Evaluating the optimal working parameters of the color sensor TCS3200 in the fresh chili destemming system

Huynh, Quoc-Khanh; Nguyen, Chi Hong; Tran, Nguyen Phuong Lan; Vo, Nguyen Hong Phuc; Huynh, Thanh Thuong; Nguyễn, Văn Minh

doi:10.22144/ctu.jen.2022.004

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…Thus, programming the RGB color sensor involves relying on the red and green color channels only and avoiding the blue color channel. Huynh et al [ 44 ] recommended that the speed of 7.2 m/min is considered as the maximum operating speed of the conveyor belt which can ensure the operation of the sensor.…”

Section: Resultsmentioning

confidence: 99%

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

Yang,

Nasrat,

Badawy

et al. 2024

PLoS ONE

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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.

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Section: Resultsmentioning

confidence: 99%

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

Yang,

Nasrat,

Badawy

et al. 2024

PLoS ONE

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“…Thirty apple were tested three times, for a total of 90 tests. This test was conducted at a conveyor belt speed of 0.1 m/s, as recommended by Khanh et al [31]. The two RGB color sensors were installed at a specified height of 30 mm and at a light intensity of 25 lux.…”

Section: Conveyor Belt Colormentioning

confidence: 99%

“…The light intensity during the scanning of fruits to be sorted has a direct impact on the accuracy and quality of the process. During this test, the conveyor belt speed was set at 0.1 m/s as recommended by Khanh et al [31], and the two RGB color sensors were fixed at 30 mm height. Utilizing attached lamps on the RGB color sensor board, the test was conducted using different light intensities of 25, 40, and 50 lux.…”

Section: Light Intensitymentioning

confidence: 99%

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Designing, Optimizing, and Validating a Low-Cost, Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits

Elwakeel,

Mazrou,

Tantawy

et al. 2023

Agriculture

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The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed easily, it is inconsistent, time-consuming, expensive, and highly influenced by the surrounding environment. In this regard, this study aimed to design and optimize the performance of a low-cost, multi-purpose, automatic RGB color-based sensor for sorting fruits. The proposed automatic color sorting system consists of hardware components including a machine frame, belt and pulleys, conveyor belt, scanning zone, plastic boxes, electric components (stepper motors, RGB color sensors, Arduino Mega, motor drivers), and software components (Arduino IDE version 2.2.1 and C++). Calibration was performed for the light intensity sensor to measure the light intensity inside the scanning zone, the conveyor speed sensor, and the RGB color sensors by testing the RGB color channels. The sensor, the height, conveyor belt color, and light intensity should be carefully adjusted to ensure a high performance of the color-based sorting system. The results showed that the appropriate sensor height ranged from 15 to 30 mm, the optimum color of the conveyor belt was black, and scanning the objects at a light intensity of 25 lux achieved the best output signals. The RGB color sensors achieved an analytical performance similar to that obtained with manual sorting without requiring the use of computers for image processing like other automatic sorting systems do in order to gather RGB data.

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“…This sensor module's digital inputs and outputs enable a direct connection to a microcontroller or other logic circuit [12]. We are able to determine the intensity of each color by using various filters [13].…”

Section: Figure 2 Tcs3200 Sensorsmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer

Susanti

Aidha²,

Yondri³

et al. 2022

AJEEET

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This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.

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Evaluating the optimal working parameters of the color sensor TCS3200 in the fresh chili destemming system

Cited by 5 publications

References 3 publications

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

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

Designing, Optimizing, and Validating a Low-Cost, Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits

The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer

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