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Article

High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis

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Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
2
College of Information Science and Technology, Hebei Agricultural University, Baoding 071000, China
3
Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
4
USDA-ARS Grain Legume Genetics Physiology Research Unit, Prosser, WA 99350, USA
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USDA-ARS Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
*
Author to whom correspondence should be addressed.
Agronomy 2018, 8(5), 63; https://doi.org/10.3390/agronomy8050063
Received: 19 April 2018 / Revised: 19 April 2018 / Accepted: 26 April 2018 / Published: 3 May 2018
(This article belongs to the Special Issue Sensing and Automated Systems for Improved Crop Management)
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. View Full-Text
Keywords: imaging/sensing; image processing; coleoptile; root injury; seed/seedling traits; automated trait assessment imaging/sensing; image processing; coleoptile; root injury; seed/seedling traits; automated trait assessment
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MDPI and ACS Style

Zhang, C.; Si, Y.; Lamkey, J.; Boydston, R.A.; Garland-Campbell, K.A.; Sankaran, S. High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis. Agronomy 2018, 8, 63. https://doi.org/10.3390/agronomy8050063

AMA Style

Zhang C, Si Y, Lamkey J, Boydston RA, Garland-Campbell KA, Sankaran S. High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis. Agronomy. 2018; 8(5):63. https://doi.org/10.3390/agronomy8050063

Chicago/Turabian Style

Zhang, Chongyuan, Yongsheng Si, Jacob Lamkey, Rick A. Boydston, Kimberly A. Garland-Campbell, and Sindhuja Sankaran. 2018. "High-Throughput Phenotyping of Seed/Seedling Evaluation Using Digital Image Analysis" Agronomy 8, no. 5: 63. https://doi.org/10.3390/agronomy8050063

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