Abstract:
In a country like Bangladesh plant disease is a common factor. The timely and accurate diagnosis
of plant disease plays a very important role in preventing the loss of productivity and
loss of reduce quality of agricultural product. Till now many Machine learning (ML) models
have been employed for the detection and classification of plant disease. For advancement
Deep learning (DL) has also employed in this research area and it has shown a vital impact
in disease detection accuracy. In this paper we classified ‘15’ species of crops from 20,069
images. The dataset have been taken from plant village. We have considered here Support
Vector machine which is a supervised learning algorithm for classification and regression.We
also used Sequential Model for detection. We have used Train-Test-split model to train the
dataset and we achieved 92.5% accuracy.
Description:
This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh