Automated water quality monitoring and regression-based forecasting system for aquaculture

Abstract

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.

The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization

“…In recent years, the aquaculture industry has expanded due to the adoption of marine ranching techniques. Various work that utilizes artificial intelligence in aquaculture can be found in the literature, which includes fish classification [2], water quality monitoring [3], fish behaviour detection [4], and feeding control [5]. Accurate counting of fish populations in fish farms is critical ISSN: 2302-9285 …”

Optimizing Forecasted Activity Notifications with Reinforcement Learning

“…The aim of these studies is to develop methods for predicting upcoming activities, events, monitoring, or accidents. For example, a forecasting system for water quality monitoring for aquaculture [ 21 ], large-scale wastewater surveillance, or managing milk production on dairy cows [ 22 ]. Several techniques have been proposed for forecasting, such as machine learning, probabilistic modeling, and rule-based systems [ 20 ].…”

“…However, sending forecasted results in a timely manner continues to be a challenge for this approach. In other studies, forecasting results are sent when forecasting results are obtained or depending on thresholds [ 16 , 21 , 23 , 24 , 25 , 26 , 27 ], while others create a separate schedule for sending forecast results [ 22 , 26 , 28 , 29 , 30 , 31 ]. Using an example of a weather information forecasting system for farmers, the system will send a push notification whenever the readings fall below the threshold [ 21 , 32 ].…”

“…In other studies, forecasting results are sent when forecasting results are obtained or depending on thresholds [ 16 , 21 , 23 , 24 , 25 , 26 , 27 ], while others create a separate schedule for sending forecast results [ 22 , 26 , 28 , 29 , 30 , 31 ]. Using an example of a weather information forecasting system for farmers, the system will send a push notification whenever the readings fall below the threshold [ 21 , 32 ]. In the case of daily activities, it is essential to consider user engagement as the recipient of the information, which means whether the user or the target has actually received the information or not.…”