Wild boar collision data and satellite computer vision refine habitat suitability mapping across France
融合野猪碰撞数据与卫星视觉模型，预测法国野猪栖息地适宜性及其时空驱动因素。
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A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management
提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。
International Journal for Research in Applied Science and Engineering Technology·10月13日 · 周二52 分 论文物联网传感与边缘计算智慧农业 / 农业物联网阅读 3
IoT-Enabled Autonomous Agriculture Monitoring Rover for Smart Farming: A Comprehensive Literature Review
综述了物联网赋能的自主农业监测机器人，分析架构、传感、导航与AI集成，指出低成本模块化系统的研究空白
YOLO-Based Leaf Disease Detection in Smart Agriculture: Evolution, Task Adaptation, and Deployment Challenges
综述了基于YOLO的叶片病害检测的演进、任务适配与部署挑战。
From Handcrafted Descriptors to Deep Embeddings: A Comparative Study for Regional Hazelnut Image Classification
比较手工特征、深度迁移学习和量子兼容分类器对三个地区榛子图像的分类性能。
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。
Hybrid transformer and CNN for hyperspectral image classification with spectral band selection
提出结合波段选择与ViT-CNN混合框架的高光谱图像分类方法，提升小样本分类精度。
Hybrid Deep Feature Extraction and Ensemble Machine Learning for Potato Leaf Disease Classification in Uncontrolled Field Conditions
融合三种CNN深度特征与集成学习，实现田间马铃薯叶片七类病害分类。
Wearable Sensors for Smart Agriculture: From Sensing Mechanisms to Crop Phenotyping and Harvesting Applications
综述柔性可穿戴传感器在智慧农业中的传感机制、作物表型监测与采收应用。
Explainable deep learning framework for real-time insect pest detection in smart agriculture
提出可解释深度学习框架CPSA-RIPD-CSTGCN，实现智慧农业中害虫实时检测。
Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm
提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。
Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery
提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。
A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging
该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。
Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection
比较三种深度学习模型检测橙子病害，并用RISE提升可解释性。
A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model
提出联邦学习结合改进EfficientNet-Lite4的水稻叶片病害检测框架，兼顾隐私与精度。
JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE·10月12日 · 周一72 分 论文水资源与灌溉智慧农业 / 农业物联网阅读 0
CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS
基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。
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。
Adaptability of Lightweight AI Models for Embedded Deployment: A Case Study of an Agricultural Monitoring Robot
综述轻量化AI方法，并以农业监测机器人为例，提出相机+轻量YOLO+Jetson+单片机的嵌入式部署
Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis
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。
A global–local momentum contrastive framework with critical feature calibration for field plant disease identification in precision agriculture
提出GLC-Net，用全局-局部动量对比学习与关键特征校准提升田间植物病害识别。