Inspection and classification of wheat quality using image processing

Abstract

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 the cohesive wheats, which could simplify the algorithm of image processing and improve the accuracy rate of classification. For the method followed while recording the data, the image information must be converted into digital information, and the results are provided using appropriate image processing algorithms. Image preprocessing steps such as binarization, image enhancement, image segmentation, and morphological processing were used to reduce noise. For image segmentation, we proposed the following new segmentation methods: (1) extracting wheat region by converting image to H channel and (2) watershed algorithm based on Euclidean distance transformation. For the classification model, 22 features of 7 different qualities of wheat were inputted in the Back Propagation (BP) neural network and Support Vector Machine (SVM) model, and the overall correct classification rates were determined to be 91% and 97% for SVM and BP neural network, respectively. The BP neural network was more suitable for wheat appearance quality detection.