ConvPlant-Net: A Convolutional Neural Network based Architecture for Leaf Disease Detection in Smart Agriculture

Convolutional Neural Networks have demonstrated state-of-the-art performance in various image classification and computer vision-related tasks. Plant disease detection is one of the essential areas of image classification. Though many models have been proposed for the efficient classification of plant disease images, there is a dire need to develop an image classification mechanism based on deep learning, which has fewer parameters to implement the algorithm on mobile devices. In this manuscript, we propose a ConvPlant-Net, a Convolutional Neural Network based Plant disease detection system that uses a combination of Depth-Wise Separable Convolutional, 2-Dimensional transpose Layer, and a Convolutional layer for efficient learning of the high and low-level features. The proposed model contains only 31,998 trainable parameters. With fewer parameters, the model has achieved an accuracy of 98.62%, 99.36%, and 99.60% on Tomato, Pepper Bell, and Potato crops from the publicly available PlantVillage dataset.