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

Türkoğlu, Muammer; Hanbay, Davut; Şengür, Abdulkadir

doi:10.1007/s12652-019-01591-w

Cited by 137 publications

(57 citation statements)

References 32 publications

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“…In this paper, the purpose of using the pre-trained AlexNet architecture is to realize a faster and highperformance detection system using both weights of pre-trained architectures and low dataset. Additionally, the previous object-oriented research used the fc6 and fc7 layers of pretrained CNN-based AlexNet architecture [14,16,[40][41][42][43]. In this study, the conv1, conv2, conv3, conv4, and conv5 layers containing high-dimensional feature vectors, as well as the fc6 and fc7 layers were used.…”

Section: Discussionmentioning

confidence: 99%

“…Transfer learning enables information to be used from pre-learned tasks and this information is then reapplied later in order to solve other problems. Thanks to this approach, the weights in the model contain a significant amount of information, resulting in a higher rate of success through faster learning as information is effectively reused [14][15][16][17]. A sample illustration of the fine-tuning process is shown in Fig.…”

Section: Pretrained Architecturementioning

confidence: 99%

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COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

2020

Self Cite

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The recent novel coronavirus (also known as COVID-19) has rapidly spread worldwide, causing an infectious respiratory disease that has killed hundreds of thousands and infected millions. While test kits are used for diagnosis of the disease, the process takes time and the test kits are limited in their availability. However, the COVID-19 disease is also diagnosable using radiological images taken through lung X-rays. This process is known to be both faster and more reliable as a form of identification and diagnosis. In this regard, the current study proposes an expert-designed system called COVIDetectioNet model, which utilizes features selected from combination of deep features for diagnosis of COVID-19. For this purpose, a pretrained Convolutional Neural Network (CNN)-based AlexNet architecture that employed the transfer learning approach, was used. The effective features that were selected using the Relief feature selection algorithm from all layers of the architecture were then classified using the Support Vector Machine (SVM) method. To verify the validity of the model proposed, a total of 6092 X-ray images, classified as Normal (healthy), COVID-19, and Pneumonia, were obtained from a combination of public datasets. In the experimental results, an accuracy of 99.18% was achieved using the model proposed. The results demonstrate that the proposed COVIDetectioNet model achieved a superior level of success when compared to previous studies.

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Section: Discussionmentioning

confidence: 99%

Section: Pretrained Architecturementioning

confidence: 99%

COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

2020

Self Cite

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“…In computer vision, they are mostly utilized for the processing of dynamically changing data such as motion behavior [21] and tracking of objects [11]. Not only temporal data can be processed by LSTMs: In [22], apple diseases and pests are detected. Here, the purpose of LSTMs was to combine the features of three deep models, namely AlexNet, GoogleNet, and DenseNet201.…”

Section: Related Papersmentioning

confidence: 99%

Active multiview recognition with hidden Markov temporal support

Nagy

Rashad

Czúni

2020

SIViP

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Our paper deals with active multiview object recognition focusing on the directional support of sequential multiple shots. Since inertial sensors are easily available nowadays, we propose the use of them to estimate the orientation change of the camera and thus to estimate the probability of relative poses. With the help of relative orientation change, we can compute transition probabilities between possible poses and can use a hidden Markov model to evaluate state (pose) sequences and can thus increase the recognition rate. Furthermore, we can plan our next viewing position to minimize the risk of misclassification, resulting in higher overall recognition rates. Besides giving the theoretical details, we use two datasets to illustrate the performance of our model through several tests including occlusion, blur, Gaussian noise, and to compare to a solution with a long short-term memory network.

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“…In another work [8], Simple Iterative Linear Clustering Algorithm (SLIC) method is used group the color features (super pixels) which in turn are used to train the ANN to classify whether the super pixels that are neither healthy nor healthy. In [9], the authors recommended better method for diagnosing apple leaf diseases using Deep Neural Network (DNN).…”

Section: Related Workmentioning

confidence: 99%

Enhanced disease detection in plant leaves using segmentation and feature extraction techniques

Durairaj¹,

Surianarayanan²

2020

Malaya J. Mat

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

Cited by 137 publications

References 32 publications

COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

Active multiview recognition with hidden Markov temporal support

Enhanced disease detection in plant leaves using segmentation and feature extraction techniques

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