Computer vision techniques to determine the health status in broccoli plantations
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Abstract
The demand for broccoli has increased significantly in the last years due to the benefits of its consumption for human health. This poses new challenges for producers, who increasingly rely on technology to improve production processes, increase yields and thereby meet the current demand. One of the technology fields that has gained interest in crop production the use of Computer Vision models, which can provide support and assistance in food production. This paper proposes an algorithm based on color detection of broccoli, which, at the macro level can identify phytosanitary problems in broccoli plantations; and, at the micro level can be used to identify the product that is suitable for consumption. The algorithm uses open source tools such as OpenCV and Python, so that it can be developed at low cost with results similar or better than those obtained with commercial softwares.
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References
Bajaj, D., & Sharma, S. (2014). A Survey of Machine Vision Techniques for Fruit Sorting and Grading. International Journal of Engineering Research & Technology, 3(7), 1187-1193.
Biswas, H., & Hossain, F. (2013). Automatic Vegetable Recognition System. International Journal of Engineering Science Invention, 2(4), 377-41.
Bradski, G., & Kaehler, A. (2008). Learning OpenCV. Sebastopol, California (USA): O’Reilly Media, Inc.
Dji. (2017). Phantom 3 Professional Specs. Recuperado de: http://www.dji.com/phantom-3-pro/ info# specs.
Fernández, D., Escarabajal, D., Ruiz, A., Conesa, J., & Molina, J. (2013). A digital image-processing-based method for determining the crop coeffcient of lettuce crops in the southeast of Spain. Biosystems engineering , 117, 23-34.
Hamachi, T., Tanabe, H., & Yamawaki, A. (2013). Development of a Generic RGB to HSV Hardware. Proceedings of the 1st International Conference on Industrial Applications Engineering, Kitakyushu, Japan.
Huddar, S., Gowri, S., Keerthana, K., Vasanthi, S., & Rupanagudi, S. (2012). Novel algorithm for segmentation and automatic identifcation of pests on plants using image processing. Computing Communication & Networking Technologies (ICCCNT).
Howe, J. (2013). OpenCV Computer Vision with Python. Birmingham: Packt Publishing Ltd.
Kehr, M., & Díaz, P. (2012). Producción de Brócoli para la Agroindustria. Temuco, Chile. Recuperado de: http://www2.inia.cl/medios/biblioteca/informativos/NR38925.pdf.
Latoschik, M. (2006). Color Models. Recuperado de: https://www.techfak.uni bielefeld.de/ ags/wbski/lehre/digiSA/WS0607/3DVRCG/Vorlesung/8a.RT3DCGVR-color.pdf
MCCTH. (2017). Decreto Ejecutivo N° 1014. Quito-Ecuador: Ministerio Coordinador de Conocimiento y Talento Humano Recuperado de: https://softwarelibre.conocimiento.gob.ec/documentos-tecnicos/decreto-1014/
Ramer, U. (1972). An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing, 1, 244-256.
Santoyo, J., & Martínez, C. (2011). Tecnología de producción de brócoli. Sinaloa, México: Fundación Produce Sinaloa A.C. Recuperado de: http://www.fps.org.mx/portal/index.php/component/phocadownload/category/35-otros?download=171:tecnologia-de-produccion-de-brocoli.
Szeliski, R. (2011). Computer Vision: Algorithms and Applications. London: Springer.
Wilhoit, J., Byler, R., Koslav, M., & Vaughan. (1990). Broccoli Head Sizing Using Image Textural Analysis. Transactions of th ASAE, 33(5), 1736-1740.