Classification and detection of plant disease using convolutional neural networks
Researcher: Tshegofatso Sekgobela, University of Limpopo
Supervisor: TBA
In earlier years, deep learning, a subset of machine learning that comprises methods like convolutional neural networks, was extensively researched and used in a range of industries, including agriculture.
In this paper, we examine how convolutional neural networks can aid in the classification and detection of plant diseases in agriculture. The dataset used to train the neural networks in this study is a large collection of images from plant village. The model had an accuracy of 90.37 %, precision of 0.91, a recall of 0.90, and an F1-score of 0.90.