Classification of Apples Based on the Shelf Life Using ANN and Data Fusion

Abstract

In this research, two groups of gala variety apples were stored in two conditions of storage for 10 weeks: the first group at 0 °C with 95% relative humidity and the second group at 20 °C with 40% relative humidity, and each week, 10 samples of both groups were selected for experiments. After weighing and measuring the dimensions of the apples, an artificial intelligence and data fusion model was used to classify the apples based on shelf life. The non-destructive acoustic test was performed with a pendulum impactor, which impact signals simultaneously detected by sound and vibration sensors. The acoustic and vibrational signals of the impact were converted from the time domain to the frequency domain using a fast Fourier transform. Then, the pattern recognition technique using the artificial neural network was used to classify the fruits based on the shelf life at 1, 2, and 3week intervals. The dominant acoustic and vibrational frequencies and masses of the samples as three features delivered to the artificial neural network and the shelf life of samples were estimated by individual, binary, and trinary set of features. The binary and trinary set of features are used as mid-level fusion by ANN. The individual features classification results were fused by the conventional and Yager’s modified Dempster-Shafer method as the high-level fusion. In the feature-level fusion, which was done using the artificial neural network, the classification accuracy of the first group increased by an average of 10.84% and the second group by 10.14%. In the decision fusion, the lowest and highest increases of accuracy by the Yager and Dempster-Shafer methods were 1.6 and 19.8% for the first group and 3.7 and 12.5% for the second group, respectively.