Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests - Turkoglu, Muammer; Hanbay, Davut; Sengur, Abdulkadir (Springer Heidelberg, 2019)
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.
Keywords: Plant diseases and pests detection; Convolutional neural networks; Deep learning architectures; Deep features; LSTM