Advanced Weed Detection in Agricultural Fields using Vision Transformers and Explainable AI Techniques - A. Mamatha (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Effective weed detection in agricultural fields is critical for optimizing crop yields and minimizing the use of herbicides. Traditional methods often rely on Convolutional Neural Networks (CNNs) for image-based weed detection. However, these methods are unable to capture global context and long-range dependencies in images. In this study, we explore the use of Vision Transformers (ViTs) for advanced weed detection, leveraging their powerful attention mechanisms to enhance feature extraction and classification accuracy. It can extract mimic feature from patch by patch with patch position. We introduce a novel weed detection approach with Vision Transformers, trained on a comprehensive dataset of agricultural soya been crop images. Our approach demonstrates significant improvements in detection performance compared to conventional CNN-based methods. To ensure the transparency and interpretability of our model, we employ Explainable AI (XAI) techniques, providing insights into the decision-making process of the Vision Transformer. Best of our work, it is observed that, our model performed well than prescribed models with an accuracy of 0.92.