scholarly journals Effect of Data Augmentation in the Classification and Validation of Tomato Plant Disease with Deep Learning Methods

2021 ◽  
Vol 38 (6) ◽  
pp. 1657-1670
Author(s):  
Shivali Amit Wagle ◽  
Harikrishnan R ◽  
Jahariah Sampe ◽  
Faseehuddin Mohammad ◽  
Sawal Hamid Md Ali
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
Data Augmentation ◽  
Classification Problem ◽  
Indian States ◽  
Tomato Plants ◽  
Color Data ◽  
Testing Accuracy ◽  
Deep Learning Model

The paper discusses disease identification and classification in tomato plants, as well as the effect of data augmentation in deep learning models. The database used here is Tomato plant leaves (TPL) images from the PlantVillage Database in the healthy and disease classes. The disease categories have been chosen depending on their occurrence in the Indian States. The proposed ResNet50, ResNet18, and ResNet101 deep-learning model with transfer learning combined with the softmax classification are used to identify and categorize the tomato leaf images into the healthy or diseases classes in the dataset. The unique combination of including the noise and blur in the images and position and color data augmentation makes the dataset robust. Two different data augmentation methods are used for the classification problem, and significant improvement is seen in the classification accuracy with the proposed augmented dataset. The model’s success rate makes the model helpful in extending support in validating a model for identifying plant disease. The validation of models is done on PlantVillage and images taken at Krishi Vigyan Kendra Narayangaon, Pune, India. ResNet101 model trained with augmented dataset outperforms the testing accuracy of 99.99% and validation accuracy of 95.83%.

scholarly journals Deep Learning for Laryngopharyngeal Reflux Diagnosis

2021 ◽  
Vol 11 (11) ◽  
pp. 4753
Author(s):  
Gen Ye ◽  
Chen Du ◽  
Tong Lin ◽  
Yan Yan ◽  
Jack Jiang
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Data Augmentation ◽  
Laryngopharyngeal Reflux ◽  
Ph Monitoring ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

Deep Learning-Based Knowledge Extraction From Diseased and Healthy Edible Plant Leaves

2021 ◽  
Vol 12 (2) ◽  
pp. 67-81
Author(s):  
Udit Jindal ◽  
Sheifali Gupta
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Edible Plant ◽  
Research Article ◽  
Segmented Images ◽  
Significant Area ◽  
Testing Accuracy ◽  
Disease Present

Agriculture contributes majorly to all nations' economies, but crop diseases are now becoming a very big issue that has to be resolving immediately. Because of this, crop/plant disease detection becomes a very significant area to work. However, a huge number of studies have been done for automatic disease detection using machine learning, but less work has been done using deep learning with efficient results. The research article presents a convolution neural network for plant disease detection by using open access ‘PlantVillage' dataset for three versions that are colored, grayscale, and segmented images. The dataset consists of 54,305 images and is being used to train a model that will be able to detect disease present in edible plants. The proposed neural network achieved the testing accuracy of 99.27%, 98.04%, and 99.14% for colored, grayscale, and segmented images, respectively. The work also presents better precision and recall rates on colored image datasets.

scholarly journals Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2972
Author(s):  
Qinghua Gao ◽  
Shuo Jiang ◽  
Peter B. Shull
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Data Augmentation ◽  
Experimental Testing ◽  
Learning Model ◽  
Hand Gesture ◽  
Angle Estimation ◽  
Gesture Classification ◽  
Deep Learning Model ◽  
Computer Interaction

Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.

scholarly journals A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signal

2017 ◽  
Author(s):  
Wei Zhang ◽  
Gaoliang Peng ◽  
Chuanhao Li ◽  
Yuanhang Chen ◽  
Zhujun Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Learning Model ◽  
Frequency Noise ◽  
Nonlinear Mapping ◽  
Intelligent Fault Diagnosis ◽  
Deep Learning Model ◽  
Adaptation Ability

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

scholarly journals Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes

2021 ◽  
Vol 12 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Mun Haeng Lee ◽  
Dong Sun Park
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
High Efficiency ◽  
Recognition Rate ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Improve Model ◽  
Improving Accuracy ◽  
And Control

Recognizing plant diseases is a major challenge in agriculture, and recent works based on deep learning have shown high efficiency in addressing problems directly related to this area. Nonetheless, weak performance has been observed when a model trained on a particular dataset is evaluated in new greenhouse environments. Therefore, in this work, we take a step towards these issues and present a strategy to improve model accuracy by applying techniques that can help refine the model’s generalization capability to deal with complex changes in new greenhouse environments. We propose a paradigm called “control to target classes.” The core of our approach is to train and validate a deep learning-based detector using target and control classes on images collected in various greenhouses. Then, we apply the generated features for testing the inference of the system on data from new greenhouse conditions where the goal is to detect target classes exclusively. Therefore, by having explicit control over inter- and intra-class variations, our model can distinguish data variations that make the system more robust when applied to new scenarios. Experiments demonstrate the effectiveness and efficiency of the proposed approach on our extended tomato plant diseases dataset with 14 classes, from which 5 are target classes and the rest are control classes. Our detector achieves a recognition rate of target classes of 93.37% mean average precision on the inference dataset. Finally, we believe that our study offers valuable guidelines for researchers working in plant disease recognition with complex input data.

scholarly journals Woven Fabric Pattern Recognition and Classification Based on Deep Convolutional Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Muhammad Ather Iqbal Hussain ◽  
Babar Khan ◽  
Zhijie Wang ◽  
Shenyi Ding
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Texture Features ◽  
Learning Model ◽  
Woven Fabrics ◽  
Machine Learning Algorithms ◽  
Woven Fabric ◽  
Automated System ◽  
Deep Convolutional Neural Networks ◽  
Deep Learning Model

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an automated system is needed for classification of woven fabric to improve productivity. In this paper, we propose a deep learning model based on data augmentation and transfer learning approach for the classification and recognition of woven fabrics. The model uses the residual network (ResNet), where the fabric texture features are extracted and classified automatically in an end-to-end fashion. We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pretrained VGGNet deep learning model which showed that the proposed method achieved higher accuracy when rotational orientations in fabric and proper lighting effects were considered.

scholarly journals Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6126
Author(s):  
Tae Hyong Kim ◽  
Ahnryul Choi ◽  
Hyun Mu Heo ◽  
Hyunggun Kim ◽  
Joung Hwan Mun
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Fall Detection ◽  
Learning Model ◽  
Measurement Unit ◽  
Percentage Error ◽  
Loss Of Balance ◽  
Impact Acceleration ◽  
Deep Learning Model

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

Deep Learning Model for Detecting and Diagnosing Plant Disease

2021 ◽  
Author(s):  
Nazmun Nessa Moon ◽  
Ms. Shayla Sharmin ◽  
Refath Ara Hossain ◽  
Israt Jahan ◽  
Fernaz Narin Nur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Learning Model ◽  
Deep Learning Model
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