Identificación de las principales enfermedades de la planta del café (Coffea arabica) a través de visión artificial
Article Sidebar
Publicado
nov 8, 2023
##plugins.pubIds.doi.readerDisplayName##
https://doi.org/10.30878/ces.v30n3a8
Main Article Content
Oscar Eder Flores Colorado
http://orcid.org/0000-0002-5913-0526
Jair Cervantes Canales
http://orcid.org/0000-0003-2012-8151
Farid García Lamont
http://orcid.org/0000-0002-9739-3802
Jose Sergio Ruíz Castilla
http://orcid.org/0000-0001-7821-4912
http://orcid.org/0000-0002-5913-0526
Jair Cervantes Canales
http://orcid.org/0000-0003-2012-8151
Farid García Lamont
http://orcid.org/0000-0002-9739-3802
Jose Sergio Ruíz Castilla
http://orcid.org/0000-0001-7821-4912
Resumen
Se utilizan técnicas de reconocimiento de patrones para identificar hojas sanas y cuatro enfermedades de la planta del café Coffeea arabica. Las enfermedades son la roya del café, el minador de la hoja, phoma quema y Cercospora coffeicola. Para lograrlo, se ocuparon diferentes técnicas de segmentación, entre ellas Otsu, PCA y método de frontera global. Con el fin de obtener el vector de características, las imágenes se procesaron para extraer las características cromáticas, geométricas y textuales. Por último, se implementaron cuatro algoritmos de clasificación, entre los que se encuentran support vector machine, random forest, Naive Bayes y redes neuronales artificiales backpropagation. La mejor precisión obtenida es del 83% con segmentación Otsu y clasificación con redes neuronales artificiales backpropagation.
Article Details
Como citar
FLORES COLORADO, Oscar Eder et al.
Identificación de las principales enfermedades de la planta del café (Coffea arabica) a través de visión artificial.
CIENCIA ergo-sum, [S.l.], v. 30, n. 3, nov. 2023.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/16715>. Fecha de acceso: 18 ago. 2026
doi: https://doi.org/10.30878/ces.v30n3a8.
Sección
Ciencias exactas y aplicadas
Citas
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96-107.
Bhange, M. & Hingoliwala, H. (2015). Smart Farming: Pomegranate Disease Detection Using Image Processing. Procedia Computer Science, 58, 280-288
Echandi, E. (1957). La quema de los cafetos causada por Phoma costarricensis n. sp. Revista de Biología Tropical, 5(1), 81-102.
Esgario, J. G., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
Flusser, J., Zitova, B., & Suk, T. (2009). Moments and moment invariants in pattern recognition. Wiley Publishing.
Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 93(26), 429-441
Gavhale, K. R., Gawande, U., & Hajari, K. O. (2014). Unhealthy region of citrus leaf detection using image processing techniques. International Conference for Convergence for Technology. IEEE.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610-621.
Henderson, T. P. (2019). La roya y el futuro del café en Chiapas. Revista Mexicana de Sociología, 81(2), 389-416.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441.
Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179-187.
Hubert, M.-C. R., & Torres, R. M. (2017). Política pública y sustentabilidad de los territorios cafetaleros en tiempos de roya: Chiapas y Veracruz. Estudios Latinoamericanos. Universidad Nacional Autónoma de México.
Krohling, R. A., Esgario, G. J. M., & Ventura, J. A. (2019). BRACOL - A Brazilian Arabica coffee leaf images dataset to identification and quantification of coffee diseases and pests. Mendeley Data, V1. https://doi.org/10.17632/yy2k5y8mxg.1
Lewis, K. P., & Espineli, J. D. (2020). Classification and detection of nutritional deficiencies in coffee plants using image processing and convolutional neural network (Cnn). International Journal of Scientific & Technology Research, 9(4), 2076-2081.
Manso, G. L., Knidel, H., Krohling, R. A., & Ventura, J. A. (2019). A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. Computer Vision and Pattern Recognition.
Mengistu, A. D., Alemayehu, D. M., & Mengistu, S. G. (2016). Ethiopian coffee plant diseases recognition based on imaging and machine learning techniques. International Journal of Database Theory and Application, 9(4), 79-88.
Montalbo, F. J. P., & Hernandez, A. A. (2020). An optimized classification model for coffea liberica disease using deep convolutional neural networks. 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 213-218). IEEE.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
Paula, P. V. A. A. D., Pozza, E. A., Alves, E., Moreira, S. I., Paula, J. C. A., & Santos, L. A. (2019). Infection process of Cercospora coffeicola in immature coffee fruits.
Price, T. V., Gross, R., Ho, W. J., & Osborne, C. F. (1993). A comparison of visual and digital image-processing methods in quantifying the severity of coffee leaf rust (Hemileia vastatrix). Australian Journal of Experimental Agriculture, 33(1), 97-101.
Pujari, D., Yakkundimath, R., & Byadgi, A. S. (2016). SVM and ANN based classification of plant diseases using feature reduction technique. International Journal of Interactive Multimedia and Artificial Intelligence, 3(7), 6-14.
Qin, F., Liu, D., Sun, B., Ruan, L., Ma, Z., & Wang, H. Luvisi, A. (Ed.) (2016). Identification of Alfalfa Leaf Diseases Using Image Recognition Technology. PLOS ONE, 11, e0168274.
Ramírez, D., & Jiménez, F. G. (2021). Manejo del minador de la hoja (Leucoptera coffeella) en el cultivo de café en Costa Rica. Agronomía costarricense: Revista de ciencias agrícolas, 45(2), 143-153.
Sampallo, G. (2003). Reconocimiento de tipos de hojas. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 7(21), 55-62.
Vichi, F. F. (2015). La producción de café en México: ventana de oportunidad para el sector agrícola de Chiapas. Espacio I+ D: Innovación más Desarrollo, 4(7).
XinHai, L. (2013). Using random forest for classification and regression. Chinese Journal of Applied Entomology, 50, 1190-1197
Yebasse, M., Shimelis, B., Warku, H., Ko, J., & Cheoi, K. J. (2021) Coffee Disease Visualization and Classification. Plants, 10, 1257. https://doi.org/10.3390/plants10061257
Bhange, M. & Hingoliwala, H. (2015). Smart Farming: Pomegranate Disease Detection Using Image Processing. Procedia Computer Science, 58, 280-288
Echandi, E. (1957). La quema de los cafetos causada por Phoma costarricensis n. sp. Revista de Biología Tropical, 5(1), 81-102.
Esgario, J. G., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
Flusser, J., Zitova, B., & Suk, T. (2009). Moments and moment invariants in pattern recognition. Wiley Publishing.
Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 93(26), 429-441
Gavhale, K. R., Gawande, U., & Hajari, K. O. (2014). Unhealthy region of citrus leaf detection using image processing techniques. International Conference for Convergence for Technology. IEEE.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610-621.
Henderson, T. P. (2019). La roya y el futuro del café en Chiapas. Revista Mexicana de Sociología, 81(2), 389-416.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417–441.
Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179-187.
Hubert, M.-C. R., & Torres, R. M. (2017). Política pública y sustentabilidad de los territorios cafetaleros en tiempos de roya: Chiapas y Veracruz. Estudios Latinoamericanos. Universidad Nacional Autónoma de México.
Krohling, R. A., Esgario, G. J. M., & Ventura, J. A. (2019). BRACOL - A Brazilian Arabica coffee leaf images dataset to identification and quantification of coffee diseases and pests. Mendeley Data, V1. https://doi.org/10.17632/yy2k5y8mxg.1
Lewis, K. P., & Espineli, J. D. (2020). Classification and detection of nutritional deficiencies in coffee plants using image processing and convolutional neural network (Cnn). International Journal of Scientific & Technology Research, 9(4), 2076-2081.
Manso, G. L., Knidel, H., Krohling, R. A., & Ventura, J. A. (2019). A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. Computer Vision and Pattern Recognition.
Mengistu, A. D., Alemayehu, D. M., & Mengistu, S. G. (2016). Ethiopian coffee plant diseases recognition based on imaging and machine learning techniques. International Journal of Database Theory and Application, 9(4), 79-88.
Montalbo, F. J. P., & Hernandez, A. A. (2020). An optimized classification model for coffea liberica disease using deep convolutional neural networks. 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 213-218). IEEE.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
Paula, P. V. A. A. D., Pozza, E. A., Alves, E., Moreira, S. I., Paula, J. C. A., & Santos, L. A. (2019). Infection process of Cercospora coffeicola in immature coffee fruits.
Price, T. V., Gross, R., Ho, W. J., & Osborne, C. F. (1993). A comparison of visual and digital image-processing methods in quantifying the severity of coffee leaf rust (Hemileia vastatrix). Australian Journal of Experimental Agriculture, 33(1), 97-101.
Pujari, D., Yakkundimath, R., & Byadgi, A. S. (2016). SVM and ANN based classification of plant diseases using feature reduction technique. International Journal of Interactive Multimedia and Artificial Intelligence, 3(7), 6-14.
Qin, F., Liu, D., Sun, B., Ruan, L., Ma, Z., & Wang, H. Luvisi, A. (Ed.) (2016). Identification of Alfalfa Leaf Diseases Using Image Recognition Technology. PLOS ONE, 11, e0168274.
Ramírez, D., & Jiménez, F. G. (2021). Manejo del minador de la hoja (Leucoptera coffeella) en el cultivo de café en Costa Rica. Agronomía costarricense: Revista de ciencias agrícolas, 45(2), 143-153.
Sampallo, G. (2003). Reconocimiento de tipos de hojas. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 7(21), 55-62.
Vichi, F. F. (2015). La producción de café en México: ventana de oportunidad para el sector agrícola de Chiapas. Espacio I+ D: Innovación más Desarrollo, 4(7).
XinHai, L. (2013). Using random forest for classification and regression. Chinese Journal of Applied Entomology, 50, 1190-1197
Yebasse, M., Shimelis, B., Warku, H., Ko, J., & Cheoi, K. J. (2021) Coffee Disease Visualization and Classification. Plants, 10, 1257. https://doi.org/10.3390/plants10061257