Fungal and Bacteria Disease Detection Using Feature Extraction with Classification Based on Deep Learning Architectures

Abstract

In India, agriculture provides a living for half the population. Food security is significantly threatened by microbial infections, however due to inadequate infrastructure, it is still difficult to identify them quickly. This paper propose novel technique in Fungal and bacteria disease detection of tomato and grapes plant using deep learning based on feature extraction and classification. Here the input images of tomato and grapes has been collected and processed for noise removal and image resize. This processed image features have been extracted using scale-invariant feature transform (SIFT) and classified using Convolutional neural network-based Alex Net architecture. The suggested model has attained the highest testing accuracy. The deep learning model utilised in this study aims to identify disease in plant leaves. However, in the future, the model can be combined with a drone or any other technology to identify plant diseases in real time and inform people of the locations of the afflicted plants so that they can be treated appropriately.