Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests

In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifica...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2022-07, Vol.13 (7), p.3335-3345
Main Authors: Turkoglu, Muammer, Hanbay, Davut, Sengur, Abdulkadir
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Classification
Classifiers
Computational Intelligence
Deep learning
Engineering
Feature extraction
Feature selection
Flowers & plants
Fruits
Machine learning
Medical imaging
Neural networks
Original Research
Performance evaluation
Pests
Plant diseases
Robotics and Automation
Support vector machines
User Interfaces and Human Computer Interaction
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Summary:In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifically, in transfer learning, we adopted deep feature extraction from various fully connected layers of these pre-trained deep models. AlexNet, GoogleNet and DenseNet201 models are used in this work for feature extraction. The extracted deep features are then fed into the LSTM layer in order to construct a robust hybrid model for apple disease and pest detection. Later, the output predictions of three LSTM layers determined the class labels of the input images by majority voting classifier. In addition, we use an automatic scheme for determining the best choice of the network parameters of the LSTM layer. The experiments are carried out using data consisting of real-time apple disease and pest images from Turkey and the accuracy rates are calculated for performance evaluation. The experimental results show that by using the proposed ensemble combination structure, the results are comparable to, or better than, the pre-trained deep architectures.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-019-01591-w