Application of Color Sensor in the Determination of Tomato Fruit Ripeness (Solanum Lycopersicum, L) in Gravitation Type Fruit Sorting Tool (Gravitation Type)
DOI:
https://doi.org/10.32734/injar.v2i1.862Keywords:
colour sensor, level of accuracy, microcontroller, sortationAbstract
Sortation is an important step in handling post-harvest fruits to extend the shelf life and increase the selling value. A tool that is able to sort quickly is needed to speed up the sortation process. Therefore, design of fruit sortationtool using gravitation type and technology to determine fruit ripeness is needed. This study aimed to design and fabricate fruit sorting tool based on the fruit size that could help farmers in sorting fruit. The performance testing was conducted using tomatoes. The results of the design shows that this tool could accommodate 12 kg (equal to 60 tomatoes) of fruit. Tool performance testing shows that this tool could work effectively on a slope of 12˚ withan effective capacity of 133.1 kg/hour (80%). Damage analysis showed that the ripe fruit was more susceptible to damage than the half-ripe fruit due to the fact that half-ripe fruit was still hard, so that the percentage of the damage analysis in ripe fruit was 23.3% and half-ripewas 10%.
Downloads
References
N. Ntagkas, E. Woltering, C. Nicole, C. Librie, and L. F. M. Marcelis, “Light regulation of vitamin C in tomato fruit is mediated through photosynthesis,” Enviroment and Experimental Botany, vol. 158, pp. 80-188, 2019.
M. M. Bordbar, J. Tashkhourian, and B. Hemmateenejad, “Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colourimetric sensor array,” Chemical, vol. 257, pp. 783-791, 2018.
Y. Zhao, L. Gong, B. Zhou, Y. Huang, and C. Liu, “Detecting tomatoes in greenhouse scenes by combining boost classifier and colour analysis,” Biosystem Engineering, vol. 148, pp. 127-137, 2016.
H. F. Hawari, N. M. Samsudin, M. N. Ahmad, A. Y. M. Shakaff, S. A. Ghani, Y. Wahad, S. K. Za’aba, and T. Akitsu, “Array of MIP Based Sensor for Fruit Maturity Assessment,” Procedia Chemistry, vol. 6, pp. 100-109, 2012.
S. Tu, Y. Xue, C. Zheng, Y. Qi, H, Wan, and L. Mau, “Detection of passion fruits and maturity classification using red-green-blue depth images,” Biosystem Engeneering, vol. 175, pp. 156-157, 2018.
N. Bertin and M. Genard, “Tomato quality as influence by preharvest factors,” Scientia Horticulturae, vol. 233, pp. 264:276, 2018.
Published
How to Cite
Issue
Section
Copyright (c) 2019 Indonesian Journal of Agricultural Research
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.