Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis

Abstract

Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r² of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a 'Smart Aquaculture Analytics' web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

Keywords: Bangladesh; Internet of things; Machine learning; Real-time monitoring; Shrimp farm; Smart aquaculture.

Fig. 1

The cultivation of (a) adult Galda in (b) a smart aqua method using Internet of Things (IoT) technology.

Fig. 2

The diagram of an overview of developing a smart aquaculture system.

Fig. 3

The pin-out diagram of the proposed IoT device for shrimp farming monitoring.

Fig. 4

Overview of the working step of the proposed system to develop a smart application for shrimp farming.

Fig. 5

The analytic hierarchy for freshwater shrimp farm monitoring system.

Fig. 6

Class-wise data distribution for each parameter (a–e) of water quality in the shrimp farm.

Fig. 7

The home page of the smart aquaculture web application for shrimp farm monitoring.

Fig. 8

The home page of the smart aquaculture web application for shrimp farm monitoring.

Fig. 9

Historical data visualization based on a range of data.

Fig. 10

Forecasting five parameters of water quality the next day and prediction of shrimp production based on the previous 10 days.

Fig. 11

Smart notification service to farmers from Smart Aquaculture Analytics system.