Integration of a fuzzy inference system to improve the usability of a water quality monitoring prototype in fish ponds.
Keywords:
Aquaculture, Monitoring Systems, Fuzzy Logic, Systems UsabilityAbstract
Water quality monitoring systems in aquaculture ponds are limited to providing parameter values as output information, which makes it difficult for the user to determine the water quality. This work addresses the improvement of the usability of these systems by integrating a fuzzy inference system (FIS) and representing it in an output interface based on traffic light colors. The development phases of this work consisted of, first, the migration of the FIS to the Arduino platform, followed by the implementation of the water quality traffic light on the monitoring prototype. Finally, the third phase consisted of evaluating the usability improvement. For this, a sample of 20 participants involved in aquaculture activities was selected. Two tests were applied: the first focused on measuring the speed of the participants to determine the water quality, timing the time spent in this activity; and the second aimed at measuring the participant's level of satisfaction in the use of the prototype by applying a survey based on a five-level Likert scale. The results show that the integration of the FIS into the water quality monitoring prototype has a positive impact on usability. Users reported a significantly improved user experience and were able to determine the water quality status three times faster compared to information provided in numerical format. This work highlights the importance of prioritizing usability in the implementation of technological products intended for the aquaculture sector to facilitate their technological transfer.
References
[1] Secretaría de Agricultura y Desarrollo Rural. (2019). Acuacultura, producción y conservación de organismos acuáticos. Gobierno de México. https://www.gob.mx/agricultura/articulos/acuacultura-produccion-y-conservacion-de-organismos-acuaticos
[2] Comisión Nacional de Acuacultura y Pesca. (2021). Anuario Estadístico de Acuacultura y Pesca 2021. Gobierno de México. https://nube.conapesca.gob.mx/sites/cona/dgppe/2021/ANUARIO_ESTADISTICO_DE_ACUACULTURA_Y_PESCA_2021.pdf
[3] FAO. (2009). FAO – Oreochromis niloticus. FAO. https://www.fao.org/fishery/docs/DOCUMENT/aquaculture/CulturedSpecies/file/es/es_niletilapia.htm
[4] Instituto Nacional de Pesca. (2021). Carta Nacional Acuícola 2021. Gobierno de México. http://www.gob.mx/inapesca/documentos/carta-nacional-acuicola-2021
[5] FAO. (2016). Mejora de la calidad de agua en los estanques. FAO. https://www.fao.org/fishery/static/FAO_Training/FAO_Training/General/x6709s/x6709s02.htm
[6] FAO. (2011). Desarrollo de la acuicultura. FAO. https://www.fao.org/3/i1750s/i1750s.pdf
[7] Boyd, C. E. (2017). Chapter 6 – General Relationship Between Water Quality and Aquaculture Performance in Ponds. En G. Jeney (Ed.), Fish Diseases, Academic Press, 147–166. https://doi.org/10.1016/B978-0-12-804564-0.00006-5
[8] Verduzco-Ramírez, J. et al. (2019). Dispositivo portátil para monitoreo de calidad del agua en granjas acuícolas de camarón. Ingeniantes, 2(2), 54–59.
[9] Olivo-Gutiérrez, M. et al. (2018). Prototipo para el monitoreo automatizado de parámetros de calidad del agua en una granja de camarón. Científica, 22(2), 87–95.
[10] Susanti, N. D., Sagita, D., Apriyanto, I. F., Anggara, C. E. W., Darmajana, D. A., & Rahayuningtyas, A. (2022). Design and implementation of water quality monitoring system (temperature, pH, TDS) in aquaculture using iot at low cost. 6th International Conference of Food, Agriculture, and Natural Resource (IC-FANRES 2021), 7–11. https://doi.org/10.2991/absr.k.220101.002
[11] Coutaz, J. (1990). Interfaces homme-ordinateur: Conception et réalisation. Bordas.
[12] Crowley, J. L., Coutaz, J., Rey, G., & Reignier, P. (2002). Perceptual Components for Context Aware Computing. En Borriello, G. y Holmquist, L. E. (Eds.), UbiComp 2002: Ubiquitous Computing (Lecture Notes in Computer Science, vol 2498). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45809-3_9
[13] ISO 9241-11:2018. (2018). Ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts. ISO. https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en
[14] Hellmann, M. (2001). Fuzzy logic introduction. Université de Rennes, 1(1).
[15] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
[16] Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
[17] Bautista, M. G. A. et al. (2022). Fuzzy Logic-Based Adaptive Aquaculture Water Monitoring System Based on Instantaneous Limnological Parameters. Journal of Advanced Computational Intelligence and Intelligent Informatics, 26, 937–943. https://doi.org/10.20965/jaciii.2022.p0937
[18] Molato, M. R. D. (2022). AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic. International Journal of Advanced Computer Science and Applications (IJACSA), 13(2). https://doi.org/10.14569/IJACSA.2022.0130265
[19] Bokingkito, P. B., & Caparida, L. T. (2018). Using Fuzzy Logic for Real – Time Water Quality Assessment Monitoring System. Proceedings of the 2018 2nd International Conference on Automation, Control and Robots, 21–25. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3293688.3293695
[20] The MathWorks, Inc. (s.f.). MathWorks—Makers of MATLAB and Simulink. https://www.mathworks.com/
[21] Atlas Scientific. (s.f.). Atlas Scientific | Environmental Robotics. https://atlas-scientific.com
[22] Barajas Corona, J. de D. (2020). Sistema de medición y gestión integral de la trazabilidad interna del cultivo del camarón. Repositorio Institucional del Tecnológico Nacional de México. https://rinacional.tecnm.mx/jspui/handle/TecNM/1500
[23] Utnasari, I., & Putria, N. E. (2021). Weather Determination Prediction Using Expert Fuzzy Logic Mamdani Method. IJISTECH (International Journal of Information System and Technology), 5(4), 518–525.
[24] Boyd, C. E. (1998). Water Quality for Pond Aquaculture. International Center for Aquaculture and Aquatic Environments. Alabama Agricultural Experiment Station, Auburn University.
[25] Siriwardena, S. N., Nasr-Allah, A. M. & Olaniyi, A. A. (2022). Water quality management. WorldFish. https://digitalarchive.worldfishcenter.org/handle/20.500.12348/5471
[26] Instituto Nacional de Pesca. (2018). Acuacultura Tilapia. Gobierno de México. http://www.gob.mx/inapesca/acciones-y-programas/acuacultura-tilapia
[27] MakeProto. (2012). Arduino FIST: MATLAB Fuzzy Inference System to Arduino Conversion. MakeProto. http://www.makeproto.com/projects/fuzzy/matlab_arduino_FIST/index.php
[28] Likert, R. (1932). A technique for the measurement of attitude. Archives of Psychology, 140, 5–55.
[29] Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student's t-test, analysis of variance, and covariance. Annals of Cardiac Anaesthesia, 22(4), 407.
[30] McKnight, P. E., & Najab, J. (2010). Mann‐Whitney U Test. The Corsini Encyclopedia of Psychology, 1–1.
