Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

Tengtrairat, Naruephorn, Woo, Wai Lok, Parathai, Phetcharat, Rinchumphu, Damrongsak and Chaichana, Chatchawan (2022) Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models. Sensors, 22 (14). p. 5161. ISSN 1424-8220

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Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia’s extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia’s weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.

Item Type: Article
Additional Information: Funding information: This research project is financially supported by the Thailand Science Research and Innovation (TSRI). Technical support from the Energy Technology for Environment (ETE) Research center, Chiang Mai University, Thailand, is also acknowledged.
Uncontrolled Keywords: machine vision, deep learning, weight estimation, aquaculture, non-intrusive methods
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 11 Jul 2022 10:45
Last Modified: 11 Jul 2022 10:45

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