Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

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DOI

  • Merete Halkjær Olesen, Denmark
  • Pejman Nikneshan, Department of Agronomy, Shahrekord University, Iran, Islamic Republic of
  • Santosh Shrestha, Denmark
  • Ali Tadayyon, Department of Agronomy, Shahrekord University, Iran, Islamic Republic of
  • Lise Christina Deleuran, Denmark
  • Birte Boelt
  • René Gislum
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.
Original languageEnglish
JournalSensors
Volume15
Issue2
Pages (from-to)4592-4604
Number of pages13
ISSN1424-8220
DOIs
Publication statusPublished - 2015

    Research areas

  • multispectral imaging, castor seed, canonical discriminant analysis (CDA), viability, germination

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