A Novel Deep Convolutional Neural Network Approach using Jacobi Polynomial and Laplacian Function (JPLF) in Recognition of Plant Leaf Disease

Pushparani S Janes; P L Chithra

doi:10.17485/IJST/v17i14.2651

Article

A Novel Deep Convolutional Neural Network Approach using Jacobi Polynomial and Laplacian Function (JPLF) in Recognition of Plant Leaf Disease

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i14.2651

Year: 2024, Volume: 17, Issue: 14, Pages: 1450-1463

Original Article

A Novel Deep Convolutional Neural Network Approach using Jacobi Polynomial and Laplacian Function (JPLF) in Recognition of Plant Leaf Disease

Pushparani S Janes¹, P L Chithra^2*

¹Department of Computer Applications, Ethiraj College for Women, Chennai, Tamil Nadu, India
²Department of Computer Science, University of Madras, Chennai, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:24 October 2023, Accepted Date:17 February 2024, Published Date:30 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: Enhancing agricultural productivity is crucial for fostering economic growth. Plant diseases significantly threaten crops, necessitating timely detection to mitigate adverse impacts on quality, quantity, and overall productivity. This research addresses the importance of early disease detection in agriculture and proposes an innovative method utilizing Jacobian Polynomial and Laplacian Function for precise identification. Methods: Efficient monitoring of large-scale crop farms with minimal workforce is essential. To achieve this, an automatic method for plant disease detection is proposed. The method leverages Jacobian polynomials to expand input features, mitigating correlation issues among input vectors. The expanded Jacobi polynomial is the input vector for a backpropagation algorithm with a novel activation function based on the Laplacian function. Findings: The efficacy of the proposed JPLF model is demonstrated through the accurate identification of leaf diseases, achieving a high testing accuracy of 92.07%. Comparative analysis with existing models, such as CNN with MobileNet V2 (85.38%) and the IoU model (83.75%), highlights the superiority of the JPLF model in plant disease detection. Novelty: To overcome the limitations of existing approaches, the incorporation of Jacobian polynomials plays a pivotal role in expanding input features. This expansion aids in eliminating correlations among input vectors, enhancing the efficacy of disease detection. The proposed model, Jacobi Polynomial and Laplacian Function (JPLF) introduces a unique activation function based on the Laplacian function, improving accuracy.

Keywords: Plant Disease Detection, Jacobi Polynomial, Laplacian Transform, Deep Learning Model, Feature Expansion

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Copyright

© 2024 Janes & Chithra. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)