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Identification of Mango Fruit Maturity Using Robust Industrial Devices and Open-Source Devices Applying Artificial Vision

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1675))

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Abstract

This article is about the communication between the programmable logic controller and the Raspberry Pi device. Due to technological advances at the Industrial level in recent years, the popularity of this interconnection has increased due to the importance of intelligent systems and the Industrial Internet of Things (IIoT). This project applied intelligent automation through a low-cost device with high efficiency, performance, precision, and monitoring in industrial processes in real-time, in order to identify mango fruit maturity according to its color. The system detects green color for unripe fruit and yellow color for ripe fruit, using computer vision techniques through an intelligent device (Raspberry Pi) and an inexpensive sensor (Pi camera module). The connection of the Siemens s7–1200 PLC with RPi3 through the Python-Snap7 library allows the transfer of information, considering the Profinet industrial communication protocol. RPi3 is used as a control node of the network through the PuTTY application to transfer data to the Siemens s7–1200 PLC, using the SSH protocol for the connection. Therefore, this work achieves real-time monitoring with low latency and high precision between the detection process and the automation of the actuators through the programmable logic controller.

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Authors and Affiliations

  1. Facultad de Sistemas y Telecomunicaciones, Universidad Estatal Península de Santa Elena, Santa Elena, Ecuador

    Samuel Baldomero Bustos Gaibor, Luis Enrique Chuquimarca Jiménez, José Miguel Sánchez Aquino, Carlos Alberto Saldaña Enderica & David Eduardo Sánchez Espinoza

Authors
  1. Samuel Baldomero Bustos Gaibor
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  2. Luis Enrique Chuquimarca Jiménez
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  3. José Miguel Sánchez Aquino
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  4. Carlos Alberto Saldaña Enderica
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  5. David Eduardo Sánchez Espinoza
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Corresponding author

Correspondence to Luis Enrique Chuquimarca Jiménez .

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Editors and Affiliations

  1. Universidad Estatal Península de Santa, La Libertad, Ecuador

    Teresa Guarda

  2. University of Minho, Guimarães, Portugal

    Filipe Portela

  3. BITrum Research Group, Leon, Spain

    Maria Fernanda Augusto

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Bustos Gaibor, S.B., Chuquimarca Jiménez, L.E., Sánchez Aquino, J.M., Saldaña Enderica, C.A., Sánchez Espinoza, D.E. (2022). Identification of Mango Fruit Maturity Using Robust Industrial Devices and Open-Source Devices Applying Artificial Vision. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_17

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  • DOI: https://doi.org/10.1007/978-3-031-20319-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20318-3

  • Online ISBN: 978-3-031-20319-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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