Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

Author:

Narayanan K. Lakshmi1ORCID,Krishnan R. Santhana2ORCID,Robinson Y. Harold3ORCID,Julie E. Golden4ORCID,Vimal S.5ORCID,Saravanan V.6ORCID,Kaliappan M.5

Affiliation:

1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, India

2. Department of Electronics and Communication Engineering, SCAD College of Engineering and Technology, Tirunelveli, India

3. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

4. Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, India

5. Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, India

6. Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Dembidolo, Ethiopia

Abstract

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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