Study On Nondestructive Detection Method Of Potato Grading Based On Multi-source Information Fusion

Detection of potato external quality is directly related to the processing and utilization rate and growth rate, and the commercial production and processing of potato is the first step. In recent years, although the machine vision and near infrared spectroscopy has respectively made certain progress in the study of detection and classification for external and internal quality of potato, but the detection is not at the same time. Aiming at this problem, the paper uses the technology of machine vision and near infrared spectroscopy, studied the nondestructive detection method of potato based on multi-source information fusion technique. Using Ke Xin Yi Hao potatoes as the research object,5kinds of sample set include deformity, black heart, mechanical damage, eye and normal. Characteristics extraction method of potato images and spectra,and the fusion method for potato detection, are studied, at the same time the potato fusion classification model is established.1) To avoid the interference of background on potato image segmentation, the paper proposed a segmentation method based on visual saliency and the hue dimension, and compared it with the gray segmentation method and the hue dimension segmentation. The gray segmentation method due to its inability complete of potato area, is not suitable for online potato image segmentation. While the hue dimension segmentation and Saliency-H dimension segmentation method can segment the complete potato area, among them Saliency-H dimension segmentation method has great advantage in the segmentation speed, data compression and potato positioning. The simulation on Matlab platform shows, the average time-consuming for hue dimension segmentation method is551.7ms, Saliency-H method reduce74ms, only needs477.7ms.2) Because of the uneven gray of potato surface, image features are difficult to be effectively covered potato samples, the paper proposed the gray gradient and manifold learning combination way for extracting potato image features, models which build are compared. The gray gradient algorithm is adopted in this paper for the Freeman chain code and histogram of oriented gradients, isometric mapping and principal component analysis for manifold learning algorithms. Histogram of oriented gradients and principal component analysis is the best combination of image features, which modeling required only23dimensional feature (feature10dimensional direction the gradient histogram features and13dimensional principal component) to achieve the optimal model.3) Potato grading and detection model based on machine vision is established. The gray gradient and manifold learning characteristics in different combinations for potato external quality (deformity, mechanical damage and eye) classification capacity is higher than the internal quality (black heart), in which histogram of oriented gradients and principal component features is the best combination for modeling. The recognition rates of deformity, mechanical injury and eye sample were93.75%,83.33%and95.45%, and the recognition rates of black heart and the normal sample were only77.27%and71.43%.4)A potato external quality online detection system is builded based on LabVIEW platform. After detect38potato samples with different external quality, identification rate of deformity, mechanical damage, eye and qualified samples was89.47%. Tuber shape is100%, and the long diameter, short diameter and height of the maximum error are respectively2.9mm,2.0mm and1.0mm. Average time for a single image100ms. Results show that the proposed potato image segmentation, feature extraction and pattern recognition methods can realize online detection of potato external quality indicators.5) Effects of band selection algorithms and manifold learning algorithms on potato grading model were compared. Band selection algorithms in the paper include genetic algorithm and successive projections algorithm, and manifold learning algorithms include Laplasse feature mapping method, kernel principal component analysis and principal component. Potato grading model using the principal component analysis of spectral feature is the optimal. The optimal pretreatment method is MSC, and the best number of principal components is20. The recognition rate for training set is97.88%, and83.87%for test set. The results shows that for potato spectral feature extraction, manifold learning algorithm is better than the band selection algorithm.6) Potato grading and detection model based on near infrared spectrum technique is established. Either Band selection algorithm or manifold learning algorithm, the recognition rate of internal quality for potato (black heart and eye) are higher than the external quality (deformity and mechanical damage), the principal component feature model is the optimal, in which the black heart and eye2types of samples has high recognition rates90.91%and95.45%, while the deformity, mechanical damage and the normal sample were only75%,75%and76.19%.7) Use Lab VIEW to establish the potato internal quality detection model based on near infrared spectroscopy. For black heart, eye and qualified potatoes, the identification rate of near infrared reflectance spectroscopy was95.45%. For algorithm execution efficiency, time-consuming of each pretreatment of near infrared reflectance spectroscopy is3.4ms,20dimensional principal component extraction time is14.6ms, the modeling time5137ms. Using the model test of single spectra, average time was15.0ms, the detection efficiency can achieve30/s, which provides technical support for only using a near-infrared spectroscopy model online detection of potato internal quality indicators.8) The fusion method for the detection of potato external quality is determined, and effects of different fusion methods on modeling results for potato grading and detection are compared. In decision level fusion, the probability outputs of machine vision and near infrared spectra based on support vector are gained. Using DS evidence theory method for decision level fusion, decision fusion model is established. Recognition rate of training set is100%, for test set is93.55%. In feature level fusion, the combination of histogram of oriented gradients and principal component analysis is the image features extraction method, and the principal component is used for spectral feature extraction, using image and spectral features as the inputs of pattern recognition, feature level fusion models respectively established by Adaboost and support vector machine. For AdaBoost model, recognition rate of training set is100%, of test set is91.40%. For support vector machine model, recognition rate of training set is100%, of test set is95.70%. Results showed that the detection for potato’s several quality indexes, support vector machine feature fusion and is better than DS decision fusion, and DS decision fusion outperforms AdaBoost feature level fusion, feature level fusion.9) Potato grading and detection model based on multi-information fusion technology is established, which can detect a number of quality indicators of potato. Recognition rates of DS evidence theory model for deformity, black heart, mechanical damage, eye and normal sample are respectively100%,95.45%,83.33%,100%and85.71%. Recognition rates of Adaboost model of deformity, black heart, mechanical damage, eye and normal sample are respectively100%,95.45%,83.33%,100%and76.19%. Recognition rates of support vector machine model for deformity, black heart, mechanical damage, eye and normal sample are respectively100%,95.45%,91.67%,100%and90.48%. Results showed that the fusion model identification for potato were superior to machine vision and near infrared spectra, the support vector machine model is the best, recognition rates of the deformity, black heart, mechanical damage, eye and normal samples reaches more than90%, which providing technical support for multi-source information fusion technology online detection of a number of potato quality indicators.10) Using LabVIEW For the testing of image segmentation, feature extraction, image preprocessing, feature extraction, average total time of each sample is less than140ms, can achieve7per second speed of processing image and near infrared spectral data. The results show that, multi-source information fusion identification model is better than machine vision and near infrared spectroscopy, which provides the technical support for potato external quality indicators online detection by using multi-source information fusion technology.