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Fusion of acoustic sensing and deep learning techniques for apple mealiness detection

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Abstract

Mealiness in apple fruit can occur during storage or because of harvesting in an inappropriate time; it degrades the quality of the fruit and has a considerable role in the fruit industry. In this paper, a novel non-destructive approach for detection of mealiness in Red Delicious apple using acoustic and deep learning techniques was proposed. A confined compression test was performed to assign labels of mealy and non-mealy to the apple samples. The criteria for the assignment were hardness and juiciness of the samples. For the acoustic measurements, a plastic ball pendulum was used as the impact device, and a microphone was installed near the sample to record the impact response. The recorded acoustic signals were converted to images. Two famous pre-trained convolutional neural networks, AlexNet and VGGNet were fine-tuned and employed as classifiers. According to the result obtained, the accuracy of AlexNet and VGGNet for classifying the apples to the two categories of mealy and non-mealy apples was 91.11% and 86.94%, respectively. In addition, the training and classification speed of AlexNet was higher. The results indicated that the suggested method provides an effective and promising tool for assessment of mealiness in apple fruit non-destructively and inexpensively.

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Notes

  1. The ImageNet dataset contains 1,281,167 training images and 50,000 test images, with each image labeled with one of 1000 classes (Deng et al. 2009).

  2. A mini-batch is a subset of the training set that is used to evaluate the gradient of the loss function and update the weights.

  3. An epoch is a full training cycle on the entire training data set.

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Acknowledgments

We would like to thank Arak University, Arak, Iran, for providing facilities and financial support for this project.

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Authors and Affiliations

  1. Department of Mechanical Engineering of Biosystems, Arak University, Arak, 38156-8-8349, Iran

    Majid Lashgari, Abdullah Imanmehr & Hamed Tavakoli

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  1. Majid Lashgari
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  2. Abdullah Imanmehr
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  3. Hamed Tavakoli
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Correspondence to Hamed Tavakoli.

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Lashgari, M., Imanmehr, A. & Tavakoli, H. Fusion of acoustic sensing and deep learning techniques for apple mealiness detection. J Food Sci Technol 57, 2233–2240 (2020). https://doi.org/10.1007/s13197-020-04259-y

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