Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data

被引:62
作者
Baltazar, Arturo [1 ]
Aranda, Jorge Isidro [2 ]
Gonzalez-Aguilar, Gustavo [3 ]
机构
[1] CINVESTAV Saltillo, Robot & Adv Mfg Program, Ctr Invest & Estudios Avanzados, Ramos Arizpe, Coahuila, Mexico
[2] Univ Michoacana, Fac Ciencias Fisicomatemat, Morelia, Michoacan, Mexico
[3] Tecnol & Alimentos Origen Vegetal, AC CIAD, Hermosillo, Sonora, Mexico
关键词
Bayesian classifier; data fusion; impact acoustic technique; tomatoes; ripening;
D O I
10.1016/j.compag.2007.07.005
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
In this work, the concept of data fusion is applied to nondestructive testing data for classification of fresh intact tomatoes based on their ripening stages. A Bayesian classifier considering a multivariate, three-class problem was incorporated for data fusion. Probability of error was estimated numerically for univariate and multivariate cases based on Bhattacharyya distance. Numerical results showed that multi-sensorial data fusion reduces the classification error considerably. The Bayesian classifier was tested on data of tomato fruits taken by the following nondestructive tests: colorimeter and acoustic impact. Results of Bayesian classifier agree with numerical estimations showing an 11% classification error in the multivariate (multi-sensor) case compared with a 48% obtained by the univariate case (single sensor). (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:113 / 121
页数:9
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