Analysis of mango fruit surface temperature using thermal imaging and deep learning

Pathmanaban Pugazhendi; Gnanavel Balakrishnan Kannaiyan; Shanmuga Sundaram Anandan; Chermadurai Somasundaram

doi:10.1515/ijfe-2022-0302

Artikel

Analysis of mango fruit surface temperature using thermal imaging and deep learning

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Veröffentlicht/Copyright: 12. Juni 2023

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Aus der Zeitschrift International Journal of Food Engineering Band 19 Heft 6

Abstract

Thermal imaging has the potential to measure the object’s surface temperature. This study investigated the thermal behavior of mango fruit stored in a refrigerated environment. Thermal images of the fruit were collected with sufficient quality by supplying hot air to the acquisition environment. Grey-Level Co-occurrence Matrix (GLCM) features of mango images were determined to distinguish the subtle and noticeable changes. The thermal images were analyzed to find the temperature difference between the different regions of the fruit. The temperature of the bruise boundary (T_bd) was higher than the bruised center (T_C) throughout the storage period. In addition, an enhanced deep-learning model was used to predict the damaged mango. Over 10 days, 3500 thermal images were obtained from the 400 mangoes. In that, 80 % of the images were used for training, 10 % for testing, and 10 % for validation. The model achieved a classification accuracy of 99.6 %.

Keywords: deep learning; GLCM feature; mango fruit; surface temperature; thermal image

Corresponding author: Pathmanaban Pugazhendi, Velammal Engineering College, Chennai, Tamilnadu, India, E-mail: pathmanaban@velammal.edu.in

Acknowledgments

The authors would like to acknowledge the Department of Computer Science Engineering and Food Technology of Velammal Engineering College, Easwari Engineering College and Sree Sastha institute of Engineering and Technology (cold Storage) for providing the facilities for this work, and Mrs.K.Vaishnavi of the English Department of Velammal Engineering College for proofreading the manuscript.

Author contributions: Pathmanaban P: Conceptualization; Investigation; Writing – original draft; Validation; Methodology; Visualization. Gnanavel B.K: Supervision; Data curation; Validation; Methodology. Shanmuga Sundaram Anandan: Writing – review & editing; Visualization; Software; Formal analysis; Project administration; Resources; Methodology; Validation. S. Chermadurai: Writing – review & editing; Visualization; Software; Formal analysis; Project administration;
Research funding: No funding was received for this study.
Conflict of interest statement: P. Pathmanaban declares that he has no conflict of interest. Gnanavel B. K. declares that he has no conflict of interest. Shanmuga Sundaram Anandan declares that he has no conflict of interest. S. Chermadurai declares that he has no conflict of interest.
Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Ethics approval: This article does not contain any studies with humans or animals.
Consent to participate: Not applicable.

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Received: 2022-11-18

Accepted: 2023-05-27

Published Online: 2023-06-12

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Artikel in diesem Heft

https://doi.org/10.1515/ijfe-2022-0302

Schlagwörter für diesen Artikel

deep learning; GLCM feature; mango fruit; surface temperature; thermal image