Real-time detection of fruit ripeness using the YOLOv4 algorithm

Widyawati Widyawati; Reni Febriani

doi:10.36055/tjst.v17i2.12254

Real-time detection of fruit ripeness using the YOLOv4 algorithm

Widyawati Widyawati, Reni Febriani

Abstract

Indonesia is a country that has many types of commodities of various kinds of fruit. Based on BPS data, fruit production reached 22 million tons in 2019, an increase of 5% compared to 2018. However, with such a large production volume, most fruit inspection (sorting) processes in Indonesia are still carried out manually by humans. The manual process requires a lot of time and energy resources, is prone to inconsistencies and inaccuracies. With the help of computer vision, the automation process is expected to be able to eliminate manual processes to reduce costs and increase efficiency and accuracy. In this study, the method used is the YOLOv4 algorithm. The algorithm is applied to detect banana ripeness automatically. The algorithm training process is carried out using 369 banana images divided into two classes, and the testing process is carried out on videos captured in real-time. Based on the research results, the best average accuracy rate is 87.6%, and the video processing speed is 5 FPS (frames per second) using a single-GPU architecture.

Indonesia merupakan negara yang memiliki banyak jenis komoditi berbagai macam buah-buahan. Berdasarkan data BPS, produksi buah mencapai 22 juta ton pada 2019 atau meningkat 5% dibandingkan 2018. Namun dengan volume produksi sebesar itu, sebagian besar proses pemeriksaan (sorting) buah di Indonesia masih dilakukan secara manual oleh manusia. Proses manual membutuhkan banyak waktu dan sumber daya, rentan terhadap inkonsistensi dan ketidakakuratan. Proses otomatisasi dengan bantuan computer vision diharapkan dapat menghilangkan proses manual sehingga dapat menekan biaya, meningkatkan efisiensi dan akurasi. Dalam penelitian ini, metode yang digunakan adalah algoritma YOLOv4. Algoritma ini diterapkan untuk mendeteksi kematangan pisang secara otomatis. Proses pelatihan algoritma dilakukan dengan menggunakan 369 citra pisang yang dibagi menjadi dua kelas dan proses pengujian dilakukan pada video yang ditangkap secara real time. Berdasarkan hasil penelitian, tingkat akurasi rata-rata terbaik adalah 87,6% dan kecepatan pemrosesan video adalah 5 FPS (frame per seconds) dengan menggunakan arsitektur single-GPU.

Keywords

Convolutional neural network, YOLOv4, fruit ripeness detection.

Full Text:

PDF

References

Kaido, B., & Katsuhito, F. (2020). Supply chain and value-added distribution of pineapple fruit in Muaro Jambi Regency, Jambi province, Indonesia. International Journal of Research in Economics and Social Sciences (IJRESS), vol. 10, no. 2, pp. 50-57.

Badan Pusat Statistik. (2020). Produksi Tanaman Buah-buahan 2020. Accesssed at 1 April 2021. Available online on https://www.bps.go.id/indicator/55/62/1/produksi-tanaman-buah-buahan.html.

Food and Agriculture Organization of the United Nations. (2011). Global Food Losses and Food Waste: Extent, Causes, and Prevention. Rome: Food and Agriculture Organization UN.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Amsterdam: Springer.

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587.

Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision, pp. 1440-1448.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788.

Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, vol. 6, no. 100134, pp. 1-13.

Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., & Pattabiraman, V. (2021). Comparative analysis of deep learning image detection algorithms. Journal of Big Data, vol. 8, no. 1, pp. 1-27.

Bagas N, H. R. W., Mailoa, E., & Purnomo, H. D. (2020). Fruit detection for classification by type with YNOv3-based CNN algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), vol. 4, no. 3, pp. 476 - 481.

Putra, I. W. S. E., Wijaya, A. Y., & Soelaiman, R. (2016). Klasifikasi citra menggunakan convolutional neural network (CNN) pada Caltech 101. Jurnal Teknik ITS, vol. 5, no. 1, pp. A65-A69.

Pangestu, R. A., Rahmat, B., & Anggraeny, F. T. (2020). Implementasi algoritma CNN untuk klasifikasi citra lahan dan perhitungan luas. Jurnal Informatika dan Sistem Informasi (JIFoSI), vol. 1, no. 1, pp. 166-174.

Miranda, N. D., Novamizanti, L. & Rizal, S. (2020). Convolutional Neural Network pada klasifikasi sidik jari menggunakan RESNET-50. Jurnal Teknik Informatika (JUTIF), vol. 1, no. 2, pp. 61-68.

Bochkovskiy, A., Wang, C.-Y. & Mark Liao, M. L. (2020). YOLOv4: Optimal speed and accuracy of object detection. Accessed at 1 April 2021. Available online on https://arxiv.org/pdf/2004.10934.pdf.

Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2021). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13029-13038.

Japkowicz, N. (2000, June). The class imbalance problem: Significance and strategies. In Proc. of the Int’l Conf. on Artificial Intelligence, vol. 56, pp. 111-117.

Redmond, J. (2016). Darknet: open source neural networks in C. Accessed at 1 April 2021. Available online on https://pjreddie.com/darknet/.

Mansoub, S. K., Abri, R., & Yarıcı, A. H. (2019). Concurrent real-time object detection on multiple live streams using optimization CPU and GPU resources in YOLOv3. SIGNAL: The Fourth International Conference on Advances in Signal, Image and Video Processing, pp. 23-28.

Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, vol. 105, no. 12, pp. 2295-2329.

DOI: http://dx.doi.org/10.36055/tjst.v17i2.12254

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Teknika: Jurnal Sains dan Teknologi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.