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Applied Sciences
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Deep Learning in Image Recognition: Latest Advances and Prospects
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Deep Learning in Image Recognition: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 618

Special Issue Editors

Dr. Yu-Xiang Zhao

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Quemoy University, Taiwan, China
Interests: machine learning; deep learning; digital image processing; human–computer interaction
Dr. Chien-Hsing Chou

E-Mail Website
Guest Editor
Department of Mathematics and Information Education, National Taipei University of Education, Taiwan, China
Interests: artificial intelligence; machine learning; data science programming; design thinking
Dr. Yi-Zeng Hsieh

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, China
Interests: computer vision; machine learning; deep learning; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image recognition has been one of the most powerful developments in the area of deep learning in recent years. From the basic technologies of image classification, object recognition, and object segmentation, to the applications of handwriting recognition, face recognition, image generation, image captioning, self-driving cars, smart homes, product inspection, security surveillance, medical imaging, augmented reality, edge computing, etc., these advances in deep learning in image recognition make it a hot topic. Although the method of deep learning is quite time-consuming and laborious, many methods that can achieve real-time calculations have been proposed thus far, and the results are quite good. There are also many discussions on the proposal of new network architecture, the improvement of network performance, and the improvement of data enhancement in deep learning. This Special Issue hopes to focus on deep learning in image recognition, and discuss the latest technological advances and future prospects.

Dr. Yu-Xiang Zhao
Dr. Chien-Hsing Chou
Dr. Yi-Zeng Hsieh
Guest Editors

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Keywords

  • applications
  • cross-disciplinary
  • real-time
  • network architecture
  • network performance
  • data enhancement

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Published Papers (1 paper)

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Research

15 pages, 870 KiB  
Article
Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
by Singara Singh Kasana and Ajayraj Singh Rathore
Appl. Sci. 2024, 14(17), 8038; https://doi.org/10.3390/app14178038 (registering DOI) - 8 Sep 2024
Abstract
Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. [...] Read more.
Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance. Full article
(This article belongs to the Special Issue Deep Learning in Image Recognition: Latest Advances and Prospects)
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