Van-Tung Nguyen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Van-Tung Nguyen has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Chi-Hsiang Lien
Department of Mechanical Engineering, National United University, Miaoli, Taiwan
2 shared publications - Quoc-Hung Phan
Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
2 shared publications - The-Phong Duong
Department of Mechanical Engineering, HCMC University of Technology and Education, Ho Chi Minh City 700000, Vietnam
2 shared publications - Fa-Ta Tsai
Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
1 shared publication - Max Ti-Kuang Hou
Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan
1 shared publication - Ngoc-Bich Le
International University
1 shared publication
Latest Publications
- Open Access
Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks
The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.
Open AccessTomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks
The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.
- 9 August 2023
- Published by SPIE-Intl Soc Optical Eng
- Vol. 12630, 75-78
AI classification for hepatitis B virus detection based on Mueller matrix imaging
A novel method of artificial intelligence (AI) classification is proposed for hepatitis B virus (HBV) detection based on the Mueller matrix imaging system. The feasibility of the proposed technique is demonstrated by measuring the optical properties of non-infected and infected HBV blood samples. Furthermore, different AI classifier techniques namely Yolo5, Yolo5-Restnet101, Yolo5-EfficientnetB0, and Yolo5-MobilenetV2 have been employed to classify the HBV samples. The results show that the proposed method provides 99% accuracy for HBV classification. In general, the proposed technique provides reliable and simple devices for HBV diagnosis applications.
- 9 August 2023
- Published by SPIE-Intl Soc Optical Eng
- Vol. 12630, 75-78
AI classification for hepatitis B virus detection based on Mueller matrix imaging
A novel method of artificial intelligence (AI) classification is proposed for hepatitis B virus (HBV) detection based on the Mueller matrix imaging system. The feasibility of the proposed technique is demonstrated by measuring the optical properties of non-infected and infected HBV blood samples. Furthermore, different AI classifier techniques namely Yolo5, Yolo5-Restnet101, Yolo5-EfficientnetB0, and Yolo5-MobilenetV2 have been employed to classify the HBV samples. The results show that the proposed method provides 99% accuracy for HBV classification. In general, the proposed technique provides reliable and simple devices for HBV diagnosis applications.
- Open Access
Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models
Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.
Open AccessClassification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models
Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.