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Solar-Powered IoT Shrimp Aquaculture Monitoring System

Undergraduate #164
Discipline: Technology and Engineering
Subcategory: Computer Science & Information Systems
Session: 2
Room: Exhibit Hall

Nicolás A. Mendoza - New Mexico State University
Co-Author(s): Ivan Nieto Gomez, New Mexico State University, NM; Delia Valles-Rosales, Texas A&M University Kingsville, TX



The cultivation of shrimp in aquaculture is costly when aiming to produce high-quality shrimp for consumption. Since shrimp survive in water, there are extra challenges that are not an issue to the average farmer. The variables most important to shrimp aquaculture include pH and temperature levels. They can change drastically throughout the day and require costly manual labor to ensure that each is maintained within a certain threshold for high shrimp yields. To a farmer, it is sometimes more beneficial to sacrifice these yields and forgo constant water sampling to reduce costs. Literature shows aqua farmers in other parts of the world also deal with unreliable electricity. By using solar power, not only is there a reduction of energy consumption for the farmer, but the monitored aquaculture will continuously capture water quality. However, existing systems are much too expensive. Systems that continue to run the risk of power shortages are too costly to be viable for the farmer. In particular, for small-scale producers of shrimp, a water monitoring system must be both low in cost and high in quality. This research aims to explore the field of Internet of Things (IoT) and its use in aquaculture monitoring. My hypothesis is that by using a Raspberry Pi, it is possible to create an IoT system that is relatively low cost and can measure values of pH, temperature, and food levels. The farmer is then notified when intervention is necessary using a smartphone application. The methodology consists of using a solar panel to power the system and QR codes to allow for manual interaction between farmer and system—increasing ease of use. Surveys are conducted with intended users to assess proficiency of the system along with a cost-benefit analysis ensuring affordability. These surveys gauge the farmers’ opinion of their current method of shrimp management, comparing it to their experience with this solar-powered, app-based system. I expect to find the usability and cost of the entire IoT system to be beneficial and a realistic measure in shrimp cultivation. Literature in shrimp aquaculture also suggests this outcome. These results would confirm that shrimp farmers can reduce costs while increasing profit and yields. Future research proposes using machine learning to analyze pH and temperature levels to predict changes in quality. This would provide preventative measures for farmers, ensuring that acceptable values are always obtained within the aquaculture.References: Raju, K. R., & Varma, G. H. (2017). Knowledge Based Real Time Monitoring System for Aquaculture Using IoT. 2017 IEEE 7th International Advance Computing Conference (IACC). doi:10.1109/iacc.2017.0075Goud, C. S., Das, S., Kumar, R., Mahamuni, C. V., & Khedkar, S. (2020). Wireless Sensor Network (WSN) Model for Shrimp Culture Monitoring using Open Source IoT. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). doi:10.1109/icirca48905.2020.9183178

Funder Acknowledgement(s): This study was supported by a stipend from NM AMP awarded to the author of this research along with an expenditures grant from NM AMP awarded to Delia Valles-Rosales PhD, New Mexico State University, Las Cruces, NM. I also would like to thank the National Institute of Food and Agriculture, U.S. Department of Agriculture 'ALFA-LoT-Alliance for Smart Agriculture in the Internet of Things Era' Award Number 2018-38422-28564

Faculty Advisor: Delia Valles-Rosales, delia.rosales@tamuk.edu

Role: All parts of the research were conducted by me.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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