Prawn Morphometrics and Weight Estimation from Images using Deep
  Learning for Landmark Localization
Alzayat Saleh Md Mehedi Hasan Herman W Raadsma Mehar S Khatkar Dean R Jerry Mostafa Rahimi Azghadi
Accurate weight estimation and morphometric analyses 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
weight estimation and morphometric analysis 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.