Metodología de rápida medición y conteo de microgotas de agua usando procesamiento digital de imágenes

Autores/as

DOI:

https://doi.org/10.24054/rcta.v1i41.2421

Palabras clave:

Procesamiento Digital de Imágenes, Medición de Tamaño de Gotas, Caracterización de Pulverizadores

Resumen

Este artículo sintetiza un procedimiento realizado para el recuento y medición del área y diámetro de gotas individuales procedentes de un generador de niebla piezoeléctrico. Se construyó un conjunto óptico basado en una cámara para capturar imágenes de las gotas de agua a contraluz. Utilizando el software ImageJ, se aplicó la técnica de umbralización auto localizada Sauvola, binarizando simultáneamente las gotas enfocadas y descartando las desenfocadas. Posteriormente, se calculó el área y el diámetro de las gotas; datos que fueron procesaron usando MATLAB. Los resultados muestran que el método se comporta adecuadamente tanto en la binarización de las gotas enfocadas como en el descarte de las gotas desenfocadas en un solo paso, lo que resultó en un recuento fiable de gotas con una medición de 5 micrómetros de precisión.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Wang, S., Dorr, G. J., Khashehchi, M., & He, X. (2015). Performance of selected agricultural spray nozzles using particle image velocimetry. Journal of Agricultural Science and Technology, 17(3), 601-613.

Fritz, B. K., & Hoffmann, W. C. (2016). Measuring spray droplet size from agricultural nozzles using laser diffraction. Journal of Visualized Experiments: JoVE, (115). DOI: https://doi.org/10.3791/54533

Knop, I., Bansmer, S. E., Hahn, V., & Voigt, C. (2021). Comparison of different droplet measurement techniques in the Braunschweig Icing Wind Tunnel. Atmospheric Measurement Techniques, 14(2), 1761-1781. DOI: https://doi.org/10.5194/amt-14-1761-2021

Gollin, D., Brevis, W., Bowman, E. T., & Shepley, P. (2017). Performance of PIV and PTV for granular flow measurements. Granular Matter, 19, 1-16. DOI: https://doi.org/10.1007/s10035-017-0730-9

Huber, F. J., Altenhoff, M., & Will, S. (2016). A mobile system for a comprehensive online-characterization of nanoparticle aggregates based on wide-angle light scattering and laser-induced incandescence. Review of Scientific Instruments, 87(5), 053102. DOI: https://doi.org/10.1063/1.4948288

Maaß, S., Wollny, S., Voigt, A., & Kraume, M. (2011). Experimental comparison of measurement techniques for drop size distributions in liquid/liquid dispersions. Experiments in Fluids, 50, 259-269. DOI: https://doi.org/10.1007/s00348-010-0918-9

Hijazi, B., Decourselle, T., Minov, S. V., Nuyttens, D., Cointault, F., Pieters, J. G., & Vangeyte, J. (2012). The use of high-speed imaging systems for applications in precision agriculture.

Damsohn, M., & Prasser, H. M. (2011). Droplet deposition measurement with high-speed camera and novel high-speed liquid film sensor with high spatial resolution. Nuclear engineering and design, 241(7), 2494-2499. DOI: https://doi.org/10.1016/j.nucengdes.2011.04.016

Minov, S. V., Cointault, F., Vangeyte, J., Pieters, J. G., & Nuyttens, D. (2015). Development of High-Speed Image Acquisition Systems for Spray Characterization Based on Single-Droplet Experiments. Transactions of the ASABE, 58(1), 27-37. DOI: https://doi.org/10.13031/trans.58.10775

Li, X., Liu, Z., Li, B., Feng, X., Liu, X., & Zhou, D. (2020). A novel attentive generative adversarial network for waterdrop detection and removal of rubber conveyor belt image. Mathematical Problems in Engineering, 2020, 1-11. DOI: https://doi.org/10.1155/2020/1037021

Soldati, G., Del Ben, F., Brisotto, G., Biscontin, E., Bulfoni, M., Piruska, A., ... & Della Mea, V. (2018). Microfluidic droplets content classification and analysis through convolutional neural networks in a liquid biopsy workflow. American journal of translational research, 10(12), 4004.

Wang, T., Kwok, T. H., Zhou, C., & Vader, S. (2018). In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of manufacturing systems, 47, 83-92. DOI: https://doi.org/10.1016/j.jmsy.2018.04.003

Shin, Y. J., & Lee, J. B. (2010). Machine vision for digital microfluidics. Review of Scientific Instruments, 81(1), 014302. DOI: https://doi.org/10.1063/1.3274673

Porav, H., Bruls, T., & Newman, P. (2019, May). I can see clearly now: Image restoration via de-raining. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 7087-7093). IEEE. DOI: https://doi.org/10.1109/ICRA.2019.8793486

Wang, L., Yue, X., Liu, Y., Wang, J., & Wang, H. (2019). An intelligent vision-based sensing approach for spraying droplets deposition detection. Sensors, 19(4), 933. DOI: https://doi.org/10.3390/s19040933

Bissell, D., Lai, W., Stegmeir, M., Troolin, D., Pothos, S., & Lengsfeld, C. (2014). An approach to spray characterization by combination of measurement techniques. In ILASS Americas 26th Annual Conference on Liquid Atomization and Spray Systems, Portland.

Ramakrishnan, A., Thomas, C., & Tharakan, T. J. (2015, February). Spray characterisation using combined radon and hough transforms. In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/SPICES.2015.7091435

Sudheer, K. P., & Panda, R. K. (2000). Digital image processing for determining drop sizes from irrigation spray nozzles. Agricultural Water Management, 45(2), 159-167. DOI: https://doi.org/10.1016/S0378-3774(99)00079-7

Zhao, H., Zhou, J., Gu, Y., Ho, C. M. B., Tan, S. H., & Gao, Y. (2018, August). Real-time computing for droplet detection and recognition. In 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) (pp. 589-594). IEEE. DOI: https://doi.org/10.1109/RCAR.2018.8621816

Chong, Z. Z., Tor, S. B., Gañán-Calvo, A. M., Chong, Z. J., Loh, N. H., Nguyen, N. T., & Tan, S. H. (2016). Automated droplet measurement (ADM): an enhanced video processing software for rapid droplet measurements. Microfluidics and Nanofluidics, 20, 1-14. DOI: https://doi.org/10.1007/s10404-016-1722-5

Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern recognition, 33(2), 225-236. DOI: https://doi.org/10.1016/S0031-3203(99)00055-2

Wu, C., Shi, Z., & Govindaraju, V. (2004, August). Fingerprint image enhancement method using directional median filter. In Biometric Technology for Human Identification (Vol. 5404, pp. 66-75). SPIE. DOI: https://doi.org/10.1117/12.542200

Publicado

2023-05-14 — Actualizado el 2023-05-14

Cómo citar

[1]
L. G. Vargas Quiroz, J. P. Montoya, M. A. Muñoz León, D. A. Salazar Paz, y A. Montoya Cañola, «Metodología de rápida medición y conteo de microgotas de agua usando procesamiento digital de imágenes», RCTA, vol. 1, n.º 41, pp. 79–86, may 2023.

Número

Sección

Artículos