Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation
This paper develops an approach to perform binary semantic segmentation on <i>Arabidopsis thaliana</i> root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/309 |
| _version_ | 1827617143802298368 |
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| author | Vaishnavi Thesma Javad Mohammadpour Velni |
| author_facet | Vaishnavi Thesma Javad Mohammadpour Velni |
| author_sort | Vaishnavi Thesma |
| collection | DOAJ |
| description | This paper develops an approach to perform binary semantic segmentation on <i>Arabidopsis thaliana</i> root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing. |
| first_indexed | 2024-03-09T09:40:45Z |
| format | Article |
| id | doaj.art-7996f0fe2cb94485a88e8dd482fc24db |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:45Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7996f0fe2cb94485a88e8dd482fc24db2023-12-02T00:55:27ZengMDPI AGSensors1424-82202022-12-0123130910.3390/s23010309Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic SegmentationVaishnavi Thesma0Javad Mohammadpour Velni1School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602, USADepartment of Mechanical Engineering, Clemson University, Clemson, SC 29634, USAThis paper develops an approach to perform binary semantic segmentation on <i>Arabidopsis thaliana</i> root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.https://www.mdpi.com/1424-8220/23/1/309plant root phenotypingdeep learningconditional generative adversarial networkscrop monitoring |
| spellingShingle | Vaishnavi Thesma Javad Mohammadpour Velni Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation Sensors plant root phenotyping deep learning conditional generative adversarial networks crop monitoring |
| title | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
| title_full | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
| title_fullStr | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
| title_full_unstemmed | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
| title_short | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
| title_sort | plant root phenotyping using deep conditional gans and binary semantic segmentation |
| topic | plant root phenotyping deep learning conditional generative adversarial networks crop monitoring |
| url | https://www.mdpi.com/1424-8220/23/1/309 |
| work_keys_str_mv | AT vaishnavithesma plantrootphenotypingusingdeepconditionalgansandbinarysemanticsegmentation AT javadmohammadpourvelni plantrootphenotypingusingdeepconditionalgansandbinarysemanticsegmentation |