Inspection and classification of wheat quality using image processing

Wheat plays an important role in our daily life and industrial production. Several computer vision approaches have been proposed for classifying wheat quality, but there were some methods focusing on the problem of cohesive wheats while image processing. In this paper, we designed a single kernel guide groove to separate wheats, which could simplify algorithm processing improve accuracy rate classification. For method followed recording data, information must be converted into digital information, results are provided using appropriate algorithms. Image preprocessing steps such as binarization, enhancement, segmentation, morphological used reduce noise. following new segmentation methods: (1) extracting region by converting H channel (2) watershed based Euclidean distance transformation. classification model, 22 features 7 different qualities inputted Back Propagation (BP) neural network Support Vector Machine (SVM) overall correct rates determined 91% 97% SVM BP network, respectively. The was more suitable appearance quality detection.