Measurement and Analysis of Detecting Fish Freshness Levels Using Deep Learning Method

https://doi.org/10.22146/ijccs.95054

Dhea Fajriati Anas(1*), Indra Jaya(2), Yeni Herdiyeni(3)

(1) IPB University
(2) IPB University
(3) IPB University
(*) Corresponding Author

Abstract


Subjective and objective tests used to determine the fish deterioration process require specialized skills and time, making them inefficient for use by the general public in markets. The quality of fish products in markets is not always guaranteed, so consumers must determine their suitability. Deep learning can be used to analyze images and automatically and accurately detect the freshness of fish. This study aims to evaluate the efficiency of deep learning models in detecting fish freshness and implementing them into an Android application for public use. "Image datasets and pH tests were collected as references for the postmortem phase over a 24-hour period, with hourly checks on three fish species (Rachycentron canadum, Trachinotus blochi, and Lates calcarifer). Data were classified into three classes, pre-rigor/fresh, rigor mortis/semi-fresh, and post-rigor/not fresh. The dataset was divided using the 10-fold cross-validation method and analyzed using YOLOv5 and Faster R-CNN algorithms. The study results showed that YOLOv5 had higher average values for each metric compared to Faster R-CNN. Dataset 8 in YOLOv5 showed precision of 99.4%, recall of 98.1%, f1-score of 98.7%, accuracy of 99.3%, and mAP of 99.3%. The YOLOv5 model for dataset 8 was selected for implementation in the Android application due to its high metric values. This application effectively provides information on fish freshness detection and confidence scores.

Keywords


Fish Freshness; YOLOv5; Faster-RCNN; Object Detection; Android Appliaction; pH

Full Text:

PDF


References

[1] A. Bochkovskiy, C.Y. Wang, and H.Y.M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” arXiv, vol. 2004, pp. 1-17, 2020.
[2] A. Daskalova, “A farmed fish welfare: stress, post-mortem muscle metabolism, and stress-related meat quality changes,” International Aquatic Research, vol. 11, pp. 113-124, 2019.
[3] A.R. Prananda, E.L. Frannita, E. Pramitaningrum, A. Hidayat, W.B. Setiawan, N. Purwaningsih, “Klasifikasi jenis cacat kulit menggunakan SMOTE-GoogLeNet,” Journal Informatic Technology and Communication vol. 8, no. 1, pp. 23-32, 2024
[4] A. Santoso, and G. Ariyanto, “Implementasi deep learning berbasis keras untuk pengenalan wajah,” Jurnal Emitor, vol. 18, no. 1, pp. 15-21, 2018.
[5] B. Anwar, “Penerapan algoritma jaringan syaraf tiruan backpropagation dalam memprediksi tingkat suku bunga bank,” Jurnal SAINTIKOM, vol. 10, no. 2, pp. 111-123, 2011.
[6] Badan Standarisasi Nasional (BSN). Ikan Segar SNI 01-2729-2013¸Jakarta, ID: Badan Standarisasi Nasional (BSN), 2013.
[7] D. Kumar, S. Kumar, and Rajput, “An intelligent system for fish freshness quality assessment using artificial neural network,” IJCRT (International Journal of Creative Research Thoughts), vol. 8, no. 2, pp. 765-958, 2020.
[8] G. Jocher. “YOLOv5”. Ultralytics. https://docs.ultralytics.com/. (accesed on May 10, 2020].

[9] H.M. Lalabadi, M. Sadeghi, and S.A. Mireei, “Fish freshness categorization from eyes and gills color features using multiclass artificial neural network and support vector machines,” Aquacultural Engineering, vol. 90, no. 2020, pp. 1-9, 2020.
[10] J. Han, and M. Kamber, Data Mining: Concepts and Techniques Tutorial, San Fransisco, USA: Morgan Kaufman Publisher, 2001.
[11] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: unified, real-time object detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[12] J. Solawetz. “Yolov5 new version-improvement and evaluation”. Roboflow. https://blog.roboflow.com/yolov5-improvements-and-evaluation/. (accesed on Jan. 10, 2023)
[13] I.C. Navotas, C.N.V. Santos, E.J.M. Balderrama, F.E.B. Candido, A.J.E. Villacanas, and J.S. Velasco, “Fish identification and freshness classification through image processing using artificial neural network,” (ARPN) Asian Research Publishing Network Journal of Engineering and Applied Sciences, vol. 13, no. 18, pp. 4912-4922, 2018.
[14] L.K.S. Tolentino, J.W.F. Orillo, P.D. Aguacito, E.J.M. Colango, J.R.H. Malit, J.T.G. Marcelino, A.C. Nadora, and A.J.D. Odeza, “Fish freshness determination through support vector machine,” Journal of Telecommunication, Electronic, and Computer Engineering, vol. 9, no. 2-5, pp. 139-143, 2017.
[15] M.F. Fibrianda, and A. Bhawiyuga, “Analisis perbandingan akurasi deteksi serangan pada jaringan komputer dengan metode naïve bayes dan support vector machine (SVM),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, pp. 3112-3123, 2018.
[16] M.H. Sazli, “A brief review of feed forward neural networks,” Commun. Fac. Sci. Univ. Ank. Series A2-A3, vol. 50, no. 1, pp. 11-17, 2006.
[17] M. Nurilmala, Nurjanah, A. Fatriani, A.R. Indarwati, R.M. Pertiwi “Kemunduran mutu ikan baronang (Siganus javus) pada penyimpanan suhu chilling,” Jurnal Teknologi Perikanan dan Kelautan, vol. 12, no. 1, pp. 92-101, 2021.
[18] M. Sornam, A. Radhika, and M. Manisha, “Fish freshness classification using wavelet transformation and fuzzy logic technology,” Asian Journal of Computer Science and Information Technology, vol. 7, no. 2, pp. 15-21, 2017.
[19] N.M.S. Iswari, Wella, and Ranny, Fish freshness classification method based on fish image using K-Nearest Neighbor. 4th International Conference on New Media Studies, 2017.
[20] N. Sengar, M.K. Dutta, B. Sarkar, “Computer vision based technique for identification of fish quality after pesticide exposure,” International Joural of Food Properties, vol. 20, no.s2, pp. 192-205, 2017.
[21] R. Vargas and L. Ruiz, “Deep learning: previous and present applications,” Journal of Awareness, vol. 2, no. 3, pp. 11-20, 2018.
[22] S. Cui, Y. Zhou, Y. Wang, and L. Zhai, “Fish detection using deep learning”. Hindawi, vol. 2020, pp. 1-13, 2020.
[23] S. Ren, K. He, R. Girshick, and J. Sun, “Faster-RCNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.
[24] S. Srivastava, A.V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms”. Journal of Big Data, vol. 8, no. 1, pp. 1-27, 2021.
T. Murakoshi, T. Masuda, K. Utsumi, K. Tsubota, Y. Wada, “Glossiness and perishable food quality: visual freshness judgment of fish eyes based on luminance distribution,” Plos One, vol. 8, no.3, pp. 1-5, 2013.
[25] T. Nurhayati, Nurjanah, and R. Nugraha, Fisiologi, Formasi, dan Degradasi Metabolit Hasil Perairan, Bogor, ID: IPB Press, 2019.
[26] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015.



DOI: https://doi.org/10.22146/ijccs.95054

Article Metrics

Abstract views : 836 | views : 609

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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


Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs


View My Stats1
View My Stats2