外文摘要：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.
外文关键词：computer vision;deep learning;machine learning;convolutional neural networks;Aquaculture;weight estimation;Morphometric analyses
作者：Hasan, Md Mehedi;Saleh, Alzayat;Raadsma, Herman W;Khatkar, Mehar S;Jerry, Dean R;Azghadi, Mostafa Rahimi
作者单位：Univ Sydney;James Cook Univ
期刊名称：AQUACULTURAL ENGINEERING
期刊影响因子：0.0
出版年份：2024
出版刊次：106
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台