Hardware

Overview

During our Human Practices activities, we identified a widespread issue through conversations with aquaculture farmers: feed sinking to the bottom leads to significant waste, as much of the feed is not efficiently utilized, potentially limiting the growth of aquatic animals. Moreover, feed sinking and overfeeding can cause severe water pollution, sometimes even resulting in the mass death of aquatic species (Fig. 1). Thus, "precision feeding" plays a crucial and indispensable role in improving both production efficiency and economic outcomes in aquaculture.

However, we found that there is currently no effective solution to this problem. Most aquaculture practitioners reduce feed input to protect water quality, but this comes at the cost of aquatic animals' growth efficiency. Others try to mitigate feed waste by reducing the amount of feed per session and increasing feeding frequency, which lowers the rate of sinking but significantly raises labor costs and still limits the economic benefits of aquaculture. This practice is not aligned with the Sustainable Development Goals (SDGs) promoting sustainability.

Additionally, when we add β-glucan to the feed to enhance its nutritional value, the cost of the feed increases, and it may also negatively impact water quality. Therefore, whether using regular feed or β-glucan-enriched feed, there is an urgent need for a system that can implement "precision feeding." Hence, we aim to develop a hardware system that enables precision feeding to improve efficiency in aquaculture and support the industry's alignment with the SDGs.

Figure 1 Schematic diagram of water pollution caused by feed sinking

Pollution of sunken and excess feed

To investigate the impact of feed sinking on environmental pollution in aquaculture, we conducted an experiment by adding 15g of fish feed (3mm pellets, composed of fish meal, extruded soybean meal, seaweed powder, brewer’s yeast powder, flour, fish oil, soybean lecithin oil, ferrous sulfate, sodium selenite, manganese sulfate, zinc sulfate, vitamins, microorganisms, and natural minerals) into a 50L water tank (dimensions: 60cm x 30cm x 35cm). At a temperature of approximately 29°C, we observed and photographed the water at 0h, 8h, and 16h intervals (Fig. 2). The results showed that after 8 hours, the water began to show signs of pollution, changing from clear to a yellowish-brown color. By the 16-hour mark, the water had become heavily polluted, turning a murky yellow-green with a noticeable odor.

Figure 2 Pollution status of sunken feed at different time

We also measured the sinking rate of the feed, which was approximately 0.075m/s in our simulated water environment (Video 1). This means that in a pond with a depth of 3 meters, the feed would sink to the bottom in just 40 seconds. Such a short time frame and rapid sinking speed make it difficult for aquatic animals to consume the feed before it reaches the bottom. Additionally, most aquatic species rarely dive to the bottom to feed, leading to the accumulation of feed at the bottom. Therefore, we believe that feed surplus and sinking are significant issues that must be addressed in aquaculture.

Design of the Hardware

To prevent feed from sinking to the bottom, we designed a feed-holding dispenser composed of three parts: a floater, a connecting rod, and a feed holder. Since most aquatic animals do not deliberately seek food at the bottom and prefer fresh feed, we can place the feed directly on the holder, ensuring that it remains available to the animals before sinking. The aquatic animals can easily consume the feed from the holder (Fig. 3).

Figure 3 Schematic diagram of hardware usage

We carefully designed the sizes of these three components based on the density of the simulated materials. To minimize the surface area occupied by the floater, we made it as small as possible while ensuring it provides enough buoyancy for the entire device. The feed holder is designed with an appropriate size to reduce its underwater footprint, avoiding interference with the swimming space of aquatic animals and preventing collisions, while still holding a substantial amount of feed. We also made the connecting rod adjustable in length, allowing us to position the feeder at the optimal depth depending on the activity level of the aquatic animals at different water layers.

Using SolidWorks, we created a hardware model where the floater is a sphere with a diameter of 3 cm and a thickness of 2 mm, the connecting rod is 8 cm long with a 3 mm diameter, and the feed holder is a curved disc with a diameter of 10 cm and a thickness of 3 mm (Fig. 4). We then 3D-printed the physical model and conducted feeding placement experiments in a simulated aquatic environment to test its effectiveness (Fig. 4 and Video 2).

Figure 4 Feeder modeling and 3D-printed model

Issue and Improvement

During our feed placement experiments, we encountered an issue: it was challenging to accurately determine where to feed in an open water surface, resulting in a significant portion of the feed missing the holder and sinking to the bottom. To address this, we sought to design a method that would restrict the feeding area, ensuring that all feed could be precisely placed onto the feed holder, rather than sinking to the bottom again.

As a solution, we added a ring around the floater with a diameter smaller than that of the feed holder (8cm), to delineate the feeding area (Fig. 5). This ensures that the feed remains within the designated range and does not sink, effectively mitigating the issue of feed waste (Video 3).

Figure 5 Feeder modeling and 3D-printed model

Automation Enhancement

In aquaculture, manual feeding is a significant cost factor, and it introduces considerable uncertainty. Variations in feed quantity and feeding rate can greatly impact feed utilization efficiency, leading to higher costs and increased unpredictability. To address these issues, we designed an automatic feeding system based on our existing feeder to achieve "precision feeding and efficient utilization."

First, we improved the floater design by combining the range-defining ring and the floating sphere into a single floating ring (Fig. 6). This modification significantly reduces the surface area occupied by the floater while maintaining its functional effectiveness.

Figure 6 Feeder modeling and 3D-printed model

Additionally, we added an automatic feed dispenser to the top of the feeder, which can dispense a preset amount of feed at a constant rate during specific times (Fig. 7). Simulations of the feeder and the improved design showed that this setup effectively achieves our goal of precision feeding (Video 4).

Figure 7 Schematic diagram of the automatic feeder

Testing and Improvement in Flowing Water Environments

In our simulated water environment, we introduced a water flow simulator to mimic the movement of water in aquaculture settings and evaluate the effectiveness of our device in real-world conditions. During the simulation, we observed that without being anchored, the feeder would drift with the water current, resulting in unstable feeding positions. This instability could make it difficult for aquatic animals to locate the feeding area, or cause the feeder to drift away from the group, leading to inefficiencies in feeding.

To address this issue, we attached a stabilizing rod to the feeder, ensuring that it remains stationary in flowing water. Furthermore, we replaced the stabilizing rod with an adjustable rope, allowing for flexibility in various aquaculture environments while maintaining the feeder’s stability and precision (Fig. 8 and Video 5).

Figure 8 Feeder modeling and 3D-printed model

Final Design of the Hardware System

To better achieve precision feeding and meet our SDG goals, we have developed a comprehensive hardware system that includes a feeder, an automatic feed dispenser, and a feed monitoring device (Fig. 9 and Fig. 10). The feeder holds the feed, while the automatic dispenser ensures a standardized and automated feeding process. After feeding, the feed monitoring device continuously tracks the amount of feed remaining in the holder, assisting us in analyzing whether the feed quantity is optimal.

Based on the analysis, we can adjust the feed amount in real-time through the automatic dispenser, ensuring that the feeding quantity is always at its most appropriate and cost-effective level (Fig. Video 6, Video 7 and Fig. 11). This maximizes feed efficiency, reduces waste, and further supports sustainable aquaculture practices.

Figure 9 Schematic diagram of the real-time monitoring automatic feeder

Figure 10 Model of the real-time monitoring automatic feeder

Conclusion

Our hardware design is driven by the need for precision feeding in aquaculture, combining automation and real-time monitoring technologies to effectively address the issues of feed sinking, waste, and water pollution. Through the integration of an innovative feeder, automatic dispenser, and monitoring system, we have not only improved feed utilization but also significantly reduced the costs associated with aquaculture, contributing to the achievement of SDGs (Fig. 11).

Moving forward, we will continue to optimize this hardware system to ensure its maximum effectiveness across various scales and environments in aquaculture, promoting the green development of the industry. Additionally, we plan to incorporate artificial intelligence technologies to develop a software program that links monitoring results with the automatic feeder, allowing for real-time adjustments in feeding quantities. This initiative will further enhance the automation level of the system, freeing up human resources and enabling aquaculture operators to focus on other important management tasks.

Figure 11 Schematic diagram of the real-time monitoring automatic feeder in operation

References

[1] Naylor, R. L., Hardy, R. W., Buschmann, A. H., Bush, S. R., Cao, L., Klinger, D. H., Little, D. C., Lubchenco, J., Shumway, S. E., & Troell, M. (2021). A 20-year retrospective review of global aquaculture. Nature, 591(7851), 551–563. https://doi.org/10.1038/s41586-021-03308-6
[2] Martins, C. I. M. , Eding, E. H. , Verdegem, M. C. J. , Heinsbroek, L. T. N. , Schneider, O. , & Blancheton, J. P. , et al. (2010). New developments in recirculating aquaculture systems in europe: a perspective on environmental sustainability. Aquacultural Engineering, 43(3), 83-93.
[3] Diaz, R. J., & Rosenberg, R. (2008). Spreading dead zones and consequences for marine ecosystems. Science (New York, N.Y.), 321(5891), 926–929. https://doi.org/10.1126/science.1156401
[4] Garlock, T. M., Asche, F., Anderson, J. L., Eggert, H., Anderson, T. M., Che, B., Chávez, C. A., Chu, J., Chukwuone, N., Dey, M. M., Fitzsimmons, K., Flores, J., Guillen, J., Kumar, G., Liu, L., Llorente, I., Nguyen, L., Nielsen, R., Pincinato, R. B. M., Sudhakaran, P. O., … Tveteras, R. (2024). Environmental, economic, and social sustainability in aquaculture: the aquaculture performance indicators. Nature communications, 15(1), 5274. https://doi.org/10.1038/s41467-024-49556-8
[5] Misra, C. K. , Das, B. K. , Mukherjee, S. C. , & Pattnaik, P. . (2006). Effect of multiple injections of beta-glucan on non-specific immune response and disease resistance in labeo rohita fingerlings. Fish & Shellfish Immunology, 20(3), 305-319.
[6] Costello, C., Cao, L., Gelcich, S., Cisneros-Mata, M. Á., Free, C. M., Froehlich, H. E., Golden, C. D., Ishimura, G., Maier, J., Macadam-Somer, I., Mangin, T., Melnychuk, M. C., Miyahara, M., de Moor, C. L., Naylor, R., Nøstbakken, L., Ojea, E., O'Reilly, E., Parma, A. M., Plantinga, A. J., … Lubchenco, J. (2020). The future of food from the sea. Nature, 588(7836), 95–100. https://doi.org/10.1038/s41586-020-2616-y