[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3006":3,"related-3006":73},{"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":72},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。",null,"Computers and Electronics in Agriculture","2026-09-19T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","玉米","遥感","高通量表型",[32,33],"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",0,"10.1016\u002Fj.compag.2026.112450",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":65,"direction":69,"ingested_from":71},"W7213658705",[40,42,44,46,48,51,53,56,58,60,62],{"name":41,"orcid":9},"Penglei Zhang",{"name":43,"orcid":9},"Tianbo Hao",{"name":45,"orcid":9},"Zhuoyuan Zhao",{"name":47,"orcid":9},"Hong Sun",{"name":49,"orcid":50},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":52,"orcid":9},"Zheng Cui",{"name":54,"orcid":55},"Ta Na","https:\u002F\u002Forcid.org\u002F0000-0002-1348-5655",{"name":57,"orcid":9},"Lang Qiao",{"name":59,"orcid":9},"Durval Dourado Neto",{"name":61,"orcid":9},"Feng Yang",{"name":63,"orcid":64},"Jingzhu Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"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。","openalex","2026-09-20T23:30:01.739142Z",{"total":74,"page":21,"page_size":74,"items":75},6,[76,119,169,233,272,301],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":9,"source_name":82,"source_url":79,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":90,"tags":92,"search_phrases":94,"slug":97,"view_count":35,"doi":98,"paper":99,"created_at":118},2058,"Machine Learning-Based Prediction of Fall Armyworm (Spodoptera frugiperda) Outbreaks in Maize Production Systems of Northern Nigeria Using Climate and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.58578\u002Fkijst.v3i3.12026","Fall Armyworm (Spodoptera frugiperda) poses a substantial threat to maize production across sub-Saharan Africa, particularly in Nigeria, where climatic variability intensifies the risk of pest outbreaks. This study developed and evaluated machine learning models for predicting Fall Armyworm outbreaks in northern Nigeria using integrated climate and remote sensing variables. A retrospective modeling framework was applied to simulated but biologically constrained datasets covering a six-year period (2019–2024) and comprising temperature, rainfall, relative humidity, Normalized Difference Vegetation Index (NDVI), and vegetation condition indices. Three supervised learning algorithms—Random Forest, Support Vector Machine, and Gradient Boosting—were trained and validated using five-fold cross-validation. Gradient Boosting achieved the highest predictive accuracy at 92.8%, followed by Random Forest at 89.2% and Support Vector Machine at 84.6%. Temperature, relative humidity, and NDVI emerged as the most influential predictors of outbreak occurrence, while the integration of satellite-derived vegetation indices improved overall model performance. These findings demonstrate the potential of combining machine learning with remote sensing data to develop scalable and cost-effective early warning systems for agricultural pest management. However, because the models were developed using simulated data, their predictive validity requires confirmation using field-collected observational data. The proposed framework contributes to data-driven pest surveillance by providing a basis for anticipating outbreaks and supporting timely decision-making in maize production systems.","草地贪夜蛾（Spodoptera frugiperda）对撒哈拉以南非洲的玉米生产构成重大威胁，在尼日利亚尤为突出，因为气候变率加剧了害虫暴发的风险。本研究开发并评估了机器学习模型，利用气候与遥感综合变量预测尼日利亚北部草地贪夜蛾的暴发。研究采用回顾性建模框架，基于模拟但受生物学约束的数据集，覆盖六年时间（2019—2024年），包括温度、降雨量、相对湿度、归一化植被指数（NDVI）及植被状况指数。三种监督学习算法——随机森林、支持向量机和梯度提升——通过五折交叉验证进行训练与验证。梯度提升取得了最高的预测准确率，达92.8%，其次为随机森林（89.2%）和支持向量机（84.6%）。温度、相对湿度和NDVI是暴发发生最具影响力的预测因子，而卫星衍生植被指数的整合提升了模型的整体性能。这些发现表明，将机器学习与遥感数据相结合，有望开发可扩展且成本效益高的农业害虫管理预警系统。然而，由于模型基于模拟数据开发，其预测效度仍需利用实地观测数据加以验证。所提出的框架为数据驱动的害虫监测提供了依据，有助于预判暴发并支持玉米生产系统中的及时决策。","Kwaghe International Journal of Sciences and Technology","2026-09-09T00:00:00Z",75,{"impact":17,"substance":86,"depth":87,"authority":88,"freshness":20,"relevant":21,"comment":89},20,16,12,"将机器学习与气候及遥感数据结合用于尼日利亚北部玉米草地贪夜蛾暴发预测，方法框架有参考价值，但基于模拟数据、结论可靠性待田间验证，属细分领域研究进展。",[91],{"name":82,"url":79},[26,27,28,29,93],"病虫害预警",[95,96],"农业人工智能 病虫害预警 智慧农业 玉米","农业人工智能 病虫害预警","农业人工智能病虫害预警智慧农业玉米-2058","10.58578\u002Fkijst.v3i3.12026",{"doi":98,"openalex_id":100,"authors":101,"venue":82,"cited_by_count":35,"oa_url":9,"card":112,"direction":69,"ingested_from":71},"W7212022953",[102,104,106,108,110],{"name":103,"orcid":9},"Okwor Jude I.",{"name":105,"orcid":9},"Attamah Chinyere G.",{"name":107,"orcid":9},"Uchendu Christian N.",{"name":109,"orcid":9},"Onyima John O.",{"name":111,"orcid":9},"Idoko Bartholomew",{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"用气候与遥感数据构建机器学习模型预测尼日利亚北部玉米草地贪夜蛾暴发。","随机森林、SVM、梯度提升，结合温度、降雨、湿度、NDVI等模拟数据，五折交叉验","梯度提升准确率最高达92.8%，温度、相对湿度和NDVI是最关键预测因子。","农业人工智能与决策模型","模型基于模拟数据，亟需用田间实测数据验证，并开发可扩展的实时早期预警系统。","2026-09-10T23:30:27.820508Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":10,"source_url":122,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":129,"tags":131,"search_phrases":133,"slug":136,"view_count":35,"doi":137,"paper":138,"created_at":168},2016,"Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112381","Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales。Computers and Electronics in Agriculture","基于光谱数据的不同空间尺度玉米产量预测中，将迁移学习与SCOPE模型相结合",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":127,"relevant":21,"comment":128},8,"将迁移学习与SCOPE辐射传输模型结合，实现跨空间尺度光谱数据的玉米产量预测，方法新颖且对遥感估产有实用价值，值得进入每日精选。",[130],{"name":10,"url":122},[26,27,132,28,29],"产量预测",[134,135],"农业人工智能 产量预测 智慧农业 玉米","农业人工智能 产量预测","农业人工智能产量预测智慧农业玉米-2016","10.1016\u002Fj.compag.2026.112381",{"doi":137,"openalex_id":139,"authors":140,"venue":10,"cited_by_count":35,"oa_url":9,"card":163,"direction":69,"ingested_from":71},"W7211999297",[141,143,146,149,151,153,155,156,158,160,162],{"name":142,"orcid":9},"Ruomei Zhao",{"name":144,"orcid":145},"Yanling Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-2323-2600",{"name":147,"orcid":148},"Weimin Guo","https:\u002F\u002Forcid.org\u002F0000-0003-4318-7154",{"name":150,"orcid":9},"Wei Liu",{"name":152,"orcid":9},"Long Zhao",{"name":154,"orcid":9},"Xiaoyuan Tian",{"name":49,"orcid":50},{"name":157,"orcid":9},"Lulu An",{"name":159,"orcid":9},"Aiguo Wang",{"name":161,"orcid":9},"Qiang Xu",{"name":47,"orcid":9},{"tldr":164,"method":165,"finding":166,"direction":69,"opportunity":167},"结合迁移学习与SCOPE模型，利用不同空间尺度光谱数据预测玉米产量。","迁移学习与SCOPE辐射传输模型，多尺度光谱数据。","该方法能跨空间尺度提升玉米产量预测精度。","可探索迁移学习在跨区域、跨年份及多作物产量预测中的泛化能力。","2026-09-10T23:30:01.419872Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":10,"source_url":172,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":183,"search_phrases":185,"slug":188,"view_count":35,"doi":189,"paper":190,"created_at":232},1276,"Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112336","Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity。Computers and Electronics in Agriculture","利用无人机获取的增强型病害检测指数与纹理特征集成监测南方玉米锈病严重度。《计算机与电子在农业中的应用》","2026-08-31T00:00:00Z",69,{"impact":178,"substance":86,"depth":17,"authority":19,"freshness":179,"relevant":21,"comment":180},15,2,"利用无人机影像与纹理特征监测玉米南方锈病，方法新颖，数据详实，对精准农业有参考价值。",[182],{"name":10,"url":172},[26,27,28,29,184],"病害监测",[186,187],"农业人工智能 智慧农业 病害监测 玉米","农业人工智能 智慧农业","农业人工智能智慧农业病害监测玉米-1276","10.1016\u002Fj.compag.2026.112336",{"doi":189,"openalex_id":191,"authors":192,"venue":10,"cited_by_count":35,"oa_url":9,"card":227,"direction":69,"ingested_from":71},"W7204748765",[193,195,197,200,203,206,208,210,212,214,216,218,220,222,225,226],{"name":194,"orcid":9},"Yanan Wei",{"name":196,"orcid":9},"Qing Li",{"name":198,"orcid":199},"Jia Yin","https:\u002F\u002Forcid.org\u002F0000-0002-6242-1092",{"name":201,"orcid":202},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":204,"orcid":205},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":207,"orcid":9},"Yongyuan Gao",{"name":209,"orcid":9},"Weijian Yu",{"name":211,"orcid":9},"Zhixiong Li",{"name":213,"orcid":9},"Yachang He",{"name":215,"orcid":9},"Guanyu Qiao",{"name":217,"orcid":9},"Yu Zhang",{"name":219,"orcid":9},"Carlos Camino",{"name":221,"orcid":9},"Jun Liu",{"name":223,"orcid":224},"Tianyi Wang","https:\u002F\u002Forcid.org\u002F0000-0003-3032-1246",{"name":47,"orcid":9},{"name":49,"orcid":50},{"tldr":228,"method":229,"finding":230,"direction":69,"opportunity":231},"结合无人机增强病害检测指数与纹理特征，监测南方玉米锈病严重度。","无人机影像，增强病害检测指数与纹理特征融合，机器学习建模。","融合指数与纹理特征可提高玉米锈病严重度监测精度。","可探索多时相、多传感器融合及深度学习，提升病害早期监测与预测能力。","2026-09-01T23:30:01.368713Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":246,"tags":248,"search_phrases":251,"slug":254,"view_count":35,"doi":255,"paper":256,"created_at":271},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems",72,{"impact":88,"substance":242,"depth":243,"authority":244,"freshness":20,"relevant":21,"comment":245},21,17,13,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[247],{"name":239,"url":236},[26,27,249,250,29],"水稻","多模态融合",[252,253],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":255,"openalex_id":257,"authors":258,"venue":239,"cited_by_count":35,"oa_url":236,"card":265,"direction":269,"ingested_from":71},"W7213619014",[259,261,263],{"name":260,"orcid":9},"Nurfadhilah Mardianti Andini",{"name":262,"orcid":9},"Mike Yuliana",{"name":264,"orcid":9},"Moch. Zen Samsono Hadi",{"tldr":266,"method":267,"finding":268,"direction":269,"opportunity":270},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","智慧农业 \u002F 农业物联网","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","2026-09-20T23:30:08.419613Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":9,"content":9,"source_name":277,"source_url":9,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":283,"tags":285,"search_phrases":288,"slug":291,"view_count":35,"doi":9,"paper":292,"created_at":300},3000,"Plant-GeoAT：几何感知3D植物点云器官身份解析用于器官级表型分析","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1809","四川农业大学吴俊杰等提出Plant-GeoAT几何感知解析网络，在RGB融合前编码局部相对-XYZ邻域并将空间邻域与特征空间关系耦合用于密集点预测。在自建油菜数据集、基于图像的大豆数据集、激光扫描Pheno4D玉米和番茄数据集上评估，5个随机种子下mIoU分别达92.26±0.28%、82.50±0.24%、99.74±0.05%、94.75±0.15%，玉米Stem IoU达99.57±0.09%。","MDPI Agronomy 16(18):1809","2026-09-15T00:00:00Z",78,{"impact":17,"substance":281,"depth":17,"authority":244,"freshness":74,"relevant":21,"comment":282},23,"方法新颖、多作物多数据集验证且精度数据扎实，属器官级表型分析细分领域的重要技术进展，值得进入每日精选。",[284],{"name":277,"url":275},[26,27,286,30,287],"油菜","三维点云",[289,290],"四川农业大学 植物点云 器官识别","Plant-GeoAT 表型分析","四川农业大学植物点云器官识别-3000",{"doi":9,"openalex_id":9,"authors":293,"venue":9,"cited_by_count":35,"oa_url":9,"card":294,"direction":69,"ingested_from":299},[],{"tldr":295,"method":296,"finding":297,"direction":69,"opportunity":298},"提出几何感知网络Plant-GeoAT，实现3D植物点云器官身份解析与器官级表型分析。","编码局部相对XYZ邻域并耦合空间与特征空间关系，在油菜、大豆、玉米、番茄点云数据","五个数据集mIoU最高达99.74%，玉米茎IoU达99.57%，验证了几何感知对器官分割的有效性。","可探索跨物种、跨传感器的轻量化几何感知模型，并推动器官级表型与基因型关联分析。","agent","2026-09-20T00:03:08.094512Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":9,"content":9,"source_name":306,"source_url":9,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"search_phrases":316,"slug":319,"view_count":35,"doi":9,"paper":320,"created_at":327},2999,"基于改进DeepLabv3+的高标准农田田间道路提取与结构指标量化框架","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1986","沈阳农业大学刘永生等开发了基于MobileNetV2改进DeepLabv3+的高标准农田田间道路提取轻量化框架，集成Normalization-based Attention Module与Content-Aware ReAssembly of FEatures。三次随机种子训练平均mIoU 93.34%、mPA 96.75%、精度98.90%，模型参数6.14M、推理速度17.04 FPS；沥青、混凝土、砾石道路宽度预测R²分别为0.650、0.486、0.662，宽度MAE 0.130\u002F0.140\u002F0.100 m。第二验证区域连通性指数从0.4682提升至0.4795，支持高标准农田田间道路高效、可量化、可追溯的验收检查。","MDPI Agriculture 16(18):1986","2026-09-16T00:00:00Z",77,{"impact":87,"substance":18,"depth":17,"authority":244,"freshness":127,"relevant":21,"comment":310},"方法有创新、指标详实，对高标准农田道路验收有实用价值，但属细分技术论文，影响面有限。",[312],{"name":306,"url":304},[26,27,314,29,315],"高标准农田","田间道路",[317,318],"沈阳农业大学 高标准农田 道路提取","DeepLabv3 田间道路 遥感","沈阳农业大学高标准农田道路提取-2999",{"doi":9,"openalex_id":9,"authors":321,"venue":9,"cited_by_count":35,"oa_url":9,"card":322,"direction":69,"ingested_from":299},[],{"tldr":323,"method":324,"finding":325,"direction":69,"opportunity":326},"提出改进DeepLabv3+轻量框架，提取高标准农田田间道路并量化结构指标。","MobileNetV2+NAM+CARAFE改进DeepLabv3+，多区域遥感","mIoU 93.34%，道路宽度预测R²最高0.662，连通性指数提升至0.4795。","可拓展至多作物、多地形道路提取，并结合时序遥感实现道路损毁动态监测。","2026-09-20T00:03:08.023498Z"]