Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data
Computers and electronics in agriculture, 2008•Elsevier
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�…
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�…
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).
Elsevier