Designing optimal Vision Transformer architecture using differential evolution for tomato leaf disease classification

Vision Transformers (ViTs) have recently demonstrated promising achievements in various computer vision tasks. However, designing a ViT model architecture requires high-level domain expertise, which can be challenging for new researchers to solve real-life problems, such as those in agricultural imaging. Agricultural datasets often include region-specific patterns influenced by factors such as soil metrics, weather conditions, crop imagery, and spectral signatures, making the design of the manual ViT model a time-consuming and expertise-driven process. To address these limitations, this study proposes a Neural Architecture Search (NAS) leveraging Differential Evolution (DE) algorithm which automates the process of fine-tuning hyperparameters, reducing the reliance on manual intervention and enabling the creation of highly optimized ViT models. The proposed approach is trained and tested on agricultural images dataset of tomato leaves, consisting of ten classes. The experimental results demonstrate the effectiveness of DE-based optimized ViT models by showcasing their ability to handle the unique complexities of agricultural datasets while achieving superior accuracy and reliability in classification tasks.