Grape leaf diseases pose a significant threat to vineyard productivity and quality, underscoring the importance of early and accurate diagnosis. This paper introduces AFC-Net, an Attention-Guided Fusion Convolutional Network designed to enhance classification performance through the integration of multiple backbone architectures. Our model combines ResNet50, EfficientNetB0, and MobileNetV2 to extract various and complementary features from input images. These features are fused using an attention-based fusion mechanism that dynamically learns the contribution weights ( \(\alpha _1\) , \(\alpha _2\) , \(\alpha _3\) ) for each backbone, allowing the model to emphasize the most informative representations. We evaluated our approach in the Grape disease dataset, which includes four classes: Black Rot, ESCA, Healthy, and Leaf Blight. AFC-Net achieves a classification accuracy of 99.83%, with a macro-averaged F1-score of 99.84%. Notably, the model attains perfect F1-scores for both Healthy and Leaf Blight classes, demonstrating exceptional robustness across categories. To enhance model transparency, we employ Grad-CAM to visualize class-discriminative regions, providing valuable insights into the model’s decision-making process. These results highlight AFC-Net as a promising solution for real-world grape disease detection, contributing to the advancement of precision agriculture.