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

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.