Residual Skip Network-Based Super-Resolution for Leaf Disease Detection of Grape Plant

Plant diseases significantly impact agricultural productivity. Early disease identification and diagnosis are critical for plant protection. Recent deep learning approaches have substantially aided detection of plant diseases. However, existing plant leaf disease identification techniques do not provide sufficient disease detection accuracies when the input image resolution is low. Images of grape leaves taken in the field may be low-resolution (LR) in nature due to limited lighting and varying weather conditions. Such LR images may result in incorrect real-time disease diagnosis. Hence super-resolution is a way to solve this problem. This research work proposes a novel Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD) in the Grape plant. The input LR image is separated into two subcomponents using the guided filtering technique. Features from the two subcomponents are extracted using single convolutional layer and four layers of the two-channel residual skip network. Concatenation is used to combine the features of two channels. Following this, a decoding block and a convolutional layer are utilized to generate the super-resolution (SR) image. A new collaborative loss function is proposed for training. The obtained SR image is given to the Disease Detection Network (DDN) for grape leaf disease detection. This approach, based on an LR image, assists the farmer in spotting grape plant diseases very early. Proposed model was extensively trained and tested on PlantVillage, Grape 400, Grape Leaf Disease datasets with multiple super-resolution scaling factors for various grape leaves images. For different super-resolution scaling factors such as Χ2, Χ4, Χ6, the proposed model RSNSR-LDD achieved accuracies of 97.19%, 99.37%, and 99.06% for the PlantVillage dataset, 96.88%, 97.12%, and 95.43% for the Grape400 dataset, and 100% for the Grape Leaf Disease dataset.