title: Viability prediction of Ricinus cummunis L. seeds using multispectral imaging.
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.
authors.affiliation: Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark. merete.olesen@agro.au.dk.
authors.forename: Merete Halkjær
authors.initials: MH
authors.lastname: Olesen
authors.affiliation: Department of Agronomy, Shahrekord University, Shahrekord 88176-53849, Chaharmahal Bakhtiyari, Iran. Nikneshan@stu.sku.ac.ir.
authors.forename: Pejman
authors.initials: P
authors.lastname: Nikneshan
authors.affiliation: Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark. santosh.shrestha@agro.au.dk.
authors.forename: Santosh
authors.initials: S
authors.lastname: Shrestha
authors.affiliation: Department of Agronomy, Shahrekord University, Shahrekord 88176-53849, Chaharmahal Bakhtiyari, Iran. tadayyon.sku@gmail.com.
authors.forename: Ali
authors.initials: A
authors.lastname: Tadayyon
authors.affiliation: Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark. Lise.deleuran@agro.au.dk.
authors.forename: Lise Christina
authors.initials: LC
authors.lastname: Deleuran
authors.affiliation: Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark. Birte.boelt@agro.au.dk.
authors.forename: Birte
authors.initials: B
authors.lastname: Boelt
authors.affiliation: Department of Agroecology, Science and Technology, Aarhus University, Slagelse 4200, Denmark. Rene.gislum@agro.au.dk.
authors.forename: René
authors.initials: R
authors.lastname: Gislum
mesh_terms: D012278:Ricinus / Q000502:physiology*; D012639:Seeds / Q000502:physiology*; D013057:Spectrum Analysis / Q000379:methods*