Fish farming is increasingly recognized as a strategic sector for strengthening food security and supporting economic livelihoods in Benin. According to Pèlèbè et al. (2020), fish farming contributes substantially to animal protein intake and provides employment opportunities in both rural and peri-urban areas. Additionally, Adegbola et al. (2022) emphasize that fish farming helps to reduce reliance on fish imports and mitigates the decline in natural fish stocks caused by climate change, overfishing, and environmental degradation.

Despite its importance, the traditional management of fish farming tanks remains largely manual. Fish farmers must continuously monitor water quality, regulate feeding frequency, and maintain optimal environmental conditions. These tasks are labor intensive and prone to human error. Moreover, the lack of real-time feedback increases the risk of abrupt changes in water quality, which may lead to stress, disease, or mortality among fish populations.

Previous studies highlight the significance of water quality control in aquaculture systems. Verma et al. (2022) note that physicochemical parameters such as pH, dissolved oxygen, and temperature must be strictly regulated to support fish growth and metabolic processes. Similarly, Ogbukagu et al. (2020) show that deviations in these parameters can impair fish physiological functions and reduce production efficiency. Therefore, it is crucial that water quality management practices are rigorously controlled to optimize fish production.

Recent technological advances have introduced the use of Internet of Things (IoT) devices and automated monitoring systems to improve aquaculture management. For example, Ineza et al. (2023) implemented an IoT-based monitoring system in Rwanda to remotely track water quality metrics. Zhang et al. (2011) demonstrated the potential of wireless sensor networks to enhance monitoring accuracy and to reduce reliance on manual measurements.

However, many existing systems focus primarily on data monitoring and notification rather than autonomous intervention. This limitation requires fish farmers to remain constantly available to take corrective action when environmental conditions change. These constraints reduce practical adoption, particularly among small-scale producers with limited technical resources.

To address these challenges, the present work introduces a semi-automated fish tank management system designed to both monitor environmental parameters and perform corrective actions automatically. The system aims to (i) automate feed distribution, (ii) transmit real-time water quality data to a remote interface, and (iii) autonomously regulate environmental conditions within acceptable ranges.

The methodology adopted enabled the development of the smart fish tank and its associated software programs. The tank and the electronic control unit are illustrated in Fig. 5.

Preliminary tests showed that the tank could be completely filled in 2 min and 30 s, and half emptied in 1 min and 15 s. The system operates according to the configuration stored on the Micro SD card, executing the following automated tasks: i.

Real-time display of all monitored environmental parameters on the integrated screen;

Verma et al. (2022) emphasize the importance of physicochemical parameters such as pH, temperature, and dissolved oxygen for fish health. In this context, our device integrates these parameters in a precise and continuous manner, reducing the risks of thermal stress and hypoxia, as also highlighted by Adegbola et al. (2022) in their study on the economic performance of fish farming in Benin.

Moreover, Ineza et al. (2023) demonstrated the effectiveness of IoT-based systems in water quality management for fish farming through automated control of environmental parameters, contributing to improved management and productivity. Our device, however, goes beyond mere monitoring: it actively intervenes to correct deviations, maintaining parameters within admissible ranges, which constitutes a major advantage for the continuous regulation and optimization of fish growth conditions.

The approach developed in our project also differs from that of Zhang et al. (2011). Although both systems use sensors for real-time water quality monitoring, Zhang et al. (2011) rely on a wireless sensor network (WSN) architecture to collect and analyze data via centralized software. In contrast, our device combines continuous monitoring with automatic intervention to directly adjust environmental parameters whenever they exceed predefined thresholds.

Furthermore, the software architecture developed in this project provides enhanced flexibility by combining local configuration software (Python with Qt6) and a remote management platform (PHP, JavaScript, SQL). This dual system allows both direct parameter control via the control unit and in-depth visualization of historical data, facilitating informed decision-making for fish farmers.

Unlike the systems of Zhang et al. (2011) and Ineza et al. (2023) – which primarily focus on data collection and visualization – our solution actively manages the aquaculture environment. This reduces the need for human intervention and significantly enhances the reliability and efficiency of the system.

During the prototype development phase, preliminary tests were conducted to determine key operational parameters, including the time to completely fill the tank, the time for partial emptying, and the oscillation angle of the supply pipe. However, due to the limited time available for the project, full-scale experimental testing and validation under real operating conditions could not be performed. Therefore, the verification process focused on functional checks to ensure proper operation and coordination of each subsystem (water supply, feeding, cleaning, and temperature regulation).

These preliminary assessments provided valuable insights into the prototype's performance, and serve as a foundation for future experimental validation and optimization once sufficient time and resources are available.

The semi-automated management system developed in this study provides a locally adapted solution for small-scale fish farming in Benin, integrating water supply, feeding, cleaning, temperature regulation, and electronic control. Each subsystem operates autonomously in response to real-time environmental parameters, ensuring optimized tank management and minimizing health risks such as thermal stress, hypoxia, and extreme pH fluctuations.

The main contribution of this work is the demonstration of a fully integrated, accessible prototype that combines continuous monitoring, automated feeding, and autonomous regulation. Preliminary functional tests indicate enhanced environmental stability, reduced labor requirements, and improved conditions for fish health. However, full-scale experimental validation on fish growth and production yield has not yet been conducted, representing an important avenue for future research.

Future developments will focus on integrating renewable energy sources, implementing predictive water quality management, and incorporating closed-loop recirculation systems to further enhance the sustainability and productivity of small-scale aquaculture operations.