A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.

Autor: Yang L; School of Mechanical and Electrical Engineering, Shihezi University, Xinjiang, China., Nasrat LS; Electrical Power Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt., Badawy ME; Agricultural Engineering Research Institute, Giza, Egypt., Mbadjoun Wapet DE; National Advanced School of Engineering, Universit´e de Yaound´e I, Yaound´e, Cameroon., Ourapi MA; Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt., El-Messery TM; International Research Centre 'Biotechnologies of the Third Millennium', Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, Russia., Aleksandrova I; International Research Centre 'Biotechnologies of the Third Millennium', Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, Russia., Mahmoud MM; Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan, Egypt., Hussein MM; Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan, Egypt.; Department of Communications Technology Engineering, Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq., Elwakeel AE; Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Mar 28; Vol. 19 (3), pp. e0301294. Date of Electronic Publication: 2024 Mar 28 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0301294
Abstrakt: Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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