Equilibrium Optimizer with Deep Convolutional Neural Network-based SqueezeNet Model for Grape Leaf Disease Classification in IoT Environment

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : P. Sindhu, G. Indirani
DOI :  10.14445/22315381/IJETT-V70I5P212

Citation 

MLA Style: Sindhu, P., and Indirani, G. "Equilibrium Optimizer with Deep Convolutional Neural Network-based SqueezeNet Model for Grape Leaf Disease Classification in IoT Environment." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 94-102. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P212

APA Style:Sindhu, P., & Indirani, G. (2022). Equilibrium Optimizer with Deep Convolutional Neural Network-based SqueezeNet Model for Grape Leaf Disease Classification in IoT Environment. International Journal of Engineering Trends and Technology, 70(5), 94-102. https://doi.org/10.14445/22315381/IJETT-V70I5P212

Abstract
Internet of Things (IoT) plays a vital role in enhancing crop quality and productivity in the agricultural sector. Accurate and earlier detection of grape leaf diseases is important to control the spreading of diseases and safeguard the healthier growth of grape productivity. Since the traditional way of visual inspection is a difficult and laborious process, automated tools using computer vision and artificial intelligence (AI) approaches are essential. At the same time, the effective selection of hyperparameter values results in improved classification results. This study introduces a novel Equilibrium Optimizer with a Deep Convolutional Neural Network-based SqueezeNet Model for Grape Leaf Disease Classification (EOSN-GLDC) model in an IoT environment. The proposed EOSN-GLDC model focuses on recognizing and classifying grape leaf diseases. The presented EOSN-GLDC model initially employs the median filtering (MF) approach to remove noise. Followed by the EO algorithm with the SqueezeNet model is utilized as a feature extractor where the hyperparameters involved are adjusted by utilizing the EO algorithm. Moreover, an extreme learning machine (ELM) classifier is applied for allocating proper class labels to the input images. To demonstrate the improved performance of the EOSN-GLDC model, a comprehensive experimental analysis is made using a benchmark dataset, and the results indicate the betterment of the EOSN-GLDC model. 

Keywords
Computer vision, Deep learning, Grape leaf diseases, Metaheuristics, Plant diseases.

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