[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4272":3,"related-4272":92},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":86,"slug":89,"view_count":90,"doi":8,"paper":8,"created_at":91},4272,"河南农科院农业信息技术研究所多项智慧农业成果亮相科企融合大会","https:\u002F\u002Fnjxxs.hnagri.org.cn\u002Farticle-114159.html","河南省农科院第二届科企融合发展大会在郑州开幕，农业信息技术研究所携移动智控水肥一体机、花生智能揉搓脱壳机、多通道土壤养分联合测速分析仪、农作物遥感监测无人机、农业害虫智能监测预警机器人、田间智能化移动作业平台、空天地农情监测平台、麦穗计数智检平台等智慧装备集中亮相。",null,"2026年9月20日，以“科技创新护粮安·产业创新强中原”为主题的黄淮海粮油作物大面积单产提升战略研讨暨河南省农科院第二届科企融合发展大会在郑州隆重开幕。会上，我所携多项智慧农业科技创新成果亮相农科系统科技成果展，以智慧农业赋能中原粮仓提质增效。\n\n本次成果展中，我所采用宣传展板、实物展品、多媒体演示相结合的形式，集中展示了农业信息技术创新与成果转化的阶段性成果，主要包括“移动智控水肥一体机”、“花生智能揉搓脱壳机”、“多通道土壤养分联合测速分析仪”、“农作物遥感监测无人机”、“农业害虫智能监测预警机器人”、“田间智能化移动作业平台”、“空天地农情监测平台”、“麦穗计数智检平台”等一系列智能装备、检测仪器与数字化系统。本次参展成果紧扣农田精准监测、智能管控、产后加工等生产需求，覆盖粮食生产全流程关键环节，破解传统农业在智能化、精准化生产中的痛点，为黄淮海粮油作物规模化、标准化、智能化生产提供坚实技术支撑。在乡村振兴示范村镇展区，展示我所牵头打造的乡村振兴示范村水牛赵村稻渔共生特色成果，演示了稻米溯源系统。除科技创新成果展示外，我所同步展出《河南农业科学》、《种业导刊》两大学术期刊实物资料，充分发挥期刊学术载体作用。","河南农科院 2026-09-20","2026-09-20T00:00:00Z","报道",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},16,14,12,2,1,"省级科研机构智慧农业装备集中亮相，成果清单具体但属会议通稿，时效已过两周，可作主题聚合素材而非每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","智能农机","成果转化","水肥一体化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,51,58,65,71,76,81],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},"I1",9,true,[26],[],[26],{"code":32,"score":52,"supported":47,"title":53,"abstract":54,"tags":57},7,[],[55,56],"遥感","农情监测",[27],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":64},"A4",3,[],[63],"花生",[],{"code":66,"score":60,"supported":47,"title":67,"abstract":68,"tags":70},"H2",[],[69],"土壤",[],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"F3",[],[],[30],{"code":77,"score":21,"supported":14,"title":78,"abstract":79,"tags":80},"F5",[],[],[28],{"code":82,"score":21,"supported":14,"title":83,"abstract":84,"tags":85},"G1",[],[],[29],[87,88],"河南农科院 智慧农业 成果","黄淮海 粮油作物 单产提升","河南农科院智慧农业成果-4272",0,"2026-10-06T00:05:02.782086Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,199,262,313,386,463],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":8,"source_name":101,"source_url":98,"published_at":102,"category":103,"cover_url":8,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":121,"classification":129,"search_phrases":165,"slug":168,"view_count":90,"doi":169,"paper":170,"created_at":198},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z","论文",80,{"impact":17,"substance":106,"depth":107,"authority":18,"freshness":13,"relevant":21,"comment":108},22,18,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[110],{"name":101,"url":98},[26,112,27,113,114],"农业人工智能","氮素监测","紫花苜蓿",{"code":116,"slug":117,"name":118,"path":119,"parent_code":120},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[122,123],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":39,"locked":14,"version":40,"method":41,"evidence":130},{"candidates":131,"agricultural_evidence":47},[132,138,145,151,161],{"code":32,"score":107,"supported":47,"title":133,"abstract":135,"tags":137},[134],"hyperspectral",[55,136,134],"remote 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Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":185,"orcid":8},"Yongjia Hu",{"name":187,"orcid":188},"Qisheng 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小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":8,"openalex_id":8,"authors":306,"venue":8,"cited_by_count":90,"oa_url":8,"card":307,"direction":195,"ingested_from":260},[],{"tldr":308,"method":309,"finding":310,"direction":195,"opportunity":311},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":318,"content":8,"source_name":319,"source_url":316,"published_at":268,"category":103,"cover_url":8,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":324,"tags":326,"primary_topic":329,"secondary_topics":330,"classification":335,"search_phrases":358,"slug":361,"view_count":21,"doi":362,"paper":363,"created_at":385},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems",69,{"impact":19,"substance":322,"depth":17,"authority":19,"freshness":46,"relevant":21,"comment":323},20,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[325],{"name":319,"url":316},[26,27,155,327,328],"油棕","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[331],{"code":152,"slug":332,"name":333,"path":334,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":336},{"candidates":337,"agricultural_evidence":47},[338,342,347,354],{"code":32,"score":139,"supported":47,"title":339,"abstract":340,"tags":341},[136],[55,136],[27],{"code":152,"score":19,"supported":47,"title":343,"abstract":344,"tags":346},[158],[345],"YOLO",[155],{"code":348,"score":93,"supported":47,"title":349,"abstract":350,"tags":353},"B5",[],[351,352],"蘑菇","mushroom",[],{"code":45,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[26],[359,360],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":362,"openalex_id":364,"authors":365,"venue":319,"cited_by_count":90,"oa_url":316,"card":380,"direction":195,"ingested_from":197},"W7222626135",[366,369,372,374,377],{"name":367,"orcid":368},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":370,"orcid":371},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":373,"orcid":8},"Jin Yuan Chan",{"name":375,"orcid":376},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":378,"orcid":379},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":381,"method":382,"finding":383,"direction":195,"opportunity":384},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":8,"source_name":392,"source_url":389,"published_at":393,"category":103,"cover_url":8,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":410,"search_phrases":434,"slug":437,"view_count":90,"doi":438,"paper":439,"created_at":462},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":17,"substance":271,"depth":272,"authority":396,"freshness":240,"relevant":21,"comment":397},13,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[399],{"name":392,"url":389},[26,27,401,155,402],"可解释AI","土壤盐渍化",{"code":152,"slug":332,"name":333,"path":334,"parent_code":36},[405,406],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":66,"slug":407,"name":408,"path":409,"parent_code":226},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":39,"locked":14,"version":40,"method":41,"evidence":411},{"candidates":412,"agricultural_evidence":47},[413,417,421,425,430],{"code":152,"score":17,"supported":47,"title":414,"abstract":415,"tags":416},[158],[155,158],[401,155],{"code":32,"score":139,"supported":47,"title":418,"abstract":419,"tags":420},[136],[55,136],[27],{"code":66,"score":18,"supported":47,"title":422,"abstract":423,"tags":424},[407],[69,407],[],{"code":222,"score":60,"supported":47,"title":426,"abstract":427,"tags":429},[],[428],"干旱",[],{"code":45,"score":21,"supported":14,"title":431,"abstract":432,"tags":433},[],[],[26],[435,436],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":438,"openalex_id":440,"authors":441,"venue":392,"cited_by_count":90,"oa_url":389,"card":457,"direction":195,"ingested_from":197},"W7221715654",[442,445,448,450,453,455],{"name":443,"orcid":444},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":446,"orcid":447},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":449,"orcid":8},"Junchi Ma",{"name":451,"orcid":452},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":454,"orcid":8},"Jiahao Qi",{"name":456,"orcid":8},"Yuan Jing",{"tldr":458,"method":459,"finding":460,"direction":195,"opportunity":461},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":468,"content":8,"source_name":469,"source_url":466,"published_at":470,"category":103,"cover_url":8,"hotness":13,"is_selected":14,"score":104,"score_detail":471,"sources":474,"tags":476,"primary_topic":479,"secondary_topics":480,"classification":481,"search_phrases":512,"slug":515,"view_count":21,"doi":516,"paper":517,"created_at":535},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",{"impact":107,"substance":106,"depth":472,"authority":396,"freshness":240,"relevant":21,"comment":473},19,"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[475],{"name":469,"url":466},[26,112,27,477,478],"数字孪生","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":482},{"candidates":483,"agricultural_evidence":47},[484,489,494,499,506],{"code":32,"score":107,"supported":47,"title":485,"abstract":486,"tags":488},[136],[55,136,487],"multispectral",[27],{"code":45,"score":19,"supported":47,"title":490,"abstract":491,"tags":493},[],[26,478,477,492],"digital twin",[477,26,478],{"code":152,"score":52,"supported":47,"title":495,"abstract":496,"tags":498},[],[156,497],"artificial intelligence",[112],{"code":500,"score":93,"supported":47,"title":501,"abstract":502,"tags":505},"I5",[],[503,504],"物联网","IoT",[],{"code":507,"score":60,"supported":47,"title":508,"abstract":509,"tags":511},"B2",[],[510],"葡萄",[],[513,514],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":516,"openalex_id":518,"authors":519,"venue":469,"cited_by_count":90,"oa_url":466,"card":529,"direction":534,"ingested_from":197},"W7221178461",[520,523,526],{"name":521,"orcid":522},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":524,"orcid":525},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":527,"orcid":528},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",{"tldr":530,"method":531,"finding":532,"direction":195,"opportunity":533},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:07.913557Z"]