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Agronomy 2018, 8(5), 63;

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

Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA
College of Information Science and Technology, Hebei Agricultural University, Baoding 071000, China
Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
USDA-ARS Grain Legume Genetics Physiology Research Unit, Prosser, WA 99350, USA
USDA-ARS Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
Author to whom correspondence should be addressed.
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)
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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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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.

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