Modified Whale Optimization Algorithm with Deep Learning-Driven Plant Leaf Disease Detection and Classification
Modified Whale Optimization Algorithm with Deep Learning-Driven Plant Leaf Disease Detection and Classification |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-4 |
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| Year of Publication : 2024 | ||
| Author : N. Venkatakrishnan, M. Natarajan |
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| DOI : 10.14445/22315381/IJETT-V72I4P127 | ||
How to Cite?
N. Venkatakrishnan, M. Natarajan, "Modified Whale Optimization Algorithm with Deep Learning-Driven Plant Leaf Disease Detection and Classification," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 260-268, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P127
Abstract
Plant Leaf Disease (PLD) cause extensive damage to crops, resulting in economic losses and reduced yields in agriculture. For timely intervention and effective disease management, earlier identification of these diseases is significant. Recently, the Deep Learning (DL) technique has had tremendous potential in the fields of Computer Vision (CV), involving recognition and classification of PLD. Researchers and developers have been capable of achieving tremendous performance in the identification and classification of PLDs by leveraging Deep Neural Networks (DNN), which aids in earlier diagnosis and intervention. This study offers a Modified Whale Optimization Algorithm with DL-Driven PLD Detection and Classification (MWOADL-PLDDC) technique. The MWOADL-PLDDC technique leverages the DL model with a hyperparameter tuning strategy for recognizing PLD. To obtain this, the MWOADL-PLDDC technique makes use of the Multi-Direction and Location Distribution of Pixels in Trend Structure (MDLDPTS) technique for feature extraction purposes. Meanwhile, the Deep Stacked Autoencoder (DSAE) method gets exploited for the recognition of healthy and diseased plant leaf images. For enhancing the detection rate of the DSAE approach, the IWOA is utilized to alter the hyperparameter value of the DSAE approach. The simulation outcomes demonstrate the efficacy of the MWOADL-PLDDC technique in the accurate recognition and classification of PLDs. The MWOADL-PLDDC technique exhibits high accuracy in distinguishing healthy leaves from diseased ones and accurately identifying the specific disease type.
Keywords
Image classification, Deep learning, Plant leaf disease, Computer vision, Convolutional whale optimization algorithm.
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