Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/24/8425 |
| _version_ | 1827669651866255360 |
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| author | Hadhami Garbouge Pejman Rasti David Rousseau |
| author_facet | Hadhami Garbouge Pejman Rasti David Rousseau |
| author_sort | Hadhami Garbouge |
| collection | DOAJ |
| description | The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light. |
| first_indexed | 2024-03-10T03:09:08Z |
| format | Article |
| id | doaj.art-da3f8a57dd64469c8e4b2966d76194ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:09:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-da3f8a57dd64469c8e4b2966d76194ff2023-11-23T10:31:20ZengMDPI AGSensors1424-82202021-12-012124842510.3390/s21248425Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep LearningHadhami Garbouge0Pejman Rasti1David Rousseau2Université d’Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, 62 Avenue Notre Dame du Lac, 49000 Angers, FranceUniversité d’Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, 62 Avenue Notre Dame du Lac, 49000 Angers, FranceUniversité d’Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, 62 Avenue Notre Dame du Lac, 49000 Angers, FranceThe use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5</mn><mo>%</mo></mrow></semantics></math></inline-formula> correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.https://www.mdpi.com/1424-8220/21/24/8425deep learningplant growthCNNRGB-Depthimage fusionfeature fusion |
| spellingShingle | Hadhami Garbouge Pejman Rasti David Rousseau Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning Sensors deep learning plant growth CNN RGB-Depth image fusion feature fusion |
| title | Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning |
| title_full | Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning |
| title_fullStr | Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning |
| title_full_unstemmed | Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning |
| title_short | Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning |
| title_sort | enhancing the tracking of seedling growth using rgb depth fusion and deep learning |
| topic | deep learning plant growth CNN RGB-Depth image fusion feature fusion |
| url | https://www.mdpi.com/1424-8220/21/24/8425 |
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