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Light Acquisition

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Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN)

Kerehalli Vinayaka Prasad¹, Hanumesh Vaidya¹, Choudhari Rajashekhar²

¹Department of Studies in Mathematics, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka, India.

Scientific Reports

|April 18, 2024

Summary

This summary is machine-generated.

Accurate grape leaf disease identification is crucial for agriculture. A Deep Convolutional Neural Network (DCNN) Classifier model, enhanced with VGG16, achieved 99.06% accuracy, outperforming standard Convolutional Neural Networks (CNNs).

Keywords:

Convolutional neural network Deep neural network classifier Support vector machine Transfer learning Visual Geometry Group

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Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Grape cultivation faces significant threats from pests and diseases, impacting productivity and crop quality.
Accurate and timely identification of grape leaf diseases is essential for effective management and economic stability.

Purpose of the Study:

To develop and evaluate a Deep Convolutional Neural Network (DCNN) Classifier model for classifying grape leaf diseases.
To compare the performance of the DCNN model against standard Convolutional Neural Network (CNN) models, with and without data augmentation.

Main Methods:

Utilized a publicly available dataset of grape leaf images.
Employed a DCNN Classifier model based on the VGG16 architecture, incorporating additional CNN layers.
Implemented CNN models with and without data augmentation for comparative analysis.

Main Results:

The DCNN Classifier model achieved high accuracy rates: 99.18% for training and 99.06% for testing.
The DCNN Classifier model demonstrated superior performance compared to the CNN models evaluated in the study.
The model's enhanced VGG16 architecture improved generalization capabilities.

Conclusions:

The DCNN Classifier model, leveraging VGG16 and supplementary CNN layers, is highly effective for grape leaf disease identification.
The model shows significant potential as a decision support system for farmers, enabling prompt disease management.
This study validates the reliability and agricultural utility of the proposed DCNN classifier.