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Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

Author

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  • Sandeep Jagtap

    (Centre for SMART, Loughborough University, Loughborough LE11 3TU, UK)

  • Chintan Bhatt

    (Department of Computer Engineering, Charotar University of Science and Technology, Anand, Gujarat 388421, India)

  • Jaydeep Thik

    (Department of Computer Engineering, Charotar University of Science and Technology, Anand, Gujarat 388421, India)

  • Shahin Rahimifard

    (Centre for SMART, Loughborough University, Loughborough LE11 3TU, UK)

Abstract

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions.

Suggested Citation

  • Sandeep Jagtap & Chintan Bhatt & Jaydeep Thik & Shahin Rahimifard, 2019. "Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach," Sustainability, MDPI, vol. 11(11), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3173-:d:237582
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    References listed on IDEAS

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    1. Juliane Jörissen & Carmen Priefer & Klaus-Rainer Bräutigam, 2015. "Food Waste Generation at Household Level: Results of a Survey among Employees of Two European Research Centers in Italy and Germany," Sustainability, MDPI, vol. 7(3), pages 1-21, March.
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    3. Dion M. F. Frampton & Nawshad Haque & David I. Verrelli & Geoff J. Dumsday & Kim Jye Lee-Chang, 2021. "Process Design Guided by Life Cycle Assessment to Reduce Greenhouse Gas-Related Environmental Impacts of Food Processing," Sustainability, MDPI, vol. 13(12), pages 1-14, June.
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    9. Tamíris Pacheco da Costa & James Gillespie & Katarzyna Pelc & Abi Adefisan & Michael Adefisan & Ramakrishnan Ramanathan & Fionnuala Murphy, 2022. "Life Cycle Assessment Tool for Food Supply Chain Environmental Evaluation," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    10. Guk-Jin Son & Dong-Hoon Kwak & Mi-Kyung Park & Young-Duk Kim & Hee-Chul Jung, 2021. "U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    11. de Souza, Michele & Pereira, Giancarlo Medeiros & Lopes de Sousa Jabbour, Ana Beatriz & Chiappetta Jabbour, Charbel Jose & Trento, Luiz Reni & Borchardt, Miriam & Zvirtes, Leandro, 2021. "A digitally enabled circular economy for mitigating food waste: Understanding innovative marketing strategies in the context of an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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