International Journal of Science, Engineering and TechnologyAuthors: Rachit Khandelwala, Paresh Jain
Abstract: Grapes are small, sweet, and versatile fruits that come in various colors and are not only enjoyed fresh but also used for making wine, raisins, and a variety of culinary products. A healthy grape harvest requires prompt identification of grape disease and taking appropriate measures to stop it from spreading. Accurate disease identification is now possible with the help of recent research advancements. However, these techniques yield low accuracy and perform poorly with low-resolution images. A novel framework incorporating TSRNet model based super resolution followed by DataLiteViT classification model is suggested as a solution to this problem. For scale factors of ×2, ×4, and ×6, respectively, the utilised lightweight TSRNet model with 2.25 M parameters can reach 30.12, 28.38, and 27.57dB Peak Signal Noise Ratio (PSNR) values and Structural Similarity Index Measure (SSIM) value of 0.913, 0.841, and 0.756. The proposed DataLiteViT model achieved accuracies of 97.90%, 97.12%, and 96.69% for low resolution images with scale factors of × 2, × 4, and × 6, respectively. Furthermore, the employed method utilizing TSRNet model for super-resolution improved DataLiteViT’s classification accuracies of 99%, 98.71%, and 98.28% for LR images with scale factors of ×2, ×4, and ×6, demonstrating an efficient and highly effective solution.
DOI: http://doi.org/10.5281/zenodo.21702891
