Study On Potato External Quality Detection Technology Based On Machine Vision

The potato's quality is directly related to the product economic benefits and processing complexity. The quality detection and classification is an essential processing step in the process of commercialization. There has been little internal research on the quality detection of potato based on machine vision. Owing to the low accuracy and strong subjectivity, a systematic study is made on the potato external quality based on the machine vision technology, to propose a set of detection criterions for evaluating the potato's external quality comprehensively, including weight, shape and external defects.1. A hardware system based on machine vision is built for identifying potatoes'external quality. The most appropriate light source and background color are determined by a lot of experiments and comparisons. According to the plane calibration of Zhang algorithm, camera calibration is done for correcting image distortion, and its accuracy is able to satisfy the demand of identifying external quality of potatoes.2. Through combining the top view and side elevation,the projection area and side elevation thickness of potato image are extracted to classify potato weight instead of metage. The correlation coefficient of model regression is 0.9836. The detection accuracies of 98%, 96% and 97% can be gained for bigger, normal and smaller potatoes, perspectively.3. In shape detection,Zernike moments are taken as the shape feature.And the study was emphasized on three content,including the calculation of Zernike moments,features selection and shape classification.(1) We propose fast calculation method of radial polynomial in the Zernike moments, which stores the power of polynomials coefficient and radius in the form of matrix. According to parameters n & m of Zernike moments to query calculation, the coefficients calculation and repetitious calculation of the radius power is reduced with a result of improving the calculation speed of Zernike moments obviously. A new image normalization method based on optimum image cut was proposed,this method is to make the object locate in the circle range of r≤0.9 and to be the maximum.The experimental results show that this way can near-perfectly preserve the invariance of scale and rotation for potato image. The fast algorithm for computation of Zernike moments and normalization method are used to calculate the initial shape feature.(2)The classifier selecting principle is investigated by combining with the genetic algorithm. A new feature selection method with the combination of genetic algorithms and probabilistic neural networks is put forward firstly. Comprehensively considering the factor of classification accuracy,selected feature number and the impact of two factors, a new fitness function is proposed. The simulation tests indicate that the fitness function and feature selection method can be used for searching the best feature combination.The initial Zernike moments parameters of potatoes are optimized using improved genetic algorithm, and nineteen Zernike moments are extracted to form the shape feature.(3) Taking SVM as classifier, a new mixed kernel function of RBF and Sigmoid kernel function is proposed, resulting in 93% and 100% detection accuracy, respectively. Its detection accuracy can reach respectively for the perfect and malformation potatoes.4. The external defects of potatoes are segmented by using color component method and SUSAN operator respectively. Meanwhile, the defects decision is realized through setting area threshold.