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ResNext50 based convolution neural network-long short term memory model for plant disease classification

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Abstract

Agricultural problems need to be dealt with advanced computing methods to increase food productivity. Automatic classification of plant disease using deep learning methods helps to analyze the food quality and productivity. The existing methods applied Convolutional Neural Network (CNN) based models such as VGG-19, VGG-16, AlexNet, and Resnet-50 for plant disease classification. The existing methods have limitations of vanishing gradient problem and overfitting problem in plant disease classification. The ResNext50-Long Short Term Memory (LSTM) is proposed to improve plant disease classification performance. The hybrid of ResNext50-LSTM model is proposed for effective feature extraction from input images and classification of plant diseases. The ResNext50 architectures helps to limit increases of features in input layer to eliminate bottleneck problem and store relevant features for long term in LSTM for classification. The ResNext 50 model increases the features from 4 to 128 for optimal path construction and existing techniques increases the features more than 128 that causes the overfitting problem. This process of feature limit helps to reduce the overfitting problem and vanishing gradient in LSTM model. The ResNext50 is applied to extract and select the relevant features from input images and LSTM model has advantage of store the relevant information on long term for classification. The ResNext50 model selects the features to differentiate the correlation between the relevant and irrelevant features in feature extraction. The ResNext50 extracted features were applied to the LSTM model to improve classification performance. The proposed Resnext50-LSTM model and existing CNN model performances are tested on plant village dataset to analysis the efficiency. The proposed ResNext50-LSTM model has validation accuracy of 95.44%, and existing ResNext50 model has 93.56% accuracy, and ResNet50 model 93.45% accuracy in plant disease classification.

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Data availability

The datasets generated during and/or analysed during the current study are available in the Kaggle repository, https://www.kaggle.com/vipoooool/new-plant-diseases-dataset.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, G D Goenka University, Gurugram, India

    Shashi Tanwar & Jaspreet Singh

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  1. Shashi Tanwar
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Tanwar, S., Singh, J. ResNext50 based convolution neural network-long short term memory model for plant disease classification. Multimed Tools Appl 82, 29527–29545 (2023). https://doi.org/10.1007/s11042-023-14851-x

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