Saleh, Alzayat, Hasan, Md Mehedi, Raadsma, Herman W., Khatkar, Mehar S., Jerry, Dean, and Rahimi Azghadi, Mostafa (2024) Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization. Aquacultural Engineering, 106. 102391.

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Abstract

Accurate morphometric analyses and weight estimation are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales and in real-time is time-consuming, labour-intensive, and prone to errors. Digital imaging of individuals and subsequent training of prediction models using Deep Learning (DL) has the potential to rapidly and accurately acquire phenotypic data from aquaculture species. In this study, we applied a novel DL approach to automate morphometric analysis and weight estimation using the black tiger prawn (Penaeus monodon) as a model crustacean. The DL approach comprises two main components: a feature extraction module that efficiently combines low-level and high-level features using the Kronecker product operation; followed by a landmark localization module that then uses these features to predict the coordinates of key morphological points (landmarks) on the prawn body. Once these landmarks were extracted, weight was estimated using a weight regression module based on the extracted landmarks using a fully connected network. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Principal Component Analysis (PCA) was also used to identify landmark-derived distances, which were found to be highly correlated with shape features such as body length, and width. We evaluated our approach on a large dataset of 8164 images of the Black tiger prawn (Penaeus monodon) collected from Australian farms. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.

Item ID:	81679
Item Type:	Article (Research - C1)
ISSN:	1873-5614
Copyright Information:	© 2024 Published by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Funders:	Australian Research Council (ARC)
Projects and Grants:	ARC Industrial Transformation Research Hub program
Date Deposited:	23 Jan 2024 23:53
FoR Codes:	30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300501 Aquaculture @ 50% 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300502 Aquaculture and fisheries stock assessment @ 50%
SEO Codes:	10 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 1002 Fisheries - aquaculture > 100205 Aquaculture prawns @ 100%
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