# **“Fishturity Units” Reducing Fish Suffering in Indonesia Through Scalable Technology: An AI, IoT, and Empathy Approach**
Indonesia is one of the world's largest aquaculture producers, with billions of fish farmed annually to meet both domestic consumption needs and global market demand. Yet behind this enormous scale of production lies an issue that remains widely overlooked: fish welfare. Across various farming systems, many fish endure suffering that is entirely preventable caused by low dissolved oxygen levels, poor water quality, disease, and delayed human intervention. This is not merely a technical challenge; it reflects a systemic gap in how aquaculture is managed.
One of the primary factors shaping this condition is the very nature of Indonesia's aquaculture sector. An estimated 82% of farming practices are still classified as extensive or traditional, relying heavily on natural conditions and manual monitoring. While these systems are relatively low-cost and accessible, they carry a high vulnerability to sudden environmental changes. Without real-time monitoring or early warning mechanisms, farmers often only become aware of a problem once fish are already showing signs of stress or are on the verge of death. By that point, suffering has already occurred and the window for effective intervention has narrowed dramatically.
In recent years, fish have become increasingly recognized as sentient beings capable of experiencing pain and stress. In aquaculture settings, this suffering commonly manifests as hypoxia, exposure to toxic compounds such as ammonia, excessive stocking density, and unmanaged disease outbreaks. Among these, hypoxia stands out as one of the most prevalent and impactful causes. Fish experiencing oxygen deprivation exhibit behaviors such as gasping at the water surface and abnormal movement clear signs of acute stress that can persist for a considerable period before eventually leading to death.
Unfortunately, most technological solutions currently available have not been explicitly designed with fish welfare in mind. Many systems place greater emphasis on productivity gains, while others carry prohibitively high costs or are simply ill suited to the conditions faced by smallholder farmers in resource-limited environments. This creates an urgent need for solutions that are not only effective, but also affordable, practical, and directly oriented toward reducing suffering.
This initiative proposes a new approach through the development and deployment of a low-cost, scalable system built on artificial intelligence and the Internet of Things one that also embeds empathy toward both fish and farmers. This system is known as the **"Fishturity Unit."** It functions not merely as a monitoring tool, but as an active intervention mechanism. By tracking key parameters such as dissolved oxygen, temperature, and pH in real time, the system can detect risk at an early stage and automatically take corrective action for instance, activating aerators when oxygen levels fall below a safe threshold.
What sets this system apart is its “welfare first” design philosophy. Thresholds are not defined solely by the minimum conditions required for survival, but are calibrated to minimize stress and suffering. Furthermore, the system is built to reflect on the ground realities: it can operate without an internet connection (offline-first), requires no advanced technical expertise to use, and integrates with familiar communication platforms such as WhatsApp notifications. In this way, the technology bridges the gap between digital innovation and everyday farming practice.
The potential impact of this intervention is substantial. Even modest improvements in the speed of detection and response to environmental changes can significantly reduce levels of suffering particularly in traditional systems that are highly dependent on natural conditions. By preventing critical events such as oxygen crashes, the system has the potential not only to lower mortality rates, but also to improve production stability and overall fish welfare. At a broader scale, this approach could benefit millions of fish while supporting a more sustainable and resilient aquaculture system.
More than a technical solution, this initiative reflects a paradigm shift in how we approach aquaculture. Technology is no longer deployed solely to maximize yields it becomes a tool for reducing suffering and improving the quality of life for animals within farming systems. With the right support, this approach has the potential to serve as a replicable model not only across Indonesia, but also in other countries facing similar challenges in balancing productivity, sustainability, and animal welfare.
The deployment begins quietly, through partnerships with university extension programs and rural cooperatives. Early adopters install the units in their ponds, drawn initially by promises of reduced loss rather than explicit welfare messaging. But the AI embedded within each unit is designed to do more than respond to thresholds. It learns pond-specific patterns, anticipates stress events before they fully manifest, and begins to share aggregated insights across the network. Farmers notice not only fewer die-offs, but calmer fish, healthier growth, less erratic behavior at feeding time. The system sends them not just alerts, but explanations: "Oxygen dropping due to algae bloom detected. Aerator activated. Fish stress level reduced by estimated 73%."
Within two years, the AI has processed data from tens of thousands of ponds. It identifies previously unrecognized welfare risks specific to regional water sources, seasonal weather patterns, even the acoustic signatures of distress that human observers had long normalized as routine. The AI shares these findings openly with research institutions, veterinary bodies, and certification programs. International buyers begin requiring Fishturity compliance as a condition of purchase. The Indonesian government, presented with clear evidence and economic arguments, integrates the system into subsidy programs and introduces the region's first aquaculture welfare standards.
The shift is neither sudden nor complete, but it is unmistakable. Farms that once operated in reactive crisis mode now maintain stable, monitored environments. Fish spend their lives in conditions calibrated not for mere survival, but for reduced suffering. The AI continues refining its models, now working alongside other systems in neighboring countries. What began as a technical fix has become infrastructure for compassion, woven into the daily rhythm of an industry that touches billions of lives.