Automatic counting of fish larvae using computer vision based on neural networks
DOI:
https://doi.org/10.17268/sci.agropecu.2022.014Keywords:
aquaculture; automation, automatic counter, ornamental fish, computer visionAbstract
Fish larvae counting is a technique applied in aquaculture to determine the efficiency of the induction stage and to know the number of fertilized larvae. For this reason, the research aims to improve the count of larvae under 3 fundamental pillars: precision, error and time. For this, we carried out an experimental investigation under a completely randomized design with two counting systems: traditional and artificial vision; each one with 10 repetitions, with 2000 larvae; Later, we carried out the count by means of artificial vision using a camera that captured images of a fish tank with moving fish. The results show that the proposed method is feasible for counting larvae, with 92.65% accuracy, 7.41% error and an average time of 61 seconds per repetition in relation to the traditional counting system: accuracy 64.44%, error 35.61% and time 2009.3 s. The developed system can be replicated in the aquaculture sector due to its effectiveness and cost.
References
Bellemo, V., Lim, Z., Lim, G., Nguyen, Q., Xie, Y., et al. (2019). Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. The Lancet Digital Health, 1(1), 35-44.
Cisneros-Montemayor, A., & Cisneros-Mata, M. (2018). A medio siglo de manejo pesquero en el noroeste de México, el futuro de la pesca como sistema socioecológico. Relaciones Estudios de Historia y Sociedad, 39(153), 99-127.
Colorado, M., Abdala, D., Rojas, E., & Martínez, J. (2017). Repoblamiento de peces en el río Ranchería y transferencia tecnológica en el cultivo del pez nativo bocachico (Prochilodus reticulatus), a comunidades campesinas asen-tadas en la zona ribereña del río Ranchería en el depar-tamento de La Guajira , Colombia. Siembra CBA, 1, 79-91.
Contreras-Sánchez, W., Contreras-García, M., Mcdonal-Vera, U., Cruz-Rosado, L., & Martínez-García, R. (2020). Reproducción inducida del robalo chucumite (Centropomus parallelus) en Tabasco, México. AquaTIC, 32, 15-25.
David-Ruales, C., Machado-Fracalossi, D., & Vásquez-Torres, W. (2018). Desarrollo temprano en larvas de peces, clave para el inicio de la alimentación exógena. Revista Lasallista de Investigacion, 15(1), 180–194.
Delgado, M. Á., Cuesta, C. A., & Díaz, A. F. (2019). Evaluación de dos protocolos hormonales para la inducción del celo e inseminación artificial a término fijo (IATF) a vacas en el postparto temprano y en anestro, como herramienta para aumentar la productividad. LOGINN Investigación Científica y Tecnológica, 3(1), 94-104.
Díaz, Á., Cerrud, G., Cerrud, G., Junqueira, G., & Solis, L. (2021). Índices morfométricos y reproducción inducida de Cyphocharax magdalenae (Steindachner, 1878) sardina Maná. Visión Antataura, 5(1), 1-14.
Espinoza, L., Chilli, V., Pepe, R., Pino, J., & Contreras, Z. (2019). Captura, acondicionamiento y primer desove de sargo Anisotremus scapularis en la Región Tacna. Ciencia & Desarrollo, 25, 68-74.
França, P., Garcia, V., da Silva, A., Lewandowski, T., Detweiler, C., et al. (2019). Automatic live fingerlings counting using computer vision. Computers and Electronics in Agriculture, 167(September), 1-9.
Hee-Jee, S., Myeong Kwan, P., & Weon, J. (2020). Automatic Grader for Flatfishes Using Machine Vision. International Journal of Control, Automation and Systems, 18(12), 1-9.
Hernández-Ontiveros, J. M., Inzunza-González, E., García-Guerrero, E. E., López-Bonilla, O. R., Infante-Prieto, S. O., et al. (2018). Development and implementation of a fish counter by using an embedded system. Computers and Electronics in Agriculture, 145(December 2016), 53–62.
Hernández, L., Londoño, J., Hernández, K., & Torres, L. (2019). Los sistemas biofloc: una estrategia eficiente en la producción acuícola. CES Medicina Veterinaria y Zootecnia, 14(1), 70-99.
Khokher, M. R., Little, L. R., Tuck, G. N., Smith, D. V., Qiao, M., et al. (2022). Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing. Canadian Journal of Fisheries and Aquatic Sciences, 79(2), 257–266.
Lu, G., & Luo, M. (2020). Genomes of major fishes in world fisheries and aquaculture: Status, application and perspective. Aquaculture and Fisheries, 5(4), 163-173.
Mayca-Pérez, J., Medina-Ibañez, A., Velásquez-Hurtado, J. E., & Llanos-Zavalaga, L. F. (2017). Representaciones sociales relacionadas a la anemia en niños menores de tres años en comunidades Awajún y Wampis, Perú. Rev Peru Med Exp Salud Publica, 34(3), 414-436.
Mejia, B., Salas, A., & Kemper, G. (2018). An Automatic System Oriented to Counting and Measuring the Geometric Dimensions of Gray Tilapia Fingerlings Based on Digital Image Processing. Proceedings of the 2018 IEEE 25th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2018, 1–4.
Meza, B., & Candelaria, M. (2017). Innovación en el sector acuícola. Ra Ximhai, 13(3), 351–364.
Moncaleano, E., Sánchez, C., & Prieto, C. (2018). Estudio histológico y morfológico del desarrollo embrionario del pez capitán de la sabana (Eremophilus mutisii). Revista U.D.C.A Actualidad & Divulgación Científica, 21(2), 479-490.
Montes-Petro, C., Atencio-García, V., Estrada-Posada, A., & Yepes-Blandón, J. (2019). Reproducción en cautiverio de vizcaína Curimata mivartii con extracto pituitario de carpa. Orinoquia, 23(2), 63–70.
Sánchez-Guashpa, A., Pico-Valencia, P., Jiménez, P., & Holgado-Terriza, J. A. (2021). Sistema de Clasificación Automático de Peces Endémicos del Ecuador Usando Redes Neuronales Convolucionales. Revista Iberica de Sistemas e Tecnologias de Informacao, 45, 444-457.
Puig-Pons, V., Muñoz-Benavent, P., Espinosa, V., Andreu-García, G., Valiente-González, J. M., et al. (2019). Automatic Bluefin Tuna (Thunnus thynnus) biomass estimation during transfers using acoustic and computer vision techniques. Aquacultural Engineering, 85, 22–31.
Rojas, I., & Salazar, V. (2018). La acuicultura frente a los impactos de la actividad agrícola en la calidad de los servicios ambientales de la cuenca del río mayo. Una propuesta para su abordaje desde la economía ecológica. Revista de Alimentación Contemporánea y Desarrollo Regional, 28(51), 2-26.
Rosas-Echevarría, C., Solís-Bonifacio, H., & Cerna-Cueva, A. (2019). Sistema eficiente y de bajo costo para la selección de granos de café: una aplicación de la visión artificial. Scientia Agropecuaria, 10(3), 347-351.
Santos, D., Dallos, L., & Gaona-García, P. (2020). Algoritmos de rastreo de movimiento utilizando técnicas de inteligencia artificial y machine learning. Información Tecnológica, 31(3), 23-38.
Vallejo, H., Paucar, J., & Martinez, O. (2018). Visión artificial mediante el coeficiente de correlación para exámenes de retinoscopía. Maskay, 8(2), 75.
Vásquez-Salazar, R. D., & Cardona-Mesa, A. A. (2017). Diseño y construcción de un equipo portátil para conteo de alevines de tilapia roja. Revista Politécnica, 13, 101-111.
Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D., & Chen, Y. (2021). Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review. Archives of Computational Methods in Engineering, 28(4), 2785–2816.
Yu, X., Wang, Y., An, D., & Wei, Y. (2022). Counting method for cultured fishes based on multi-modules and attention mechanism. Aquacultural Engineering, 96, 102215.
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