The influence of lighting on fingerlings counting

  • Vanir Garcia IFMS
  • Diego André Sant’Ana IFMS
  • Vanda Alice Garcia Zanoni UnB
  • Marcio Carneiro Brito Pache IFMS
  • Marco Hiroshi Naka IFMS
  • Pedro Lucas França Albuquerque UCDB
  • Tiago Lewandowski UCDB
  • Adair da Silva Oliveira Junior UFMS
  • João Victor Araújo Rozales UCDB
  • Milena Wolff Ferreira UCDB
  • Eduardo Quirino Arguelho de Queiroz UCDB
  • José Carlos Marino Almanza UCDB
  • Hemerson Pistori UCDB / UFMS


The search for the automation of processes within fish farms has encouraged the research of new methods for counting fingerlings. The purpose of these devices is to reduce manual labor, increase counting precision and reduce fish stress. The automatic counting system consists of software that uses computer vision techniques to process captured images and a mechanical structure to manipulate fingerlings. A difficulty encountered in image processing is the influence of lighting, which may vary according to the environment, position, and time of day that the device is used. In this paper, we vary the illumination intensity, measured by luxmeter, to identify if this variation interferes in the counting precision. We analyzed five video blocks recorded in the Pacu Project environment in Terenos-MS. A MSE of 9.85 was achieved. The conclusion was that the illumination interferes in the recognition of fingerlings in the video and, consequently, in the counting.


V. Raman, S. Perumal, S. Navaratnam, and S. Fazilah, “Computer assisted counter system for larvae and juvenile fish in malaysian fishing hatcheries by machine learning approach,” Journal of Computers, pp. 423–431, 01 2016.

J. M. d. Brito, T. C. Pontes, K. M. Tsujii, F. E. Araújo, and B. L. Richter, “Automação na tilapicultura: revisão de literatura,” Nutritime, vol. 14, no. 3, pp. 5053–5062, 05 2017.

A. Ibrahin, J. Kolo, B. Aibinu, A. Abdullahi, M. Folorunso, T. Mutitu, I. Aliyu, Kolo, J. Gana, A. Aibinu, J. Agajo, A. Orire, M. Orire, Folorunso, T. Folorunso, Mutiu, and M. Adegboye, “A proposed fish counting algorithm using digital image processing technique,” vol. 5, 03 2017.

S. Abe, T. Takagi, K. Takehara, N. Kimura, T. Hiraishi, K. Komeyama, S. Torisawa, and S. Asaumi, “How many fish in a tank? constructing an automated fish counting system by using ptv analysis,” in International Congress on High-Speed Imaging and Photonics, 2017.

J. Hernández-Ontiveros, E. Inzunza-González, E. García-Guerrero, O. López-Bonilla, S. Infante-Prieto, J. Cárdenas-Valdez, and E. Tlelo-Cuautle, “Development and implementation of a fish counter by using an embedded system,” Computers and Electronics in Agriculture, vol. 145, pp. 53–62, 2018. [Online]. Available: [link].

P. Albuquerque, V. Garcia, A. Oliveira, T. Lewandowski, C. Detweiler, A. Barbosa Goncalves, C. Costa, M. Naka, and H. Pistori, “Automatic live fingerlings counting using computer vision,” Computers and Electronics in Agriculture, vol. 167, p. 105015, 12 2019.

M. Marengoni and S. Stringhini, “Tutorial: Introdução à visão computacional usando opencv,” Revista de Informática Teórica e Aplicada, vol. 16, no. 1, pp. 125–160, 2010. [Online]. Available: [link].

E. Lantsova, T. Voitiuk, T. Zudilova, and A. Kaarna, “Using low-quality video sequences for fish detection and tracking,” in 2016 SAI Computing Conference (SAI), 2016, pp. 426–433.

J. Le and L. Xu, “An automated fish counting algorithm in aquaculture based on image processing,” 01 2017.

B. Zion, “The use of computer vision technologies in aquaculture – a review,” Computers and Electronics in Agriculture, vol. 88, pp. 125–132, 2012. [Online]. Available: [link].

S. Cadieux, F. Michaud, and F. Lalonde, “Intelligent system for automated fish sorting and counting,” vol. 2, 02 2000, pp. 1279 – 1284 vol.2.

I. Karplus, V. Alchanatis, and B. Zion, “Guidance of groups of guppies (poecilia reticulata) to allow sorting by computer vision,” Aquacultural Engineering, vol. 32, no. 3, pp. 509–520, 2005. [Online]. Available: [link].

Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. V. Gool, “Domain adaptive faster r-cnn for object detection in the wild,” 2018.

I. Choi and A. Banerjee, “Multi-scale generalized plane match for optical flow,” 2018.

R. Salazar, A. A. C. Mesa, and L. Y. O. Osorio, “Propuesta de sistema de conteo de alevines de tilapia roja de bajo costo usando técnicas de visión artificial,” 2015.

R. Salazar and A. A. C. Mesa, “Dise˜no y construcción de un equipo portátil para conteo de alevines de tilapia roja,” 2017.

H. Pistori, V. Odakura, J. Monteiro, W. Gonçalves, A. Roel, J. Silva, and B. Machado, “Mice and larvae tracking using a particle filter with an auto-adjustable observation model,” Pattern Recognition Letters, vol. 31, pp. 337–346, 03 2010.

K.-C. Lee, J. Ho, and D. Kriegman, “Nine points of light: acquiring subspaces for face recognition under variable lighting,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, 2001, pp. I–I.

R. Lumauag and M. Nava, “Fish tracking and counting using image processing,” 11 2018, pp. 1–4.

J. S. Park and N. Cho, “Generation of high dynamic range illumination from a single image for the enhancement of undesirably illuminated images,” Multimedia Tools and Applications, vol. 78, 07 2019.

Y. Toh, T. Ng, and B. Liew, “Automated fish counting using image processing,” International Conference on Computational Intelligence and Software Engineering, 12 2009.

R. T. Labuguen, E. J. P. Volante, A. Causo, R. Bayot, G. Peren, R. M. Macaraig, N. J. C. Libatique, and G. L. Tangonan, “Automated fish fry counting and schooling behavior analysis using computer vision,” in 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, 2012, pp. 255–260.

M. C. B. Pache, D. A. Sant’Ana, F. P. C. Rezende, J. V. de Andrade Porto, J. V. A. Rozales, V. A. de Moraes Weber, A. da Silva Oliveira Junior, V. Garcia, M. H. Naka, and H. Pistori, “Non-intrusively estimating the live body biomass of pintado real® fingerlings: A feature selection approach,” Ecological Informatics, vol. 68, p. 101509, 2022. [Online]. Available: [link].

T. Hong Khai, S. N. H. S. Abdullah, M. K. Hasan, and A. Tarmizi, “Underwater fish detection and counting using mask regional convolutional neural network,” Water, vol. 14, no. 2, 2022. [Online]. Available: [link]

C. S. Costa, V. A. G. Zanoni, L. R. V. Curvo, M. de Araújo Carvalho, W. R. Boscolo, A. Signor, M. dos Santos de Arruda, H. H. P. Nucci, J. M. Junior, W. N. Gonçalves, O. Diemer, and H. Pistori, “Deep learning applied in fish reproduction for counting larvae in images captured by smartphone,” Aquacultural Engineering, vol. 97, p. 102225, 2022. [Online]. Available: [link].
GARCIA, Vanir et al. The influence of lighting on fingerlings counting. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 148-153. DOI: