The advancement of modern agriculture is heavily dependent on accurate weed detection, which contributes to efficient resource utilization and increased crop yield. Traditional methods, however, often need more accuracy and efficiency. This paper presents WeedFocusNet, an innovative approach that leverages attention-driven ResNet152V2 transfer learning addresses these challenges. This approach enhances model generalization and focuses on critical features for weed identification, thereby overcoming the limitations of existing methods. The objective is to develop a model that enhances weed detection accuracy and optimizes computational efficiency. WeedFocusNet, a novel deep-learning model, performs weed detection better by employing attention-driven transfer learning based on the ResNet152V2 architecture. The model integrates an attention module, concentrating its predictions on the most significant image features. Evaluated on a dataset of weed and crop images, WeedFocusNet achieved an accuracy of 99.28%, significantly outperforming previous methods and models, such as MobileNetV2, ResNet50, and custom CNN models, in terms of accuracy, time complexity, and memory usage, despite its larger memory footprint. These results emphasize the transformative potential of WeedFocusNet as a powerful approach for automating weed detection in agricultural fields
