A Mobile App for Counting Shrimp Larvae Based on the YOLO V5 Method

Shochibah Yatimatul Asmak; Denny Daffa Rizaldi; Rendy Adi Fatma Saputra; Anan Pepe Abseno; Valentinus Robi Hananto; Eka Sari Oktarina

doi:10.52435/complete.v5i2.647

Authors

Shochibah Yatimatul Asmak Department of Computer Engineering, Universitas Dinamika, Surabaya, Indonesia
Denny Daffa Rizaldi Department of Information System, Telkom University Surabaya, Surabaya, Indonesia
Rendy Adi Fatma Saputra Department of Information System, Telkom University Surabaya, Surabaya, Indonesia
Anan Pepe Abseno Department of Digital Manufacturing, Indonesia Multi Colour Printing, Krian, Indonesia
Valentinus Robi Hananto College of Information Science and Engineering, Ritsumeikan University, Ibaraki, Japan
Eka Sari Oktarina Department of Computer Engineering, Telkom University Surabaya, Surabaya, Indonesia

DOI:

https://doi.org/10.52435/complete.v5i2.647

Keywords:

Counting Larva, Object Detection, Shrimp Larvae

Abstract

Manual counting of shrimp larvae in aquaculture is labour-intensive and time-consuming. This study aims to develop a mobile application to automate the counting process using an object detection algorithm. The application features dual functionality for real-time camera capture and image upload. Model performance was evaluated using several metrics, including Mean Average Precision, precision, and recall. The object detection model achieved a Mean Average Precision (mAP) of 93.93%, precision of 91%, and recall of 89.3%. Trials of the application demonstrated an average accuracy rate of 91.03% in detecting shrimp larvae. Despite challenges in detecting transparent larvae and distinguishing them from debris, the results indicate that the application holds promise for enhancing efficiency in shrimp farming operations. Future improvements may be directed towards enhancing application performance by refining the dataset and tuning model parameters to increase recall without compromising precision. This study represents a significant step towards integrating AI-driven technologies into aquaculture, potentially transforming the shrimp larvae counting and management process in the industry.

References

P. J. G. Henriksson, L. K. Banks, S. K. Suri, T. Y. Pratiwi, N. A. Fatan, and M. Troell, "Indonesian aquaculture futures-identifying interventions for reducing environmental impacts," Environmental Research Letters, vol. 14, no. 12, 2019, doi: 10.1088/1748-9326/ab4b79.

S. Armalivia, Z. Zainuddin, A. Achmad, and Muh. A. Wicaksono, "Automatic Counting Shrimp Larvae Based You Only Look Once (YOLO)," in 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), IEEE, Apr. 2021, pp. 1–4, doi: 10.1109/AIMS52415.2021.9466058.

S. Arsad et al., "The Application of Microalgae Feeding Regime on Whiteleg Shrimp Culture in Each Stage: A Mini Review," Sains Malaysiana, vol. 52, no. 1, pp. 1–16, Jan. 2023, doi: 10.17576/jsm-2023-5201-01.

J. Reis, A. Weldon, P. Ito, W. Stites, M. Rhodes, and D. A. Davis, "Automated feeding systems for shrimp: Effects of feeding schedules and passive feedback feeding systems," Aquaculture, vol. 541, p. 736800, Apr. 2021, doi: 10.1016/j.aquaculture.2021.736800.

H. Liu, L. Hao, X. Ma, Y. Yu, and L. Wang, "Application of Deep Learning-Based Object Detection Techniques in Fish Aquaculture: A Review," Journal of Marine Science and Engineering, vol. 11, no. 4, p. 867, Apr. 2023, doi: 10.3390/jmse11040867.

L. Zhang, W. Li, C. Liu, X. Zhou, and Q. Duan, "Automatic fish counting method using image density grading and local regression," Computers and Electronics in Agriculture, vol. 179, p. 105844, Oct. 2020, doi: 10.1016/j.compag.2020.105844.

X. Peng, T. Zhou, Y. Zhang, and X. Zhao, "Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion," Sensors, vol. 24, no. 9, May 2024, doi: 10.3390/s24092916.

E. A. Awalludin, M. Y. Mat Yaziz, N. R. Abdul Rahman, W. N. J. H. W. Yussof, M. S. Hitam, and T. N. T Arsad, "Combination of Canny Edge Detection and Blob Processing Techniques for Shrimp Larvae Counting," in 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, Sep. 2019, pp. 308–313, doi: 10.1109/ICSIPA45851.2019.8977746.

D. Liu et al., "Shrimpseed_Net: Counting of Shrimp Seed Using Deep Learning on Smartphones for Aquaculture," IEEE Access, vol. 11, pp. 85441–85450, 2023, doi: 10.1109/ACCESS.2023.3302249.

C.-T. Yeh and M.-S. Ling, "Portable Device for Ornamental Shrimp Counting Using Unsupervised Machine Learning," Sensors and Materials, vol. 33, no. 9, p. 3027, Sep. 2021, doi: 10.18494/SAM.2021.3240.

A. Wibowo, L. Lusiana, and T. K. Dewi, “Implementasi Algoritma Deep Learning You Only Look Once (YOLOv5) Untuk Deteksi Buah Segar Dan Busuk,” Paspalum: Jurnal Ilmiah Pertanian, vol. 11, no. 1, p. 123, Mar. 2023, doi: 10.35138/paspalum.v11i1.489.

M. S. Nuha and R. Alexandro H., “Pemanfaatan Yolo untuk Pengenalan Kesegaran Buah Mangga,” Joutica, vol. 7, no. 1, p. 513, Feb. 2022, doi: 10.30736/jti.v7i1.747.

A. Putra Pranjaya, F. Rizki, R. Kurniawan, and N. K. Daulay, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Penyakit Pada Daun Tanaman Padi Berbasis YoloV5 (You Only Look Once),” Media Online, vol. 4, no. 6, pp. 3127–3136, 2024, doi: 10.30865/klik.v4i6.1916.

K. Ahmad Baihaqi and C. Zonyfar, “Deteksi Lahan Pertanian Yang Terdampak Hama Tikus Menggunakan Yolo v5,” Syntax: Jurnal Informatika, vol. 11, no. 02, pp. 01–11, Nov. 2022, doi: 10.35706/syji.v11i02.7226.

R. F. Putra and D. I. Mulyana, “Optimasi Deteksi Objek Dengan Segmentasi dan Data Augmentasi Pada Hewan Siput Beracun Menggunakan Algoritma You Only Look Once (YOLO),” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 8, no. 1, pp. 93–103, Jan. 2024, doi: 10.35870/jtik.v8i1.1391.

H. Duan, J. Wang, Y. Zhang, X. Wu, T. Peng, X. Liu, and D. Deng, "Shrimp Larvae Counting Based on Improved YOLOv5 Model with Regional Segmentation," Sensors, vol. 24, no. 19, p. 6328, 2024, doi: 10.3390/s24196328.

I. F. E. Babila, S. A. E. Villasor, and J. C. D. Cruz, "Object Detection for Inventory Stock Counting Using YOLOV5," in IEEE 18th International Colloquium on Signal Processing & Applications (CSPA 2022), 2022, doi: 10.1109/cspa55076.2022.9782028.

S. Jha, C. Seo, E. Yang et al., "Real Time Object Detection and Tracking System for Video Surveillance System," Multimedia Tools and Applications, vol. 80, pp. 3981–3996, 2021.

A. Benjumea, I. Teeti, F. Cuzzolin, and A. Bradley, "YOLO-Z: Improving Small Object Detection in YOLOv5 for Autonomous Vehicles," arXiv (Cornell University), 2021, doi: 10.48550/arxiv.2112.11798.

E. S. Oktarina, W. I. Kusumawati, and M. Musayyanah, "Implementasi Aplikasi Penghitung Benur dengan Menggunakan HP Android pada Instalasi Budidaya Air Payau Banjarkemuning, Sedati, Sidoarjo," Society Jurnal Pengabdian Masyarakat, vol. 3, no. 4, 2024, doi: 10.55824/jpm.v3i4.427.