Online Measuring and Size Sorting for Perillae Based on Machine Vision

Abstract

Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.

Makine Öğrenmesi Algoritmaları Kullanılarak Kayısı İç Çekirdeklerinin Sınıflandırılması

“…%99.25'lik bir doğrulukla başarılı sonuçlar elde etmişlerdir [14]. Zhao ve arkadaşları [15], nanegiller ailesinden olan sağlık ve gıda sektöründe kullanılan Perilla bitkisinin makine öğrenmesi ile sınıflandırılması işlemini gerçekleştirmişlerdir. Kamera ile aldıkları görüntülere bir matematiksel model geliştirerek modellemesini yapmışlardır.…”