Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images
There have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are s...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/21/4053 |
| _version_ | 1797467376945463296 |
|---|---|
| author | Ganbayar Batchuluun Se Hyun Nam Kang Ryoung Park |
| author_facet | Ganbayar Batchuluun Se Hyun Nam Kang Ryoung Park |
| author_sort | Ganbayar Batchuluun |
| collection | DOAJ |
| description | There have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are sensitive to light, and cannot be used in environments with low illumination. Although thermal cameras are not susceptible to these drawbacks, they are sensitive to atmospheric temperature and humidity. Moreover, in previous thermal camera-based studies, time-consuming manual analyses were performed. Therefore, in this study, we conducted a novel study by simultaneously using thermal images and corresponding visible light images of plants to solve these problems. The proposed network extracted features from each thermal image and corresponding visible light image of plants through residual block-based branch networks, and combined the features to increase the accuracy of the multiclass classification. Additionally, a new database was built in this study by acquiring thermal images and corresponding visible light images of various plants. |
| first_indexed | 2024-03-09T18:51:48Z |
| format | Article |
| id | doaj.art-51dd44cecf0749f69d8395c8e8f255f7 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:51:48Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-51dd44cecf0749f69d8395c8e8f255f72023-11-24T05:44:11ZengMDPI AGMathematics2227-73902022-11-011021405310.3390/math10214053Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light ImagesGanbayar Batchuluun0Se Hyun Nam1Kang Ryoung Park2Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, KoreaThere have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are sensitive to light, and cannot be used in environments with low illumination. Although thermal cameras are not susceptible to these drawbacks, they are sensitive to atmospheric temperature and humidity. Moreover, in previous thermal camera-based studies, time-consuming manual analyses were performed. Therefore, in this study, we conducted a novel study by simultaneously using thermal images and corresponding visible light images of plants to solve these problems. The proposed network extracted features from each thermal image and corresponding visible light image of plants through residual block-based branch networks, and combined the features to increase the accuracy of the multiclass classification. Additionally, a new database was built in this study by acquiring thermal images and corresponding visible light images of various plants.https://www.mdpi.com/2227-7390/10/21/4053plant imageimage classificationthermal imagevisible light imagedeep learning |
| spellingShingle | Ganbayar Batchuluun Se Hyun Nam Kang Ryoung Park Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images Mathematics plant image image classification thermal image visible light image deep learning |
| title | Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images |
| title_full | Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images |
| title_fullStr | Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images |
| title_full_unstemmed | Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images |
| title_short | Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images |
| title_sort | deep learning based plant classification using nonaligned thermal and visible light images |
| topic | plant image image classification thermal image visible light image deep learning |
| url | https://www.mdpi.com/2227-7390/10/21/4053 |
| work_keys_str_mv | AT ganbayarbatchuluun deeplearningbasedplantclassificationusingnonalignedthermalandvisiblelightimages AT sehyunnam deeplearningbasedplantclassificationusingnonalignedthermalandvisiblelightimages AT kangryoungpark deeplearningbasedplantclassificationusingnonalignedthermalandvisiblelightimages |