融合CNN/ViT/GNN与GAN增强，实现高精度智能杂草检测
A Hybrid CNN-ViT-GNN Framework with GAN-Based Augmentation for Intelligent Weed Detection in Precision Agriculture
- 结合卷积、视觉变换器与图神经网络，捕捉局部、全局与关系特征
- 在多数据集上达到99.33%的准确率、精确率、召回率和F1分数
- 适合农业自动化场景，可部署于边缘设备实现实时检测
杂草检测是精准农业的关键环节，准确识别物种可实现选择性除草，助力可持续作物管理。本文提出一种混合深度学习框架，融合卷积神经网络（CNN）、视觉变换器（ViTs）与图神经网络（GNN），提升对多种田间条件的鲁棒性。采用生成对抗网络（GAN）进行数据增强，平衡类别分布，提升模型泛化能力。此外，引入自监督对比预训练方法，从有限标注数据中学习更丰富特征。实验在多基准数据集上取得99.33%的准确率、精确率、召回率和F1分数。该模型架构支持局部、全局与关系特征表示，具备高可解释性与适应性。实际应用中，框架可实现实时高效部署于边缘设备，减少除草剂依赖，提供可扩展、可持续的精准农业解决方案。
原文摘要 · Abstract (English)
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to build robustness to multiple field conditions. A Generative Adversarial Network (GAN)-based augmentation method was imposed to balance class distributions and better generalize the model. Further, a self-supervised contrastive pre-training method helps to learn more features from limited annotated data. Experimental results yield superior results with 99.33% accuracy, precision, recall, and F1-score on multi-benchmark datasets. The proposed model architecture enables local, global, and relational feature representations and offers high interpretability and adaptability. Practically, the framework allows real-time, efficient deployment to edge devices for automated weed detecting, reducing over-reliance on herbicides and providing scalable, sustainable precision-farming options.
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