A Hybrid CNN-ViT-GNN Framework with GAN-Based Augmentation for Intelligent Weed Detection in Precision Agriculture

• URL: http://arxiv.org/abs/2511.15535v1
• Date: Wed, 19 Nov 2025 15:32:08 GMT
• Title: A Hybrid CNN-ViT-GNN Framework with GAN-Based Augmentation for Intelligent Weed Detection in Precision Agriculture
• Authors: Pandiyaraju V, Abishek Karthik, Sreya Mynampati, Poovarasan L, D. Saraswathi,
• Abstract summary: This paper proposes a hybrid deep learning framework recipe for weed detection.<n>A Generative Adversarial Network (GAN)-based augmentation method was imposed to balance class robustness and better generalize the model.<n> Experimental results yield superior results with 99.33% accuracy, precision, recall, and F1-score on multi-benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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