An ensemble deep learning model for automatic classification of cotton leaves diseases

Title

An ensemble deep learning model for automatic classification of cotton leaves diseases

Creator

Kukadiya H.; Arora N.; Meva D.; Srivastava S.

Description

Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved.

Source

Indonesian Journal of Electrical Engineering and Computer Science, Vol-33, No. 3, pp. 1942-1949.

Date

2024-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Classification; Convolutional neural network; Ensemble model; Hyperparameter tuning; Machine learning

Coverage

Kukadiya H., Department of Computer Application, Marwadi University, Rajkot, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Meva D., Department of Computer Application, Marwadi University, Rajkot, India; Srivastava S., School of Sciences, Christ (Deemed to be University), Delhi NCR, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 25024752

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijeecs.v33.i3.pp1942-1949

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185585750&doi=10.11591%2fijeecs.v33.i3.pp1942-1949&partnerID=40&md5=69791800f23c19717224e468349a1fbc