A method of cotton root segmentation based on edge devices
The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1122833/full |
| _version_ | 1828013158180061184 |
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| author | Qiushi Yu Hui Tang Lingxiao Zhu Wenjie Zhang Liantao Liu Nan Wang |
| author_facet | Qiushi Yu Hui Tang Lingxiao Zhu Wenjie Zhang Liantao Liu Nan Wang |
| author_sort | Qiushi Yu |
| collection | DOAJ |
| description | The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root. |
| first_indexed | 2024-04-10T09:42:22Z |
| format | Article |
| id | doaj.art-abdb3c9b8c7a4f7e97152b29c95e67aa |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T09:42:22Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-abdb3c9b8c7a4f7e97152b29c95e67aa2023-02-17T08:35:07ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-02-011410.3389/fpls.2023.11228331122833A method of cotton root segmentation based on edge devicesQiushi Yu0Hui Tang1Lingxiao Zhu2Wenjie Zhang3Liantao Liu4Nan Wang5College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaCollege of Agronomy, Hebei Agricultural University, Baoding, ChinaCollege of Modern Science And Technology, Hebei Agricultural University, Baoding, ChinaCollege of Agronomy, Hebei Agricultural University, Baoding, ChinaCollege of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, ChinaThe root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.https://www.frontiersin.org/articles/10.3389/fpls.2023.1122833/fullin situ roothigh-throughput phenotypelow-cost acquisitionsemantic segmentationedge equipment |
| spellingShingle | Qiushi Yu Hui Tang Lingxiao Zhu Wenjie Zhang Liantao Liu Nan Wang A method of cotton root segmentation based on edge devices Frontiers in Plant Science in situ root high-throughput phenotype low-cost acquisition semantic segmentation edge equipment |
| title | A method of cotton root segmentation based on edge devices |
| title_full | A method of cotton root segmentation based on edge devices |
| title_fullStr | A method of cotton root segmentation based on edge devices |
| title_full_unstemmed | A method of cotton root segmentation based on edge devices |
| title_short | A method of cotton root segmentation based on edge devices |
| title_sort | method of cotton root segmentation based on edge devices |
| topic | in situ root high-throughput phenotype low-cost acquisition semantic segmentation edge equipment |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1122833/full |
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