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Special Issue "Deep Learning for Nondestructive Detection and Analysis Using Hyperspectral Imaging"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 April 2023 | Viewed by 1634

Special Issue Editor

Dr. Seung-Chul Yoon
E-Mail Website
Guest Editor
USDA-ARS Quality & Safety Assessment Research Unit, Athens, GA, USA
Interests: hyperspectral imaging; artificial intelligence; deep learning; real-time machine vision; non-destructive sensing of agricultural and food products for safety and quality assessment; big image data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

In recent years, deep learning has shown tremendous performance and promise in various visual information extraction, detection, and analysis tasks for diverse scientific communities and industries. Hyperspectral images carry both visual and spectral information, which has proven to be really useful and effective for nondestructive visual detection and analysis tasks. With the advent of deep learning, a lot of progress has been made in deep learning-based hyperspectral image processing and analysis, especially for remote sensing applications. Still, a lot of work needs to be done for many other applications to improve deep learning-based hyperspectral imaging and find solutions to challenging real-world problems. This Special Issue aims to introduce and promote recent research on adaptation and applications of deep learning for hyperspectral image processing and analysis. This Special Issue is particularly interested in recent work involving new and innovative methods for nondestructive quality and safety assessment and sensing of materials and products with deep learning-based hyperspectral imaging.

We solicit both original research papers and review articles on various aspects of deep learning-based hyperspectral imaging, including but not limited to, the following topics and applications:

  • Transfer learning
  • Deep learning architectures and models
  • Quality and safety assessment of agriculture and food products
  • Plant phenotyping
  • Environment monitoring
  • Precision agriculture
  • Health and medical applications
  • Instrumentation
  • Sensor/data fusion
  • Spectral image management, pretreatment, and processing

Dr. Seung-Chul Yoon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Letter
Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms
Sensors 2020, 20(23), 6980; https://doi.org/10.3390/s20236980 - 07 Dec 2020
Cited by 6 | Viewed by 1189
Abstract
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. [...] Read more.
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used. Full article
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