High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis
| Autor: | Kimberly A. Garland-Campbell, Rick A. Boydston, Sindhuja Sankaran, Chongyuan Zhang, Yongsheng Si, Jacob Lamkey |
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| Rok vydání: | 2018 |
| Předmět: |
seed/seedling traits
automated trait assessment 0106 biological sciences root injury business.product_category Correlation coefficient imaging/sensing image processing coleoptile Image processing 01 natural sciences lcsh:Agriculture Digital image processing Digital camera Mathematics biology lcsh:S food and beverages 04 agricultural and veterinary sciences Phenotypic trait biology.organism_classification Horticulture Coleoptile Germination Seedling 040103 agronomy & agriculture 0401 agriculture forestry and fisheries business Agronomy and Crop Science 010606 plant biology & botany |
| Zdroj: | Agronomy, Vol 8, Iss 5, p 63 (2018) Agronomy; Volume 8; Issue 5; Pages: 63 |
| ISSN: | 2073-4395 |
| DOI: | 10.3390/agronomy8050063 |
| Popis: | Image-based evaluation of phenotypic traits has been applied for plant architecture, seed, canopy growth/vigor, and root characterization. However, such applications using computer vision have not been exploited for the purpose of assessing the coleoptile length and herbicide injury in seeds. In this study, high-throughput phenotyping using digital image analysis was applied to evaluate seed/seedling traits. Images of seeds or seedlings were acquired using a commercial digital camera and analyzed using custom-developed image processing algorithms. Results from two case studies demonstrated that it was possible to use image-based high-throughput phenotyping to assess seeds/seedlings. In the seedling evaluation study, using a color-based detection method, image-based and manual coleoptile length were positively and significantly correlated (p < 0.0001) with reasonable accuracy (r = 0.69–0.91). As well, while using a width-and-color-based detection method, the correlation coefficient was also significant (p < 0.0001, r = 0.89). The improvement of the germination protocol designed for imaging will increase the throughput and accuracy of coleoptile detection using image processing methods. In the herbicide study, using image-based features, differences between injured and uninjured seedlings can be detected. In the presence of the treatment differences, such a technique can be applied for non-biased symptom rating. |
| Databáze: | OpenAIRE |
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