PlantGhostNet: An Efficient Novel Convolutional Neural Network Model to Identify Plant Diseases Automatically

Plants are an integral part of the food chain. They provide food to the majority of the world's population. Besides this, the economy of many countries depends on agriculture directly or indirectly. The productivity of agriculture is directly dependent on the health of crops. Therefore, the identification of diseases in plants during their initial growing phases provides more profit to farmers and improves the economies of many agrarian countries. This paper proposes a novel Convolutional Neural Network (CNN) model named PlantGhostNet for automatic plant disease detection. The proposed PlantGhostNet model reduces the amount of trainable parameters significantly. To decrease the trainable parameters, the Ghost Module is used. This research work also uses Squeeze-and-Excitation Module for performance improvement of the proposed model. To the best of our knowledge, there is no research work present in the literature that utilizes the combination Ghost Module and Squeeze-and-Excitation Module for plant disease detection. The PlantGhostNet model is used to identify the Bacterial Spot disease of peach plants. However, the proposed work can be applied to diagnose other plant diseases as well. The PlantGhostNet model achieves 99.75 percent training accuracy and 99.51 percent validation accuracy in detecting Bacterial Spot disease of peach plants. High accuracy and less amount of trainable parameters make the PlantGhostNet model suitable to be deployed in low computational power devices such as smartphones, tablets, etc.