title: [Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].
abstract: The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.forename: Xiao-yu
authors.initials: XY
authors.lastname: Li
authors.forename: Meng-ling
authors.initials: ML
authors.lastname: Xu
authors.forename: Rui
authors.initials: R
authors.lastname: Jin
authors.forename: Jing
authors.initials: J
authors.lastname: Ku
authors.forename: Sen-miao
authors.initials: SM
authors.lastname: Xu
authors.forename: Zhen-zhong
authors.initials: ZZ
authors.lastname: Wu
mesh_terms: D000383:Agriculture; D000465:Algorithms; D008962:Models, Theoretical; D010935:Plant Diseases; D011198:Solanum tuberosum; D013057:Spectrum Analysis; D060388:Support Vector Machine