Agricultural Mechatronics: Orange Sorting System Using Image Segmentation

Haris Imam Karim Fathurrahman, Imam Haris Aulia

Abstract


Sorting oranges after harvest is a critical step. It requires separating ripe fruit from unripe. Traditionally, this is done by hand. This method is inefficient and subjective. It is not suitable for modern agriculture. This study creates an automated system to solve this problem. The system uses mechatronics and image processing. Its core uses the HSV color space for image analysis. This method is effective for assessing the peel's color, which indicates maturity. The mechatronic system performs the physical sorting using a servo motor. It includes a conveyor belt, a digital camera, a processing unit, and an actuator. This research was tested on 30 sample oranges. The results show 90% accuracy in mechatronics sorting. This proves the system is a reliable and effective tool for quality control.

Keywords


Agricultural; HSV; Mechatronics; Orange; Sorting

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References


T. O. Ogunbode, V. I. Esan, M. H. Ayegboyin, O. M. Ogunlaran, E. T. Sangoyomi, and J. A. Akande, “Analysis of farmers’ perceptions on sustainable sweet orange farming in nigeria amid climate change,” Sci. Rep., vol. 15, no. 1, p. 5205, 2025, https://doi.org/10.1038/s41598-025-90056-6.

L. S. Buller, L. C. Ampese, J. M. Costa, and T. Forster-Carneiro, “Revalorization of industrial by-products from frozen concentrated orange juice for a circular economy,” Biomass Convers. Biorefinery, vol. 14, no. 18, pp. 21659–21668, 2024, https://doi.org/10.1007/s13399-023-04218-5.

C. Ertekin and A. Comart, “Energy Analysis of Citrus Production in Turkey and the World,” Appl. Fruit Sci., vol. 66, no. 2, pp. 535–549, 2024, https://doi.org/10.1007/s10341-024-01036-5.

Karl R Gegenfurtner, “Perceptual ripening of oranges,” Iperception., vol. 15, no. 4, p. 20416695241258748, 2024, https://doi.org/10.1177/20416695241258748.

S. Schenck, S. Barrios, and P. Lema, “Preserving freshness and nutrients: the impact of passive and active modified atmosphere packaging on ready-to-eat orange (var. Navel) segments,” Int. J. Postharvest Technol. Innov., vol. 9, no. 2, pp. 146–166, 2024, https://doi.org/10.1504/IJPTI.2024.138700.

D. Singh, A. Singh, M. Rakhra, T. Sarkar, G. S. Cheema, and A. Khamparia, “Predictions on the future of agriculture and recent developments in agricultural technology,” in International Conference On Artificial Intelligence Of Things For Smart Societies, pp. 297–303, 2024, https://doi.org/10.1007/978-3-031-63103-0_31.

S. Talwani, M. Rakhra, and T. Sarkar, “Perspectives on the Future of Agriculture and Recent Developments in Agricultural Technology,” in 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 1–6, 2024, https://doi.org/10.1109/ICRITO61523.2024.10522413.

F. A. Kitole, E. Mkuna, and J. K. Sesabo, “Digitalization and agricultural transformation in developing countries: Empirical evidence from Tanzania agriculture sector,” Smart Agric. Technol., vol. 7, p. 100379, 2024, https://doi.org/10.1016/j.atech.2023.100379.

K. Lochan, A. Khan, I. Elsayed, B. Suthar, L. Seneviratne, and I. Hussain, “Advancements in Precision Spraying of Agricultural Robots: A Comprehensive Review,” IEEE Access, vol. 12, pp. 129447–129483, 2024, https://doi.org/10.1109/ACCESS.2024.3450904.

S. Mahmoudi, A. Davar, and P. Sohrabipour, “Leveraging imitation learning in agricultural robotics : a comprehensive survey and comparative analysis,” no. October, pp. 1–21, 2024, https://doi.org/10.3389/frobt.2024.1441312.

C. Lytridis and T. Pachidis, “Recent advances in agricultural robots for automated weeding,” AgriEngineering, vol. 6, no. 3, pp. 3279–3296, 2024, https://doi.org/10.3390/agriengineering6030187.

Z. Luo, W. Yang, Y. Yuan, R. Gou, and X. Li, “Semantic segmentation of agricultural images: A survey,” Inf. Process. Agric., vol. 11, no. 2, pp. 172–186, 2024, https://doi.org/10.1016/j.inpa.2023.02.001.

E. D. K. Ruby, G. Amirthayogam, G. Sasi, T. Chitra, A. Choubey, and S. Gopalakrishnan, “Advanced image processing techniques for automated detection of healthy and infected leaves in agricultural systems,” Mesopotamian J. Comput. Sci., vol. 2024, pp. 44–52, 2024, https://doi.org/10.58496/MJCSC/2024/006.

M. Muñoz, R. A. G. Morales, and J. A. Sánchez-Molina, “Comparative analysis of agricultural IoT systems: Case studies IoF2020 and CyberGreen,” Internet of Things, vol. 27, p. 101261, 2024, https://doi.org/10.1016/j.iot.2024.101261.

A. K. Saini and A. K. Yadav, “A Comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics,” Eur. J. Agron., vol. 163, p. 127440, 2025, https://doi.org/10.1016/j.eja.2024.127440.

O. A. Elufioye, C. U. Ike, O. Odeyemi, F. O. Usman, and N. Z. Mhlongo, “Ai-Driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture,” Comput. Sci. IT Res. J., vol. 5, no. 2, pp. 473–497, 2024, https://doi.org/10.51594/csitrj.v5i2.817.

D. K. Pandey and R. Mishra, “Towards sustainable agriculture: Harnessing AI for global food security,” Artif. Intell. Agric., vol. 12, pp. 72–84, 2024, https://doi.org/10.1016/j.aiia.2024.04.003.

A. A. Mana, A. Allouhi, A. Hamrani, S. Rehman, I. El Jamaoui, and K. Jayachandran, “Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices,” Smart Agric. Technol., vol. 7, p. 100416, 2024, https://doi.org/10.1016/j.atech.2024.100416.

P. Yao et al., “Designing and Testing a Picking and Selecting Integrated Remote-Operation-Type Dragon-Fruit-Picking Device,” Appl. Sci., vol. 14, no. 11, p. 4786, 2024, https://doi.org/10.3390/app14114786.

N. I. Giannoccaro, G. Rausa, R. Rizzi, P. Visconti, and R. De Fazio, “An Innovative Vision-Guided Feeding System for Robotic Picking of Different-Shaped Industrial Components Randomly Arranged,” Technologies, vol. 12, pp. 1–26, 2024, https://doi.org/10.3390/technologies12090153.

J. Xiang, L. Wang, L. Li, K.-H. Lai, and W. Cai, “Classification-design-optimization integrated picking robots: A review,” J. Intell. Manuf., vol. 35, no. 7, pp. 2979–3002, 2024, https://doi.org/10.1007/s10845-023-02201-5.

B. Jackvony and M. Jouaneh, “Building an Educational Automated Mechatronics-Based Sorting System,” Automation, vol. 5, no. 3, pp. 297–309, 2024, https://doi.org/10.3390/automation5030018.

K. T. Putra, T. K. Hariadi, S. Riyadi and A. N. N. Chamim, "Feature Extraction for Quality Modeling of Malang Oranges on an Automatic Fruit Sorting System," 2018 2nd International Conference on Imaging, Signal Processing and Communication (ICISPC), pp. 74-78, 2018, https://doi.org/10.1109/ICISPC44900.2018.9006688.




DOI: https://doi.org/10.31763/simple.v7i2.142

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Signal and Image Processing Letters
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