Machine vision based intelligent oven for baking inspection of cupcake: Design and implementation. (April 2022)

• Record Type: Journal Article
• Authors: Abdanan Mehdizadeh, Saman
• Is Part Of: Mechatronics. Volume 82(2022)
• Publication Date: 2022-04
• Subjects: Machine vision -- color and texture feature -- t-test -- SVM classification; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2022.102746
• Languages: English
• ISSNs: 0957-4158

Abstract

Highlights: An intelligent oven for baking inspection of cupcake was designed based on image processing. To perform cupcake classification, SVM classifier combined with rank key feature selection was used. Features of G, b as well as kurtosis of a and energy were selected as optimum feature vector combinations. The classifier accuracy obtained for G and b combination were 98.6, and for kurtosis of a and energy combination were 97.9. Abstract: In industrial ovens, the cooking process is controlled by adjusting cooking temperature and time. However, for having high-quality product, variations in parameters like inconsistency in raw materials, baking powder level, batter thickness and batter moisture should be taken into consideration. To handle these variations and produce high-quality products, an intelligent oven based on image processing was designed to scrutinize color and texture of cupcakes while baking. In this regard, a digital camera was used to take digital images from inside the oven, continuously. Subsequently, color and texture features were extracted from the set of cupcake images while baking. To maximize classification accuracy, a search algorithm along with a rank key feature using t-test class separability criteria method was implemented to find an optimum set of feature combinations. According to the feature selection algorithm, G ( RGB color space) and b ( Lab color space) as well as kurtosis of a ( Lab color space) and energy were the most significant feature combinations. Then, the data classification was carried out using the support vector machine (SVM). The classifier accuracy, specificity, sensitivity and Kappa coefficient obtained for G and b combination were 98.6, 100, 92.3 and 95.2%, respectively; and for kurtosis of a and energy combination were 97.9, 98.3, 95.6 and 92.2% for, respectively. Pursuant to the result, an adaptive automatic control could be conducted by real-time monitoring of color and texture features of baking goods. Through this image analysis method, baking process could dynamically regulate and optimize cooking time, energy consumption as well as quality of product.