AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning
提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。
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#杂草识别
Compressing Semantic Segmentation Models for Agricultural IoT: A Systematic Study of Pruning Trade-Offs for Crop–Weed Segmentation
系统评估六种语义分割模型在农业IoT上的压缩精度-效率权衡，重点分析剪枝策略。
A data-centric multi-modal ai framework for weed detection and classification in smart agriculture
A data-centric multi-modal ai framework for weed detection and classification in smart agriculture。Discover Applied Sciences
WeedNet: a foundation model-based global-to-local AI approach for weed species identification and classification
提出WeedNet基础模型，实现全球杂草物种识别与区域微调。
Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms
利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。
A comprehensive review of deep learning methods for weed classification in precision agriculture
综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。
Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review
对精准农业中深度学习杂草分类、检测与分割研究进行文献计量与综述分析。
Field-based deep learning classification of cotton and weeds for machine-vision-assisted intra-row weed management
用MobileNetV2和DenseNet169对田间棉花与杂草图像做二分类，验证机器视觉除草可行性
Comparative evaluation of classical machine learning and deep learning models for early weed detection in precision agriculture
对比经典机器学习与深度学习模型在精准农业早期杂草检测中的性能。
International Journal of Creative and Open Research in Engineering and Management·9月3日 · 周四56 分 论文物联网传感与边缘计算智慧农业 / 农业物联网阅读 2
Autonomous Multi-Crop Weeding and Nutrient Management
提出一种集成AI、IoT和嵌入式系统的自主多作物除草与养分管理机器，实现精准农业。
A deep learning perception framework for farm digital twins: weed species classification, semantic segmentation, and gradient-based visual interpretability
Anusree Manoj, Aiswarya S. Kumar, S. Remya, Somula Ramasubbareddy, Yong Yun Cho提出一种面向农业数字孪生系统的深度学习感知框架,涵盖杂草物种分类、语义分割和基于梯度的视觉可解释性分析。