Banana Plant Disease Classification Using Hybrid Convolutional Neural Network

被引：24

作者：

Narayanan, K. Lakshmi ^{[1
]}

Krishnan, R. Santhana ^{[2
]}

Robinson, Y. Harold ^{[3
]}

Julie, E. Golden ^{[4
]}

Vimal, S. ^{[5
]}

Saravanan, V. ^{[6
]}

Kaliappan, M. ^{[5
]}

机构：

[1] Francis Xavier Engn Coll, Dept Elect & Commun Engn, Tirunelveli, India

[2] SCAD Coll Engn & Technol, Dept Elect & Commun Engn, Tirunelveli, India

[3] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, India

[4] Anna Univ Reg Campus, Dept Comp Sci & Engn, Tirunelveli, India

[5] Ramco Inst Technol, Dept Artificial Intelligence & Data Sci, Rajapalayam, India

[6] Dambi Dollo Univ, Coll Engn & Technol, Dept Comp Sci, Dembidolo, Ethiopia

来源：

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | 2022年 / 2022卷

关键词：

D O I：

10.1155/2022/9153699

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：13

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