Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination.

Mirosław Jabłoński,Józef Walczyk,Ryszard Tadeusiewicz,Adam Piłat,Paweł Tylek

doi:10.3390/s16081319

Abstract

Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference.

Highlights

Oak (Quercus robur L.) is present almost all over Europe—from the Scandinavian Peninsula in the north to the Apennine Peninsulas and the Balkan in the south and from the Iberian Peninsula in the west to the foothills of the Ural Mountains in the east
We propose the use of computer image processing methods along with machine vision setup to speed up the preparation of high volumes of acorns for sowing
Colour-based discrimination is both fast and non-destructive as scarification is a regular procedure applied to acorns during processing

Summary

Introduction

Oak (Quercus robur L.) is present almost all over Europe—from the Scandinavian Peninsula in the north to the Apennine Peninsulas and the Balkan in the south and from the Iberian Peninsula in the west to the foothills of the Ural Mountains in the east. In Europe, the oak is a forest-creating species, regenerated artificially; natural regeneration is confined to certain areas and only to the years of the most abundant harvest [1,2,3]. Obtaining certified seed material requires a number of physical actions which in turn call for knowledge of the rules governing the separation processes [4]. These are based on the recognition of physical differences in properties between various. It should be noted that trees growing in the same stand can produce seeds that are very diverse in terms of size and weight—seeds from older trees are often smaller than from younger trees [6,7]

Methods

Discussion

Conclusion