Plant Disease Detection Using Deep Learning
Ebrahim Hirani, Varun Magotra, Jainam Jain, Pramod Bide
Abstract
In recent years, use of Convolutional neural networks has been explored in a wide range of applications whether its image classification, feature extraction or image segmentation. One of those applications is plant disease detection, since plant disease is one of the most significant factors that leads to poor yield in the agricultural sector. Over the period of time various deep learning approaches have been used to solve this problem of identification and classification of plant disease. But then to there are some limitation to these approaches. The recent application of transformer networks in computer vision tasks has shown great promise. This paper compares these approaches with traditional CNN approaches in the task of plant disease detection. The best validation accuracy that our transformer model achieves is 97.98%.