Development of machine learning models for the prediction of dissolved oxygen in aquaculture 4.0

Abstract

The world faces the problem of feeding a growing population, which will reach more than 9 billion people by 2050. Thus, there is a need to develop activities that promote food production, within the dimensions of sustainability (social, technicaleconomic, and environmental). In this context, IoT systems focused on aquaculture 4.0 stand out, which allows the cultivation of high productions per unit of volume, with low environmental impact. However, these systems need to be extremely controlled, requiring sensors to perform realtime readings of water metrics, with emphasis on the dissolved oxygen (DO) sensor, which plays an essential role in determining the quality and quantity of available habitat for the organisms present in the system. Even with this importance, this sensor is often not used, due to its high associated cost. As an alternative solution to this problem, machine learning models have been proposed to predict DO, using temperature and pH readings as inputs. Experiments were carried out comparing different data scaling techniques and the prediction performance in different seasons of the year and regression metrics were used to evaluate the implemented models. The results showed that the proposed LSTM model is capable of making OD predictions and being applied in IoT and aquaculture 4.0 systems.