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Sensors 2015, 15(2), 4592-4604; doi:10.3390/s150204592

Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

1
Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark
2
Department of Agronomy, Shahrekord University, Shahrekord 88176-53849, Chaharmahal Bakhtiyari, Iran
*
Author to whom correspondence should be addressed.
Received: 22 December 2014 / Revised: 5 February 2015 / Accepted: 9 February 2015 / Published: 17 February 2015
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Abstract

The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour. View Full-Text
Keywords: multispectral imaging; castor seed; canonical discriminant analysis (CDA); viability; germination multispectral imaging; castor seed; canonical discriminant analysis (CDA); viability; germination
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Olesen, M.H.; Nikneshan, P.; Shrestha, S.; Tadayyon, A.; Deleuran, L.C.; Boelt, B.; Gislum, R. Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging. Sensors 2015, 15, 4592-4604.

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