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.

准确的形态测量分析和重量估计在水产养殖中非常有用，可用于优化饲养、预测收获产量、识别选择性育种所需的性状、分级过程以及监测生产动物的健康状况。然而，通过传统的手工方法在工业规模上实时收集表型数据非常耗时、劳动密集型，并且容易出错。使用深度学习（DL）对个体进行数字成像并随后训练预测模型有可能快速准确地获取水产养殖物种的表型数据。在这项研究中，我们应用了一种新颖的深度学习方法，使用黑虎虾（斑节对虾）作为甲壳类动物模型来自动进行形态测定分析和重量估计。深度学习方法包含两个主要组件：特征提取模块，使用克罗内克乘积运算有效组合低级和高级特征；接下来是地标定位模块，然后使用这些特征来预测虾体上关键形态点（地标）的坐标。一旦提取了这些地标，就可以使用基于使用全连接网络提取的地标的权重回归模块来估计权重。对于形态测量分析，我们利用检测到的标志来得出五个重要的虾特征。主成分分析（PCA）也被用来识别地标导出的距离，这些距离被发现与身体长度和宽度等形状特征高度相关。我们在从澳大利亚农场收集的 8164 张黑虎虾（斑节对虾）图像的大型数据集上评估了我们的方法。我们的实验结果表明，新颖的深度学习方法在准确性、鲁棒性和效率方面优于现有的深度学习方法。