Nile Tilapia Count and Location: AI and CLAHE Unleashed | InformativeBD

Ben Saminiano, Arnel Fajardo, and  Ruji Medina, from the different institute of the Philippines. wrote a research article about, Nile Tilapia Count and Location: AI and CLAHE Unleashed. entitled, Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and CLAHE. This research paper published by the Journal of Biodiversity and Environmental Sciences (JBES). an open access scholarly research journal on Biodiversity. under the affiliation of the International Network For Natural Sciences | INNSpub. an open access multidisciplinary research journal publisher.

Abstract

Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.

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Introduction

Accurate counting of organisms, such as Nile Tilapia (Oreochronis niloticus), is important for various applications, including fisheries management, environmental monitoring, and aquaculture operations (Li et al., 2020). In the Philippines, tilapia is the second most important cultured species, with approximately 281,111 MT of total production in 2021. In 2020, tilapia made up 20% of the aquaculture production in the country, with Central Luzon as the leading producer. Tilapia is an important commodity for food security and economic development (PCAARRD, n.d.).

The tilapia industry in the Philippines has made notable growth in production from 2002 to 2022, with an increase of 115.58%. This may be attributed to several programs done by the government, such as improving the strain of tilapia and improving the technology in production and culture to sustain industry growth (Bureau of Fisheries and Aquatic Resources, 2022).

However, despite the progress made in tilapia aquaculture, problems and challenges persist. Pollutionrelated problems like diseases and water quality management, sources of quality fingerlings, and market competition are among the key challenges faced by farmers (Bureau of Fisheries and Aquatic Resources, 2022). Addressing these challenges and enhancing the efficiency and sustainability of tilapia production is crucial for the industry's continued growth.

In this context, developing an automated methodology for accurate surface tilapia detection using a Convolutional Neural Network (CNN) brings an opportunity to improve tilapia farming practices. Leveraging the capabilities of CNN and Contrast Limited Adaptive Histogram Equalization (CLAHE) aims to develop an approach to determine whether Nile Tilapia are at the surface or submerged. The insights gained from this research can contribute to optimizing feeding strategies, improving management practices, and enhancing tilapia aquaculture's overall productivity.

The paper is presented as follows: Section 1 introduces the motivation for the research. Section 2 concentrated on the related works on image processing, CNN, and CLAHE. The methodology of the research is presented in Section 3. Section 4 presents the Tests and Results. Finally, Section 5 discussed the conclusion and future works.

Reference

Bureau of Fisheries and Aquatic Resources. 2022. The Philippine Tilapia Industry Roadmap (2022-2025).

Conrady CR, Er Ş, Attwood CG, Roberson LA, de Vos L. 2022. Automated detection and classification of southern African Roman seabream using mask R-CNN. Ecological Informatics 69, 101593.

Jose JA, Kumar CS, Sureshkumar S. 2022. Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models. Information Processing in Agriculture 9(1), 68–79.

Li D, Miao Z, Peng F, Wang L, Hao Y, Wang Z, Chen T, Li H, Zheng Y. 2020. Automatic counting methods in aquaculture: A review.

Lumauag R, Nava M. 2019. Fish tracking and counting using image processing. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, 1-4. https://doi.org /10.1109/HNICEM.2018.8666369

Mandal R, Connolly RM, Schlacher TA, Stantic B. 2018. Assessing fish abundance from underwater video using deep neural networks. In Proceedings of the International Joint Conference on Neural Networks (Vols. 2018-July). https://doi.org/10.1109 /IJCNN.2018.8489482

Mishra A, Gupta M, Sharma P. 2018. Enhancement of Underwater Images using Improved CLAHE. 2018 International Conference on Advanced Computation and Telecommunication, ICACAT 5, 1-6. https://doi.org/10.1109/ICACAT.2018.8933665

Muksit AAl, Hasan F, Hasan Bhuiyan Emon MF, Haque MR, Anwary AR, Shatabda S. 2022. YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Ecological Informatics 72, 101847. https://doi.org/10.1016 /J.ECOINF.2022.101847

PCAARRD. 2023. (n.d.). Tilapia – Industry Strategic Science and Technology Plans (ISPs) Platform. Retrieved June 24, 2023, from  https://ispweb. pcaarrd. dost.gov.ph/tilapia-2/

Redmon J, Farhadi A. 2018. YOLOv3: An incremental improvement. ArXiv.

Saminiano B. 2020. Feeding Behavior Classification of Nile Tilapia (Oreochromis niloticus) using Convolutional Neural Network. International Journal of Advanced Trends in Computer Science and Engineering 9(1.1 S I), 259–263. https://doi.org /10.30534/ijatcse/2020/4691.12020

Wang H, Zhang S, Zhao S, Wang Q, Li D, Zhao R. 2022. Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. Computers and Electronics in Agriculture 192, 106512. https://doi.org/10.1016 /J.COMPAG.2021.106512

Yu C, Fan X, Hu Z, Xia X, Zhao Y, Li R, Bai Y. 2020. Segmentation and measurement scheme for fish morphological features based on Mask R-CNN. Information Processing in Agriculture 7(4), 523–534. https://doi.org/10.1016/J.INPA.2020.01.002

Source : Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network andCLAHE