Land-based aquaculture is an ideal aquaculture solution for creating high-quality seafoods and providing optimal conditions for maximizing growth of seafood production because environmental factors are well controlled. Predicting the growth of indoor-cultured abalone is meaningful because it facilitates evaluation of the effectiveness of this type of farming and understanding of the effects of controllable environmental factors on abalone growth. In this study, such predictions were made using an ensemble of machine learning algorithms: the random forest, gradient boosting, support vector machine, and neural network algorithms. Data were collected in the town of Fukushima, Hokkaido, Japan, and the increase in the weight of abalone was hypothesized from independent variables, including air and water temperature, loss of individuals caused by mortality or emigration, flow speed, age, and growth period between two measurements. The results showed that the ensemble method predicts growth well, with a low mean absolute error and mean square error. Temperature adjustment can make a strong contribution to increasing the weight of abalone, where a stable and warm temperature enhances growth. Moreover, the age of abalone is closely related to growth. Abalone size increased strongly in the early stages but decreased slightly once near market size.

陆基水产养殖是生产高品质海鲜的理想水产养殖解决方案，并且由于环境因素得到良好控制，因此可以为海鲜产量最大化增长提供最佳条件。预测室内养殖鲍鱼的生长具有重要意义，因为它有助于评估此类养殖的有效性，并了解可控环境因素对鲍鱼生长的影响。在这项研究中，此类预测是使用一组机器学习算法进行的：随机森林、梯度提升、支持向量机和神经网络算法。数据收集于日本北海道福岛镇，根据自变量假设鲍鱼重量的增加，包括气温和水温、死亡或迁徙造成的个体损失、流速、年龄和生长期两次测量之间。结果表明，集成方法可以很好地预测生长，平均绝对误差和均方误差较低。温度调节对于增加鲍鱼的重量有很大的贡献，稳定而温暖的温度可以促进鲍鱼的生长。而且，鲍鱼的年龄与生长密切相关。鲍鱼尺寸在早期阶段大幅增加，但在接近上市尺寸后略有下降。