Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting
提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。
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农业人工智能 智慧农业 遥感
同时包含 农业人工智能、智慧农业、遥感 的农业资讯，共 107 条。
Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions
推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。
Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision
综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。
Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models
构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。
IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION
综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。
Modeling the global potential of smart farming innovations
全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。
BTSR-Net: Boundary–topology guided structural refinement for high-resolution remote sensing image segmentation
提出BTSR-Net，用边界与拓扑引导提升高分辨率遥感影像分割的结构连续性。
A lightweight hybrid network with inverted residuals and state space model for remote sensing image segmentation
提出IRUM-Net混合网络，结合Mamba与CNN实现遥感图像轻量高精度语义分割。
Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal·10月15日 · 周四49 分 论文数字农业与前沿技术智慧农业 / 农业物联网阅读 1
QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI
研究将智慧农业技术融入农业高校专业课程教学的方法论。
Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework
提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。
Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems
提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。
Barley head detection using UAV imagery and YOLOv10
Barley head detection using UAV imagery and YOLOv10。Precision Agriculture
Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain
融合Sentinel-1 SAR与Sentinel-2光学特征，在印尼热带山地实现93%精度的作物类
Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture
综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。
Applications of Neutrosophic Topology in the Indian Context: A Review and Emerging Research Directions
综述中智拓扑在印度多领域的应用潜力，并指出理论与实证数据脱节的局限。
A privacy-preserving hyper-spectral remote sensing image analysis framework based on matrix outsourcing computation
提出基于矩阵外包计算的隐私保护框架，实现高光谱遥感图像安全高效分析。
Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece
用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。
Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice
Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square
A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification
提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用
Zenodo (CERN European Organization for Nuclear Research)·10月8日 · 周四64 分 论文物联网传感与边缘计算智慧农业 / 农业物联网阅读 1
INTEGRATING "SMART AGRICULTURE" TECHNOLOGIES INTO THE EDUCATIONAL PROCESS IN AGRICULTURAL EDUCATION
探讨将智慧农业技术融入高等农业教育，作为学生解决实际农艺问题的教学工具。
Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy
提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。
HyBEAR: a hyperspectral benchmark for bare soil detection
提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。
Ensemble Machine Learning for Farm-Level Rainfall Forecasting Based on Satellite Observations
基于卫星观测，用集成机器学习为农场尺度做降雨预报。
Improving early season crop type mapping using machine learning algorithms with combined SAR and optical time series
融合Sentinel-1 SAR与Sentinel-2光学时序，用集成机器学习实现印度早季作物类型高
Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger
结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。
Road surface material classification from unmanned aerial vehicle red, green, blue and multispectral imagery using spectral features and ensemble machine learning model
用无人机RGB与多光谱影像结合集成机器学习，分类路面材料并验证多光谱与纹理特征的增益。
Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review
系统综述2018-2026年196项AI用于土壤特性与农业的研究。
International Journal of Emerging Trends in Engineering and Development·10月2日 · 周五61 分 论文农业AI与大模型农业遥感与作物表型2 个信源同时报道 阅读 2
Comparative Performance of Machine Learning and Deep Learning Algorithms for Agricultural Land Mapping in Anambra State, Nigeria
比较RF、SVM、ANN、CNN在尼日利亚农业用地制图中的表现
A Feature Engineering Framework for High-Precision Land Use and Land Cover Classification Using Remote Sensing Images
提出EODFE-HPLULC框架，结合YOLOv12、EfficientNet-B5和深度信念网络实
An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)
用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。
Aerial Phenotyping of Coffee Flowering Using Deep Learning for Early Yield Prediction
用无人机RGB影像和深度学习对咖啡开花分级，实现早期产量预测。
A new vegetation–thermal integrated approach (NNL-SM) for high-resolution soil moisture estimation
提出NNL-SM模型，融合NDMI、NDVI和LST将SMAP土壤水分从9km降尺度至1km。
Latent-space feature reconstruction-enhanced semantic segmentation for agricultural field roads under visual degradation
提出潜空间特征重建增强的语义分割方法，用于视觉退化下的农田道路提取。
PineWilt-MultiSeg: Joint spatial–spectral learning enables precise detection of pine wilt disease
提出PineWilt-MultiSeg模型，联合空谱学习实现松材线虫病精准检测。
A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data
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。
UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress
用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。
Agrivlm: integrating vision-language models into real-time remote sensing for smart agriculture
Agrivlm: integrating vision-language models into real-time remote sensing for smart agriculture。Wireless Networks
Raspberry Pi Lightweight Deep Learning Models Deployed for Corn Growth Stage Classification Using PhenoCam Images
用PhenoCam图像训练轻量深度学习模型，在树莓派上实时分类玉米8个生长阶段。
International Journal of Systems Assurance Engineering and Management·9月24日 · 周四62 分 论文农业人工智能与决策模型2 个信源同时报道 阅读 3
Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence
利用人工智能进行土壤制图和肥料优化，以支持精准农业。
Journal of Geography Environment and Earth Science International·9月24日 · 周四66 分 论文GIS与空间分析农业遥感与作物表型阅读 5
Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies
综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。
Zenodo (CERN European Organization for Nuclear Research)·9月30日 · 周三68 分 论文农业AI与大模型智慧农业 / 农业物联网2 个信源同时报道 阅读 4
AI For Sustainable Development Opportunities & Innovation
综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。
Diag-STFN：全球收获前作物产量预测的诊断时空多模态融合网络——覆盖38国玉米29国小麦（Ecological Informatics 2026）
提出诊断式时空多模态融合网络Diag-STFN，实现全球收获前玉米小麦产量预测。
Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province
基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。
农机化研究2026(11)：基于多源感知与语义分割的果树行识别与路径跟踪控制——郑州工业应用技术学院许洋洋等 DeepLabv3+ F1达0.94
提出融合无人机多光谱与语义分割的果树行识别与路径跟踪方法，实现植保机自主导航。
Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）
系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。
International Journal of Applied Earth Observation and Geoinformation·9月21日 · 周一80 分 论文遥感与农情监测农业遥感与作物表型阅读 6
Relaxing the clear-sky assumption: Cloud-tolerant spatiotemporal fusion via mask-guided feature modulation and temporal-memory collaboration
提出CloudSTF框架，利用含云影像实现高保真时空融合，突破晴空假设。
Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery
提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。
Time Series Extrinsic Regression for Smart Agriculture: Field‐Level Crop Yield Prediction From Multisource Agricultural Time Series
将田块级作物产量预测建模为时间序列外回归，比较多源农业时间序列上的神经与非神经方法。
AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment
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。
Explainable spectral–image fusion multi-task learning for maize canopy biochemical and structural trait retrieval
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。
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
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。
Machine learning and remote sensing for smallholder precision agriculture in Ethiopia
系统综述埃塞俄比亚小农精准农业中机器学习和遥感的应用鸿沟与出路。
AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol
提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。
An information-driven air–ground collaborative framework for UAV-based tillage defect identification and re-tillage path optimization
提出空地协同框架，用无人机识别耕整缺陷并优化补耕路径。
Evaluating Mesh Reconstruction Methods for Crop Phenotyping
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。
Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction
用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。
Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning
综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。
Application of Remote Sensing and Machine Learning in Sustainable Agriculture
综述遥感与机器学习在可持续农业中的应用现状与前景。
Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture
用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。
Estimation of crop canopy nitrogen content using deep transfer learning with PROSAIL-PRO model and UAV hyperspectral imagery
融合PROSAIL-PRO物理模型与深度迁移学习，用无人机高光谱估算作物冠层氮含量。
Transformer-Fused Hybrid Descriptors for Remote Sensing Scene Classification, Integrating Texture-Morphology Features with EfficientNetV2 Semantic Embeddings on MLRSNet
提出Transformer融合手工纹理形态特征与EfficientNetV2深度特征的遥感场景分类模
Zenodo (CERN European Organization for Nuclear Research)·9月30日 · 周三72 分 论文农业AI与大模型数字乡村与农业信息化2 个信源同时报道 阅读 4
Empowering India's Digital Future through Artificial Intelligence in Geography
本文探讨地理空间人工智能（GeoAI）如何推动印度数字治理与可持续发展。
Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop
用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。
Zenodo (CERN European Organization for Nuclear Research)·9月30日 · 周三46 分 论文农业AI与大模型智慧农业 / 农业物联网2 个信源同时报道 阅读 3
Uses of Artificial Intelligence In Geography
综述人工智能在地理学中的应用，涵盖GIS、遥感、城市规划、农业与环境监测等。
UAV-based rice nitrogen status monitoring using machine learning and deep learning
UAV-based rice nitrogen status monitoring using machine learning and deep learning。Precision Agriculture
Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet
提出增强NightNet实现无人机夜间柑橘果实轻量检测。
Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts
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。
《基于遥感的农作物产量估算综述:机器学习技术及环境、算法和硬件限制》
系统综述遥感估产方法，分类传感器、平台、建模与机器学习，指出深度学习性能最优。
A Robust and Efficient Scheme for Real-Time SAR Speckle Noise Reduction based on Attentional Self-Supervised Contrastive Deep Learning
提出RAST-Net，用注意力自监督对比深度学习实现SAR图像实时去斑。
Crop Simulation Model: A Digital Tool for Sustainable Water Management in Agriculture
综述作物模拟模型在估算作物需水与灌溉管理中的应用及改进方向。
Multi-grained image-text retrieval in farmland remote sensing via multimodal large language models
提出农田遥感图文跨模态检索任务，构建数据集并提出FarmMGR框架实现语言驱动检索。
Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji·9月10日 · 周四66 分 论文农业AI与大模型农业遥感与作物表型阅读 3
A Hybrid Transfer Learning Framework for Seasonal Classification of Satellite Images
提出混合迁移学习框架，用预训练模型提取特征并结合机器学习分类器实现卫星图像季节分类。
An Optimized Temporal Attention-based Deep Learning Framwork for Accurate Crop Yield Prediction from Multi-source Agricultural Data
提出TA-ABC-Bi-LSTM模型，用多源农业数据精准预测水稻产量。
Machine Learning-Based Prediction of Fall Armyworm (Spodoptera frugiperda) Outbreaks in Maize Production Systems of Northern Nigeria Using Climate and Remote Sensing Data
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。
Quantifying Thermoregulation in Niche Construction of Apis mellifera as an Extended Phenotype to Map the Selection Pressure of Volatile Climate Using Hybrid CNN Bi-LSTM Architecture and Remote Sensing
Quantifying Thermoregulation in Niche Construction of Apis mellifera as an Extended Phenotype to Map the Selection Pressure of Volatile Climate Using Hybrid CNN Bi-LSTM Architecture and Remote Sensing。Research Square
AgroInsect: A hybrid AI-driven edge-cloud architecture for precision pest detection and geostatistical interpolation in agriculture
提出AgroInsect边缘-云混合AI架构，实现农田害虫精准检测与地统计插值。
Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales
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。
Accelerated Computation of Vegetation Indices on Heterogeneous Platforms Using OpenMP, CUDA, and OpenCL for Sustainable Agricultural Monitoring
评估异构平台加速NDVI和NDWI计算，实现可持续农业监测。
An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data
提出BO-TCBDA深度学习框架，融合多源遥感数据估算冬小麦产量，兼具高精度与可解释性。
农业大模型在田间地头的落地实践：伏羲大脑"佳家农"智慧农服平台案例研究
介绍伏羲大脑农业大模型在佳木斯农高区的落地实践，通过手机和卫星数据提供农事指导，服务超5.6万农户。
Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning
通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。
活力中国调研行｜农户有了数字新农具——黑龙江佳木斯佳家农智慧农服平台服务超6万户
新华社记者随活力中国调研行采访团走进黑龙江佳木斯国家农业高新技术产业示范区，佳家农智慧农服平台由佳木斯国家农高区联合中国科学院计算技术研究所打造，依托伏羲大脑农业大模型汇集卫星遥感、气象、土壤、作物长势、病虫害等多源数据；目前已服务农户超过6万户、覆盖耕地超过700万亩。
SMFS-RF: a knowledge-guided machine-learning method for crop phenology extraction from fine-resolution vegetation index data
SMFS-RF: a knowledge-guided machine-learning method for crop phenology extraction from fine-resolution vegetation index data。Remote Sensing of Environment
Integrating Geospatial Information and Artificial Intelligence for Irrigation Planning in Kwali Area Council, Federal Capital Territory, Nigeria
结合地理空间与AI评估尼日利亚Wako灌区适宜性，划分灌溉分区。
CAFF-Net for coupled eco-health prediction of urban green space and water body with multi-source remote sensing
提出CAFF-Net，用多源遥感数据耦合预测城市绿地和水体生态健康。
VGG21 Deep Learning + Drone Multispectral Imagery Detects Wheat Waterlogging Stress and Silicon Fertilizer Effects
用无人机多光谱和VGG21深度学习检测小麦涝害并评估硅肥效果。
Advanced Remote Sensing and AI Techniques in Agriculture and Forestry
综述遥感和AI在农林领域的先进技术及其应用。
Super-Resolution-Assisted Farmland Boundary Extraction from Medium-Resolution Satellite Image: A Real-ESRGAN and YOLO Segmentation Framework
提出结合Real-ESRGAN超分辨率和YOLO分割的农田边界提取框架，提升中分辨率卫星影像识别精度
International Research Journal of Modernization in Engineering Technology and Science·9月3日 · 周四73 分 论文物联网传感与边缘计算智慧农业 / 农业物联网阅读 1
AGRITWIN AI: A DIGITAL TWIN AND MULTIMODAL EXPLAINABLE AI FRAMEWORK FOR PRECISION AGRICULTURE USING SATELLITE IMAGERY, IOT SENSORS, WEATHER INTELLIGENCE, LARGE LANGUAGE MODELS, AND RETRIEVAL-AUGMENTED GENERATION
提出 AgriTwin AI 框架，融合数字孪生与多模态可解释 AI，弥合农业数据与认知鸿沟。
Continual Learning Model Evaluation for Hyperspectral-Based Tomato Bacterial Spot Detection in Greenhouse
评估持续学习框架用于温室高光谱番茄细菌性斑点病冠层检测。
A physics-guided hybrid framework for peanut nitrogen monitoring (Computers and Electronics in Agriculture)
提出物理-数据融合框架POSNH，用于花生冠层氮素含量遥感监测，降低样本依赖并提高泛化能力。
Zenodo (CERN European Organization for Nuclear Research)·9月2日 · 周三67 分 论文农业AI与大模型农业遥感与作物表型2 个信源同时报道 阅读 3
Deep Learning Based Semantic Segmentation of Satellite Imagery for Land Cover Classification
综述深度学习用于卫星影像土地覆盖分类的语义分割方法，并分析挑战与未来方向。
MetaMamba: Meta-learning with mamba for few-shot vegetation species classification using UAV-based hyperspectral imagery in desert rangeland
提出MetaMamba方法，结合元学习与Mamba，实现荒漠草原无人机高光谱影像少样本植被物种分类。
An Improved Transformer-KAN Model for Soybean Mapping Based on Multi-Temporal Remote Sensing Data
提出改进的Transformer-KAN模型，基于多时相遥感数据实现高精度、可迁移的大豆种植区制图。
A dictionary-based method for crop yield prediction using satellite remote sensing data
提出基于HYDRA字典方法的机器学习框架，结合物候特征预测玉米产量。
Hyperspectral Imaging for Disease Detection in Wheat Crops: A Comprehensive Survey
综述高光谱成像在小麦病害检测中的应用，涵盖方法、挑战与未来方向。
CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests
提出CFAEVM-UNet模型，用于无人机图像中密集小害虫的精确分割。
Diag-STFN：用于全球收获前作物产量预测的诊断时空多模态融合网络
提出Diag-STFN网络，用于全球收获前玉米和小麦产量预测，在CY-Bench基准上取得最优性能。
International Journal of Engineering Technology Research & Management·12月21日 · 周一30 分 论文遥感与农情监测农业人工智能与决策模型2 个信源同时报道 阅读 2
PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS
利用无人机多光谱成像与机器学习检测作物胁迫并优化产量。
PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery
提出 PhenoStream 网络基础设施，自动化航空影像作物性状提取全流程。