Keywords
Artificial Intelligence, Mauritia Flexuosa, Neural Networks, Visual Recognition, Watson Inteligencia Artificial, Maurita Flexuosa, Redes Neuronales, Reconocimiento Visual, Watson.
How to Cite
Acosta Rodríguez, G. A., Hernández González, J. M. ., Figueroa , E. C. ., & Salamanca Mahecha, D. . (2021). DEVELOPMENT OF A PROTOTYPE OF A VISUAL RECOGNITION SYSTEM TO DETERMINE THE DEGREE OF MATURITY OF MAURITIA FLEXUOSA IN THE DEPARTMENT OF GUAVIARE. Revista Sennova: Revista Del Sistema De Ciencia, Tecnología E Innovación, 6. https://doi.org/10.23850/23899573.3255
Abstract
Realizar un prototipo de un Sistema de Reconocimiento Visual (SRV) que por medio de redes neuronales (inteligencia artificial) con acoplación de la caracterización del objeto a evaluar tendrá como funcionalidad la identificación de la madurez del fruto de la palma de moriche (Mauritia flexuosa) por medio de una imagen utilizando drones para los registros fotográficos de los racimos de la misma. Esto con el fin, primero, de minimizar la intervención humana en los humedales que son hábitats naturales de la palma para mantener el equilibrio en la fauna y flora propio de este ecosistema. Segundo, crear una alternativa económica legal y justa para las familias campesinas y comunidades indígenas del Municipio de San José del Guaviare.
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Adolf, R. G., Nelson, U., Pablo, A. J., & Marcela, C. J. (2018). BIOPROSPECCIÓN DE ACEITE DE MORICHE – Mauritia Resumen 1 Introducción.
Berner, D. K., & Paxson, L. K. (2003). Use of digital images to differentiate reactions of collections of yellow starthistle (Centaurea solstitialis) to infection by Puccinia jaceae. Biological Control, 28(2), 171–179. https://doi.org/10.1016/S1049-9644(03)00096-3
Camargo, A., & Smith, J. S. (2009a). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering, 102(1), 9–21. https://doi.org/10.1016/j.biosystemseng.2008.09.030
Golden, R. C., KIM, B., BOESE, DENNIS, J., P., & D., C. (2008). DIGITAL IMAGE ANALYSIS OF ZOSTERA MARINA LEAF INJURY.
Hairuddin, M. A., Md Tahir, N., & Baki, S. R. S. (2011). Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis. Proceedings - 2011 IEEE International Conference on System Engineering and Technology, ICSET 2011, 116–120. https://doi.org/10.1109/ICSEngT.2011.5993432
Kai, S., Liu, Z., Su, H., & Guo, C. (2011). A research of maize disease image recognition of corn based on BP networks. Proceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011, 1, 246–249. https://doi.org/10.1109/ICMTMA.2011.66
Lindow, S. E. (1983). Quantification of Foliar Plant Disease Symptoms by Microcomputer-Digitized Video Image Analysis. Phytopathology, 73(4), 520. https://doi.org/10.1094/phyto-73-520
Macedo-Cruz, A., Pajares, G., Santos, M., & Villegas-Romero, I. (2011). Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage. Sensors, 11(6), 6015–6036. https://doi.org/10.3390/s110606015
Olmstead, J. W., Lang, G. A., & Grove, G. G. (2001). Assessment of severity of powdery mildew infection of sweet cherry leaves by digital image analysis. HortScience, 36(1), 107–111. https://doi.org/10.21273/hortsci.36.1.107
Pang, J., Bai, Z. Y., Lai, J. C., & Li, S. K. (2011). Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing.
Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99. https://doi.org/10.1016/j.compag.2010.06.009
Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision-based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593. https://doi.org/10.1002/jsfa.8080
Trey, Z. F. O., Goore, B. T., & Konan, B. M. (2020). Classification of Plant Species by Similarity Using Automatic Learning. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 311 LNICST, 186–201. https://doi.org/10.1007/978-3-030-41593-8_14
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