# Quality Assessment of Components of Wheat Seed Using Different Classifications Models
###### Abstract  
To use machine vision technology in visual quality control of cereal seeds, sufficient knowledge is necessary. In this work, the capability of machine visual systems, equipped with industrial digital cameras for the identification and classification of seven-grain groups in wheat seed samples, was studied. Two statistical models and three support vector machines were employed in this study. Through image processing of 21,000 single grains, the shape, colour, and textural features of each grain were determined. Ninety-one features were ranked through the Relieff method. The shape features were the most prominent, followed by the textural and colour features. Among the five models tested, the highest classification accuracy was obtained using quadratic support vector machine (QSVM) and the first 35 features. In the test run of this model with independent data, the classification accuracy for sound white wheat, small white wheat, broken white wheat, shrunken white wheat, red wheat, barley and rye were, respectively, 98.7, 98, 99.3, 90.7, 99, 100, and 97.3%, with an overall average accuracy of 97.6%. In the context of this study, the machine vision system--comprising an industrial digital camera and quadratic support vector machine or non-linear discriminate analysis method--was identified as a valuable system in the investigation of the visual qualities of wheat seeds. cervical classification; wheat certification; image processing support vector machine; Relieff +
Footnote †: journal:Thus, quantitative and objective methods for measuring cereal features are useful and will be required in the future. Machine vision systems have been widely used in different research areas and recent technological developments in computer sciences and image processing have expanded the use of digital images. Recent studies have indicated that image processing is one of the key elements in developing automatic evaluation systems for crops. The results indicated that the digital image processing system is much more accurate in comparison with the manual methods [1]. By employing this advanced tool, one can attain standardization, integration, cost reduction, and improvement in quality control while increasing accuracy and reducing time. Regarding the classification of cereals and the alien grains, extensive studies have been conducted worldwide, and a limited number of them were conducted in Iran. To present a machine vision system for quality control of wheat seeds, it is required to conduct thorough and foundational research. These studies have identified and classified wheat and barley grains variety through image processing with an acceptable accuracy [2; 3; 4; 5; 6]. For a single-seed classification of red wheat, durum wheat, barley, wild oat, and rye, using three groups of features (shape, colour, and texture), a thorough study was conducted with a video camera [7]. The highest accuracy in this research belonged to the model comprising three group features: shape, colour, and texture. To identify damaged wheat grains, the images of grains while falling were captured by a high-speed digital camera and subsequently processed. By using LDA (linear discriminate analysis) and KNN (K-nearest neighbour) methods, an accuracy of 91-94% was obtained [8]. Some researchers reported that not only the cereal grains but also the dockage in samples can be accurately classified by machine vision systems [9].Colour and texture features are more helpful in combination than alone in grain classification [10]. One can attain the highest accuracy in classifying wheat and barley by considering three features: maximum radius, green band mean, and grey-level co-occurrence matrix (GLCM) at 90\({}^{\circ}\)[11]. Visual features of wheat based on digital image processing work very well in identifying the germinated and musty wheat seeds [12]. A literature review shows that, in normal imaging, machine vision technology uses grain feature differences such as shape, texture, and colour for the identification of product verity and constituent elements.