Machine learning for manually-measured water quality prediction in fish farming

Abstract

Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved variables based on the observed ones, and ii) forecasting when a low amount of data is available for training. We show that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day. Moreover, we showed that these prediction models can be implemented on a mobile-based information system and run in an average smartphone that fish farmers can afford.

Using machine learning technique for disease outbreak prediction in rainbow trout ( Oncorhynchus mykiss ) farms

“…The reported studies (Kokol et al, 2022; Vabalas et al, 2019) indicate the availability of ML techniques in the validation of limited data as in our study. Furthermore, the reported results showed the possibility of achievement of successful results with ML by using limited data (Zambrano et al, 2021).…”

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“…Random Forests for forecasting water quality variables such as dissolved oxygen, pond temperature, etc. [110] RMSE for pond temperature = 0.5971, RMSE for dissolved oxygen = 1.616.…”

“…Both classifiers provided an average accuracy of 100% during the training phase and average of 99.45% during the testing phase. Zambrano et al [110] propose the use of machine learning tools to forecast water quality variables such as dissolved oxygen, pH, and pond temperature. A comparison is performed between Random Forests (RF), ANNs, and multivariate linear regression, out of which RFs performed the best.…”

Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds

“…In fsh farming, Zambrano et al [23] introduced an ML model for manually observed water quality prediction. In cases where the number of measurements was restricted, the author used RF, MLR, and ANN to assess data from water quality indicators that are regularly recorded in fsh growth and farming.…”