Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels
- Nurchaya Simanjuntak Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
-
Raymond Erz Saragih
*
Universitas Universal, Batam,
Indonesia
- Yonky Pernando Universitas Universal, Batam, Indonesia (*) Corresponding Author
Abstract
In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the potential of employing YOLOv8, a cutting-edge deep learning model, to automate Ambarella fruit ripeness classification. This work focuses on the YOLOv8n, YOLOv8s, and YOLOv8m, lightweight models within the YOLOv8 family. Our results are promising: all three models achieved 100% accuracy on the training set, with YOLOv8s demonstrating the lowest loss at 0.00286. The web application was utilised to deploy the trained models, allowing users to upload images of Ambarella fruit and run the model for inference.
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References
N. Ngadi et al., “Challenge of Agriculture Development in Indonesia: Rural Youth Mobility and Aging Workers in Agriculture Sector,” Sustainability 2023, Vol. 15, Page 922, vol. 15, no. 2, p. 922, Jan. 2023, doi: 10.3390/SU15020922.
F. Sarie and I. Harsono, “Measuring the Impact of Pesticide Use, Labor Availability, and Agricultural Technology on Vegetable Farming Efficiency in Central Java,” West Science Interdisciplinary Studies, vol. 2, no. 01, pp. 244–253, Jan. 2024, doi: 10.58812/WSIS.V2I01.615.
M. M. Lubis, M. Abror, and J. Djuhjana, “Effect of fertilizer discount on fresh fruit bunches production and Nutrients in North Sumatra Indonesia,” IOP Conf Ser Earth Environ Sci, vol. 1308, no. 1, p. 012004, Feb. 2024, doi: 10.1088/1755-1315/1308/1/012004.
É. M. dos Santos et al., “Spondias sp: Shedding Light on Its Vast Pharmaceutical Potential,” Molecules 2023, Vol. 28, Page 1862, vol. 28, no. 4, p. 1862, Feb. 2023, doi: 10.3390/MOLECULES28041862.
F. Jemziya, A. M. Rikasa, B. S. G. M. Basnayake, M. B. F. Jemziya, R. T. B. Rambodagalla, and A. M. Rikasa, “Development of Ambarella (Spondias dulsis) fruit pulp incorporated ice cream,” 2022, Accessed: Jan. 02, 2024. [Online]. Available: https://www.researchgate.net/publication/370939069
I. Hayati, A. Hartana, N. Ratna Djuita, and N. Sri Ariyanti, “Morphological Variation of Kedondong (Spondias Dulcis Parkinson) in Central Part of Sumatra,” Floribunda, vol. 6, no. 8, Apr. 2022, doi: 10.32556/floribunda.v6i8.2022.375.
P. Tyagi, R. Semwal, A. Sharma, U. S. Tiwary, and P. Varadwaj, “E-nose: A low-cost fruit ripeness monitoring system,” Journal of Agricultural Engineering, vol. 54, no. 1, Nov. 2022, doi: 10.4081/jae.2022.1389.
M. Rizzo, M. Marcuzzo, A. Zangari, A. Gasparetto, and A. Albarelli, “Fruit ripeness classification: A survey,” Artificial Intelligence in Agriculture, vol. 7, pp. 44–57, Mar. 2023, doi: 10.1016/J.AIIA.2023.02.004.
P. L. I. Jayarathna, J. A. E. Jayawardena, and C. Vanniarachchy, “Identification of Physical, Chemical Properties and Flavor Profile of Spondias dulcis in Three Maturity Stages,” International Research Journal of Advanced Engineering and Science, vol. 5, no. 1, pp. 208–211, 2020, doi: 10.5281/zenodo.3695472.
P. Siricharoen, W. Yomsatieankul, and T. Bunsri, “Fruit maturity grading framework for small dataset using single image multi-object sampling and Mask R-CNN,” Smart Agricultural Technology, vol. 3, p. 100130, Feb. 2023, doi: 10.1016/J.ATECH.2022.100130.
M. Momeny et al., “Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy,” Comput Biol Med, vol. 141, p. 105175, Feb. 2022, doi: 10.1016/J.COMPBIOMED.2021.105175.
H. Khosravi, S. I. Saedi, and M. Rezaei, “Real-time recognition of on-branch olive ripening stages by a deep convolutional neural network,” Sci Hortic, vol. 287, p. 110252, Sep. 2021, doi: 10.1016/J.SCIENTA.2021.110252.
M. Tools, B. Xiao, M. Nguyen, and W. Qi Yan, “Fruit ripeness identification using YOLOv8 model,” Multimedia Tools and Applications, vol. 83, no. 9, pp. 28039–28056, 2023, 123AD, doi: 10.1007/s11042-023-16570-9.
L. Chuquimarca, B. Vintimilla, and S. Velastin, “Banana Ripeness Level Classification Using a Simple CNN Model Trained with Real and Synthetic Datasets,” INSTICC, Mar. 2023, pp. 536–543. doi: 10.5220/0011654600003417.
“Deep Learning for Faces on Orphanage Children Face Detection.” Accessed: May 31, 2024. [Online]. Available: https://jurnal.stmikroyal.ac.id/index.php/jurteksi/article/view/1858
J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Intelligent Systems and Applications,” Intelligent Systems and Applications, vol. 2, pp. 52–61, 2021, doi: 10.5815/ijisa.2021.02.04.
E. Tapia-Mendez, I. A. Cruz-Albarran, S. Tovar-Arriaga, and L. A. Morales-Hernandez, “Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables,” Applied Sciences, vol. 13, no. 22, p. 12504, Nov. 2023, doi: 10.3390/app132212504.
A. Ajil, A. Ali, A. K. K. Reddy, L. V. C. Reddy, A. V. G. S. Reddy, and O. Goud, “Fruit Ripeness Assertion Using Deep Learning,” International Journal of Human Computations & Intelligence, vol. 2, no. 2, pp. 63–72, Apr. 2023, doi: 10.5281/ZENODO.7900479.
R. E. Saragih, Y. Roza, and A. Rezki Purnajaya, “Ambarella Fruit Ripeness Classification based on EfficientNet Models,” 2022.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Jun. 2015, [Online]. Available: http://arxiv.org/abs/1506.02640
M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Machines 2023, Vol. 11, Page 677, vol. 11, no. 7, p. 677, Jun. 2023, doi: 10.3390/MACHINES11070677.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020, Accessed: Sep. 07, 2023. [Online]. Available: http://arxiv.org/abs/2004.10934
G. Jocher, “Ultralytics YOLOv5.” 2020. doi: 10.5281/zenodo.3908559.
C. Li et al., “YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,” Sep. 2022, Accessed: Sep. 07, 2023. [Online]. Available: https://arxiv.org/abs/2209.02976v1
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Jul. 2022, Accessed: Sep. 07, 2023. [Online]. Available: https://arxiv.org/abs/2207.02696v1
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8.” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
T. Akter et al., “Improved transfer-learning-based facial recognition framework to detect autistic children at an early stage,” Brain Sci, vol. 11, no. 6, 2021, doi: 10.3390/brainsci11060734.
R. Mishra, S. Goyal, T. Choudhury, and T. Sarkar, “Banana ripeness classification using transfer learning techniques,” Proceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022, 2022, doi: 10.1109/IC3SIS54991.2022.9885244.
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