Deep learning (DL) is a cutting-edge technology that enables high-throughput phenotyping in aquaculture. The routine application of DL offers new opportunities for the genetic selection of appearance traits, especially those related to body shape. The criteria currently used for the trait selection of commercial interest, such as rapid growth and weight gain, can directly influence the animal's appearance, which is a criterion for sales and profit. Different morphotypes of the pacu Piaractus mesopotamicus (elliptical and rounded) have been described previously and may represent different commercial trends. Therefore, this study aimed to 1) develop a computer vision system (CVS) through deep learning that targets the prediction of morphometric measurements and body shape (morphotypes) in pacu, 2) analyze whether morphotypes vary according to the environment, sex, and/or age, and 3) estimate genetic parameters for body shape, using the condition factor (K) and ellipticity (E) as criteria. Data from 1380 individuals corresponding to 48 full-sib families were evaluated in two distinct environments (breeding nucleus: env1; commercial fish farm: env2). The animals were evaluated based on their weight and morphometric measurements at 15 and 28 months of age (growth stage). We used the mask R-CNN model as a deep-learning algorithm, which was optimized for a ResNet architecture with only 18 layers. This resulted in a faster training period (8GB NVIDIA 2060 RTX in less than a day), which requires less computational effort. The pacu CVS was effectively developed to account for the segmentation of several fish body regions (head, body, fins, and pelvis), as corroborated by the high correlations of measurements predicted manually and automatically. We detected K and E variation at different growth stages and environments, in which fish tend to have rounded shapes in env2 and at 28 months old. The body shape heritability indicates that this trait is under moderate genetic control and should respond to selection. In conclusion, this study established an efficient CVS for pacu that is resilient to field conditions, allowing high-throughput phenotyping for the routine assessment of body shape in breeding programs for this species.

深度学习 (DL) 是一项尖端技术，可在水产养殖中实现高通量表型分析。DL 的常规应用为外观特征的遗传选择提供了新的机会，尤其是那些与体型相关的特征。目前用于商业利益性状选择的标准，例如快速生长和体重增加，可以直接影响动物的外观，这是销售和利润的标准。pacu Piaractus mesopotamicus的不同形态类型（椭圆和圆形）之前已经描述过，可能代表不同的商业趋势。因此，本研究旨在 1) 通过深度学习开发计算机视觉系统 (CVS)，以预测 pacu 中的形态测量和身体形状（形态类型），2) 分析形态类型是否因环境、性别和/或年龄，以及 3) 估计体型的遗传参数，使用条件因子 (K) 和椭圆率 (E) 作为标准。在两个不同的环境（育种核心：env1；商业养鱼场：env2）中评估了对应于 48 个全同胞家庭的 1380 个个体的数据。根据动物在 15 个月和 28 个月（生长阶段）时的体重和形态测量值对动物进行评估。我们使用 mask R-CNN 模型作为深度学习算法，它针对只有 18 层的 ResNet 架构进行了优化。这导致了更快的训练周期（8GB NVIDIA 2060 RTX 在不到一天的时间内），这需要更少的计算工作。pacu CVS 被有效地开发以解释几个鱼体区域（头部、身体、鳍和骨盆）的分割，手动和自动预测的测量值的高度相关性证实了这一点。我们在不同的生长阶段和环境中检测到 K 和 E 的变化，其中鱼在 env2 和 28 个月大时往往具有圆形。体型遗传力表明该性状处于中等遗传控制之下，应该对选择作出反应。总之，这项研究为 pacu 建立了一种有效的 CVS，该 CVS 对现场条件具有弹性，