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Early identification of potato leaf diseases is crucial to maximise crop production while sustaining global food security. While deep learning has transformed agricultural diagnostics, practical agricultural conditions frequently cause diminished effectiveness with low- resolution (LR) images. This research presents integrated two-stage architecture to tackle these issues- a novel DRBNet model for image super-resolution followed by Mobilenet based approach for disease diagnosis. The proposed DRBNet method preserves both temporal and global information, by incorporating multi-scale bidirectional interactions with identical and reduced features in its architecture. It accomplishes excellent reconstruction with PSNR scores of 34.05, 32.08, & 31.09 and SSIM scores of 0.98, 0.90, & 0.86 with downscaling of ×2, ×4, and ×6, respectively. The MobileNet model accomplishes accurate potato leaf disease identification along with optimized computing efficiency and fewer parameters by employing depthwise separable convolutions to develop a streamlined feature extraction method. This integrated approach performs noticeably better than contemporary architectures, based on research findings. The suggested integrated approach obtains impressive classification accuracies of 99.73%, 99.56%, and 99.37%, for ×2, ×4, & ×6 LR downscale factors. This integrated model provides a flexible, versatile method for potato health monitoring in realistic smart agricultural situations, by maintaining exceptional precision even at lower resolutions.