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. 2024 Jun 6;24(11):3682.
doi: 10.3390/s24113682.

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System

Md Moniruzzaman Hemal  1 , Atiqur Rahman  1 , Nurjahan  1   2 , Farhana Islam  3 , Samsuddin Ahmed  1 , M Shamim Kaiser  4 , Muhammad Raisuddin Ahmed  5
Affiliations

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System

Md Moniruzzaman Hemal et al. Sensors (Basel). .

Abstract

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.

Keywords: Internet of Things (IoT); machine learning; real-time water quality; smart fish farming; smart robot; solar power.

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Conflict of interest statement

The authors declare no conflicts of Interest.

Figures

Figure 1
Figure 1
Integrated system architecture for the proposed model.
Figure 2
Figure 2
Flowchart of mobile robotic agent.
Figure 3
Figure 3
Flowchart of the proposed model.
Figure 4
Figure 4
Circuit diagram of the proposed system.
Figure 5
Figure 5
Conceptual diagram of ML model.
Figure 6
Figure 6
Distribution of fish species before and after applying SMOTE.
Figure 7
Figure 7
System prototype and portable display system of the prototype.
Figure 8
Figure 8
Confusion matrix of ensemble model before and after SMOTE.
Figure 9
Figure 9
ROC curves of different ML algorithms before applying SMOTE.
Figure 10
Figure 10
ROC curves of different ML algorithms after applying SMOTE.
Figure 10
Figure 10
ROC curves of different ML algorithms after applying SMOTE.
Figure 11
Figure 11
Influence of the parameters on the output using SHAP.
Figure 12
Figure 12
Fish Recommender System.
See this image and copyright information in PMC

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