Cotton Leaf Disease Detection using Courier Super Resolution Network (CSRN)

Growth and yield of cotton plant can be impacted by a number of illnesses. Effective care and loss prevention of many diseases depend on early detection and diagnosis. The input must be at the highest resolution possible for this diseased leaf classification to produce correct results. In this paper, a Courier Super Resolution Network (CSRN) model is proposed for conducting image super resolution for cotton plant leaf classification to take place efficiently. The model considered regenerates deep characteristics to get a super resolution image as output with a given low resolution image in the PlantVillage dataset of cotton plant leaf images with PSNR values of 31.491, 32.752 and 34.857, SSIM values of 0.8364, 0.9024 and 0.9185 for scaling factors two, four, six.