[1B5-GS-2] Machine learning: Industrial application

[1B5-GS-2-04] The importance of featuer engineering for the weight estimation of cultured fish

Weight estimation of cultured fish

〇Junya Kobayashi¹, Masashi Tsubaki², Hideki Asoh², Yui Mineshita¹, Ichiro Nagano¹ (1. Nissui Corporation, 2. National Institute of Advanced Industrial Science and Technology)

Keywords:Aquaculture, Weight estimation , Machine learning, Feature engineering

Fish weight monitoring in aquaculture enhances the potential for improving productivity, profitability, and management quality. Conventionally, farmed fish had been directly weighed after been taken out. Such direct measurements can be time-consuming, and stressful for the fish, negatively affecting their growth performance and even resulting in increased mortality. Recently, weight estimation methods using underwater camera measurements has been developed, but there is still room for improvement in their accuracy. In this study, real measurement data of body shape and weigh of farmed Japanese amberjack, which is one of the most important cultured fish species in Japan, was accumulated across the entire aquaculture stage. Various weight estimation models were constructed based on these data and the estimation accuracy was evaluated. We found that weight estimation model with practically sufficient accuracy can be formulated using the features obtained from the current camera measurements by feature engineering. Additionally, further accuracy improvement can be achieved by adding body width as a new feature. These findings provide valuable insights for aquaculture weight estimation practices.