alexa Computerised image analysis of biocrystallograms originating from agricultural products
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): JO Andersen, CB Henriksen, J Laursen, AA Nielsen

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Procedures are presented for computerised image analysis of biocrystallogram images, originating from biocrystallization investigations of agricultural products. The biocrystallization method is based on the crystallographic phenomenon that when adding biological substances, such as plant extracts, to aqueous solutions of dihydrate CuCl2, biocrystallograms with reproducible dendritic crystal structures are formed during crystallisation. The morphological features found in the structures are traditionally applied for visual ranking or classification, e.g. in comparative studies of the effects of farming systems on crop quality. The circular structures contain predominantly a single centre from where ramifications expand in a zonal structure. In previous studies primarily texture analysis was applied, and the images analysed and classified by means of a circular region-of-interest (ROI), i.e. the region specified for analysis. In the present study the objective was to examine how the discriminative information relevant for classification purposes is distributed over the zonal structure, and how the information is affected by the varying location of the crystallisation centre. The texture analysis procedures were applied to a so-called degradation series of 33 images, including seven groups representing discrete ‘treatment levels’. The biocrystallograms were produced over seven consecutive days, on the basis of a single carrot extract degrading while stored at 6°C. This degradation is known to induce systematic changes in morphological features over a number of successive days. The biocrystallograms were scanned at 600 dpi, with 256 grey levels. Eight first-order statistical parameters were calculated for four resolution scales, and 15 second-order parameters for five scales, giving a total of 107 observations for each image. Classification of an individual image was performed by means of stepwise discriminant analysis. Four main types, and several subtypes and sizes of ROI were examined. The 33 images as well as a subset of 21 images were examined. When imposing a restriction on the centre location in the subset, thereby reducing the within-group variance, the scores were markedly improved. Classifications of the total set and the subset showed scores up to 84.8 and 100%, respectively. A number of parameters showed a monotonic relationship with degradation day number. Multiple linear regressions based on up to eight parameters indicated strong relationships, with R2 up to 0.98. It is concluded that the procedures were able to discriminate the seven groups of images, and are applicable for biocrystallization investigations of agricultural products. Perspectives for the application of image analysis are briefly mentioned.

This article was published in ComP and Electra in Agri and referenced in Journal of Information Technology & Software Engineering

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