Optimized Strategy for Rice Plant Disease Detection Using Convolutional Neural Networks

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : R. Rajakumar, D. Vaishnavi, R. Ramesh, J. Ganesh

10.14445/23488549/IJECE-V11I7P121

How to Cite?

R. Rajakumar, D. Vaishnavi, R. Ramesh, J. Ganesh, "Optimized Strategy for Rice Plant Disease Detection Using Convolutional Neural Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 7, pp. 208-219, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I7P121

Abstract:

Image classification has evolved greatly in recent years, owing to the development of machine learning algorithms and the availability of large-scale image datasets. Convolutional Neural Networks (CNNs) have profoundly impacted the field of image classification due to their ability to learn hierarchical representations directly from pixel data. Unlike traditional machine learning algorithms, which rely on handcrafted features, CNNs can extract information hierarchically through multiple convolutional and pooling layers. Initially, compared various feature extraction techniques such as Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), Scale Invariant-Future Transform (SIFT), Speeded-Up Robust Features (SURF), and wavelet domains like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with standard classifiers. In order to improve the results, design a novel CNN model for image classification tasks, with an emphasis on hyperparameter optimization, data augmentation, dropout regularization, and efficient data loading. Experimental results on benchmark datasets show that our proposed CNN model outperforms baseline models, with an improving accuracy of 76.00%. These findings demonstrate the usefulness of our approach in advancing the state-of-the-art forward in image classification task.

Keywords:

Image classification, Feature extraction, Convolutional neural network, Data augmentation, Efficient data loading.

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