载于无人地面车辆（UGV）平台上的同步多视角RGBD相机。具体而言，首先建立固定多相机系统以引入跨视角几何约束。然后，提出一种语义引导的特征分布调控策略，以平衡作物前景区域与背景区域之间的特征采样，从而缓解由空间不平衡特征分布引起的定位退化。随后，融合多视角观测以生成具有高几何一致性的田块尺度稠密点云。最后，通过集成点云密度投影和三维拓扑分析，开发了一套自动化处理流程。该流程能够对密集种植的玉米群体中的单株植物进行准确实例分割，并支持对株高和茎粗等关键形态性状的稳健原位量化。在多种环境条件下进行的田间试验表明，植株计数准确率达到94.55%。对于形态性状，该流程估计株高的R²为0.85，RMSE为41.29 mm。沿两个正交轴估计茎粗的R²分别为0.79和0.76，相应的RMSE分别为1.21 mm和1.03 mm。结果验证了所提出框架在复杂农业环境中进行三维重建的可行性和鲁棒性，并展示了其作为高通量原位作物表型分析可靠解决方案的潜力。","Artificial Intelligence in Agriculture","2026-10-01T00:00:00Z",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":283},"提出UGV多RGBD SLAM框架实现田间玉米高通量原位三维表型，方法新颖、数据扎实，对智慧农业育种与表型研究有参考价值。",[285],{"name":280,"url":277},[25,26,33,287,288],"三维表型","无人地面车",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":292},{"candidates":293,"agricultural_evidence":45},[294,298,305,309],{"code":31,"score":246,"supported":45,"title":295,"abstract":296,"tags":297},[32],[33,32],[33],{"code":299,"score":77,"supported":45,"title":300,"abstract":301,"tags":304},"I3",[],[302,303],"作物表型","crop phenotyping",[],{"code":50,"score":20,"supported":13,"title":306,"abstract":307,"tags":308},[],[],[25],{"code":56,"score":20,"supported":13,"title":310,"abstract":311,"tags":312},[],[],[26],[314,315],"UGV 多相机 SLAM 玉米 三维表型","田间玉米 原位表型 点云","UGV多相机SLAM玉米三维表型-4018","10.1016\u002Fj.aiia.2026.08.019",{"doi":317,"openalex_id":319,"authors":320,"venue":280,"cited_by_count":67,"oa_url":277,"card":342,"direction":72,"ingested_from":214},"W7215041090",[321,323,326,328,331,334,336,338,340],{"name":322,"orcid":8},"Si Yang",{"name":324,"orcid":325},"Yuqiang Liang","https:\u002F\u002Forcid.org\u002F0009-0005-9307-6091",{"name":327,"orcid":8},"Guanming Huang",{"name":329,"orcid":330},"Guangjie Qiu","https:\u002F\u002Forcid.org\u002F0000-0001-9477-9593",{"name":332,"orcid":333},"Weiliang Wen","https:\u002F\u002Forcid.org\u002F0000-0001-7184-5121",{"name":335,"orcid":8},"Wang Chuanyu",{"name":337,"orcid":8},"Wenbo Gou",{"name":339,"orcid":8},"Xinyu Guo",{"name":341,"orcid":8},"Chunjiang Zhao",{"tldr":343,"method":344,"finding":345,"direction":72,"opportunity":346},"提出UGV搭载多RGBD相机的田间玉米原位3D表型系统，实现高通量三维重建与性状提取。","UGV多RGBD同步采集，语义引导特征分布调节与多视角融合，密度投影和拓扑分析分","植株计数准确率94.55%，株高R²=0.85、RMSE=41.29mm，茎径R²约0.76-0.7","可探索多模态传感器融合与轻量化SLAM，提升密植冠层遮挡下的实时单株表型精度。","2026-10-02T23:30:19.673397Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":8,"source_name":354,"source_url":351,"published_at":281,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":363,"classification":368,"search_phrases":388,"slug":391,"view_count":392,"doi":393,"paper":394,"created_at":422},3973,"A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112465","A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data。Computers and Electronics in Agriculture","一种基于物理情境条件调节的多模态融合网络，用于利用多源无人机遥感数据估算玉米冠层氮含量。《计算机与电子农业》","Computers and Electronics in Agriculture",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":356},"多源无人机遥感与物理上下文多模态融合估算玉米冠层氮素，方法新颖、数据可靠，对精准施肥有参考价值。",[358],{"name":354,"url":351},[25,26,33,360,361],"遥感","氮素营养",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[364],{"code":299,"slug":365,"name":366,"path":367,"parent_code":149},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":38,"locked":13,"version":39,"method":40,"evidence":369},{"candidates":370,"agricultural_evidence":45},[371,375,380,384],{"code":31,"score":246,"supported":45,"title":372,"abstract":373,"tags":374},[32],[33,32],[33],{"code":299,"score":246,"supported":45,"title":376,"abstract":378,"tags":379},[377],"remote sensing",[360,377],[360],{"code":50,"score":20,"supported":13,"title":381,"abstract":382,"tags":383},[],[],[25],{"code":56,"score":20,"supported":13,"title":385,"abstract":386,"tags":387},[],[],[26],[389,390],"无人机 遥感 玉米 氮含量","多模态融合 冠层氮素","无人机遥感玉米氮含量-3973",2,"10.1016\u002Fj.compag.2026.112465",{"doi":393,"openalex_id":395,"authors":396,"venue":354,"cited_by_count":67,"oa_url":8,"card":417,"direction":72,"ingested_from":214},"W7215084428",[397,399,401,404,407,409,411,414],{"name":398,"orcid":8},"Wang Baoju",{"name":400,"orcid":8},"Jing Zhao",{"name":402,"orcid":403},"Yu Yan","https:\u002F\u002Forcid.org\u002F0009-0004-4202-6935",{"name":405,"orcid":406},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"name":408,"orcid":8},"Xinlong Li",{"name":410,"orcid":8},"Huizheng Wang",{"name":412,"orcid":413},"Weiguang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9862-5839",{"name":415,"orcid":416},"Yubin Lan","https:\u002F\u002Forcid.org\u002F0000-0001-6664-5571",{"tldr":418,"method":419,"finding":420,"direction":72,"opportunity":421},"提出物理上下文条件化多模态融合网络，用多源无人机遥感估算玉米冠层氮含量。","多源无人机遥感数据与物理上下文条件化多模态融合网络。","该网络能提升玉米冠层氮含量估算精度，融合多源数据优于单一模态。","可探索物理先验与多模态融合在不同作物、生育期及跨区域迁移中的泛化能力。","2026-10-02T23:30:02.413294Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":8,"content":428,"source_name":429,"source_url":8,"published_at":430,"category":87,"cover_url":8,"hotness":12,"is_selected":13,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":439,"secondary_topics":440,"classification":441,"search_phrases":461,"slug":464,"view_count":51,"doi":8,"paper":8,"created_at":465},3933,"大数据智能决策技术助力黑龙江玉米生产数字化转型","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F572335.shtm","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办。项目组首次将深度学习应用于规模化农场种植布局优化，研发基于PPO强化学习的农场尺度智能种植规划算法；研发自适应玉米生育进程动态监测预报算法，分辨率10m~1km，可逐日动态监测，关键生育期可提前20天精准预报；构建生产、生态维度综合效益评估算法。","[![Image 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[人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨 来源：中国科学报 发布时间：2026\u002F9\u002F29 20:15:16 选择字号：小 中 大 大数据智能决策技术助力黑龙江玉米生产数字化转型 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办。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主办，北大荒集团北安分公司、红星农场协办，共70余人参会。 ![Image 2](https:\u002F\u002Frmtzx.sciencenet.cn\u002Fkxwsprint\u002F6abb7e11e4b078fce44b1363.jpg)现场展示的不同决策方案的应用效果。中国农科院供图 会议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，现场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。项目负责人、北京市农林科学院信息技术研究中心杨贵军研究员系统介绍了粮食生产大数据平台的研发与应用进展。 课题负责人、中国农业科学院农业资源与农业区划研究所研究员孙亮重点介绍了玉米智能决策技术领域取得的突破性成果。 在产前种植规划方面，项目组首次将深度学习应用于规模化农场种植布局优化，研发了基于PPO强化学习的农场尺度智能种植规划算法，在保障国家粮食安全的前提下，统筹经济效益与农业绿色发展，实现多目标协同。 ![Image 3](https:\u002F\u002Frmtzx.sciencenet.cn\u002Fkxwsprint\u002F6abb7e55e4b078fce44b1365.png)观摩会现场。中国农科院供图 在产中动态监测方面，项目组研发适配粮食主产区、农场多尺度的自适应玉米生育进程动态监测预报算法，分辨率10m~1km，可实现逐日动态监测，关键生育期可提前20天完成精准预报。该算法为玉米关键生育期追肥处方的动态实时更新、产量与适宜收获期预测提供了坚实技术基础。 在产后效益评价方面，项目组构建生产、生态维度综合效益评估算法，围绕北大荒玉米生产全环节社会化服务模式，精准量化其在提升生产效益、改善农田生态等方面核心优势，为优化农场要素配置、推动农业绿色发展提供理论与实践支撑。 据介绍，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。相关成果将为黑龙江玉米生产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。 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 4](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572335)\n\n相关新闻 相关论文\n*   1\n*   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