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Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network.

S Abisha¹, A M Mutawa², Murugappan Murugappan^3,4,5

¹Department of Electronics and Communication Engineering, Rohini College of Engineering and Technology, Nagercoil, India.

|April 5, 2023

Summary

This study introduces a novel method using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN) for accurate brinjal leaf disease identification. The DCNN model achieved a high accuracy of 93.30% in classifying various plant diseases.

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Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Accurate plant disease diagnosis is crucial for crop yield.
Manual identification of plant diseases is time-consuming and often relies on microscopic symptoms.
Early detection of diseases in crops like brinjal is essential for effective management.

Purpose of the Study:

To develop an automated system for identifying and classifying brinjal leaf diseases.
To compare the performance of Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN) for disease classification.
To improve the accuracy and efficiency of plant disease diagnosis in agriculture.

Main Methods:

Collected 1100 images of diseased brinjal leaves (five species) and 400 healthy leaf images.
Applied image preprocessing techniques including Gaussian filtering and Expectation-Maximization (EM) segmentation.
Utilized discrete Shearlet transform for feature extraction (texture, color, structure) and DCNN/RBFNN for classification.

Main Results:

Deep Convolutional Neural Networks (DCNN) achieved a mean accuracy of 93.30% with feature fusion.
Radial Basis Feed Forward Neural Networks (RBFNN) achieved 87% accuracy with feature fusion.
DCNN outperformed RBFNN in classifying brinjal leaf diseases, especially with integrated features.

Conclusions:

The proposed DCNN and RBFNN models offer an efficient and accurate method for brinjal leaf disease classification.
Automated image analysis using DCNN shows significant potential for early disease detection in agriculture.
Feature fusion techniques enhance the performance of neural networks in identifying plant diseases.