利用VideometerLab 多光譜成像系統(tǒng)鑒別甜菜種子加工損傷-質(zhì)量控制

隆興儀表

較近，來(lái)自Aarhus大學(xué)的Birte 教授研究團(tuán)隊(duì)發(fā)表了題為Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis 的文章，對(duì)多光譜成像技術(shù)在種子質(zhì)量控制的應(yīng)用進(jìn)行了深入研究。VideometerLab 多光譜成像系統(tǒng)是的光譜、計(jì)算機(jī)等技術(shù)集成設(shè)備，體現(xiàn)了近視距多光譜研究的較高水準(zhǔn)，廣泛為機(jī)構(gòu)如ISTA等等廣泛使用。

　　Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis
　　Zahra Salimi and Birte Boelt *
　　Department of Agroecology, Aarhus University, 4200 Slagelse, Denmark; z.salimi@agro.au.dk
　　* Correspondence: bb@agro.au.dk
　　Received: 17 April 2019; Accepted: 16 May 2019; Published: 22 May 2019
　　Abstract: The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.
　　Keywords: machine vision; mechanical damage; prediction model; seed quality; seed polishing