Application of unsupervised learning in the early detection of late blight in potato crops using image processing
Introducción— La detección automática puede ser útil en la búsqueda de grandes campos de cultivo simplemente detectando la enfermedad con los síntomas que aparecen en la hoja. Objetivo— Este artículo presenta la aplicación de técnicas de aprendizaje automático destinadas a detectar la enfermedad del...
- Autores:
-
Garcia Ariza, Juana Valentina
SUAREZ BARON, MARCO JAVIER
JUNCO ORDUZ, EDMUNDO ARTURO
González-Sanabria, Juan-Sebastián
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9988
- Acceso en línea:
- https://hdl.handle.net/11323/9988
https://repositorio.cuc.edu.co/
- Palabra clave:
- Aprendizaje automático
Aprendizaje no supervisado
Agrupamiento jerárquico
Tizón tardío
K-Means
Machine learning
Unsupervised learning
Hierarchical clustering
Late blight
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| dc.title.translated.none.fl_str_mv |
Aplicación del aprendizaje no supervisado en la detección temprana del tizón tardío en cultivos de papa mediante procesamiento de imágenes |
| title |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| spellingShingle |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing Aprendizaje automático Aprendizaje no supervisado Agrupamiento jerárquico Tizón tardío K-Means Machine learning Unsupervised learning Hierarchical clustering Late blight |
| title_short |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| title_full |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| title_fullStr |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| title_full_unstemmed |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| title_sort |
Application of unsupervised learning in the early detection of late blight in potato crops using image processing |
| dc.creator.fl_str_mv |
Garcia Ariza, Juana Valentina SUAREZ BARON, MARCO JAVIER JUNCO ORDUZ, EDMUNDO ARTURO González-Sanabria, Juan-Sebastián |
| dc.contributor.author.none.fl_str_mv |
Garcia Ariza, Juana Valentina SUAREZ BARON, MARCO JAVIER JUNCO ORDUZ, EDMUNDO ARTURO González-Sanabria, Juan-Sebastián |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje automático Aprendizaje no supervisado Agrupamiento jerárquico Tizón tardío |
| topic |
Aprendizaje automático Aprendizaje no supervisado Agrupamiento jerárquico Tizón tardío K-Means Machine learning Unsupervised learning Hierarchical clustering Late blight |
| dc.subject.proposal.eng.fl_str_mv |
K-Means Machine learning Unsupervised learning Hierarchical clustering Late blight |
| description |
Introducción— La detección automática puede ser útil en la búsqueda de grandes campos de cultivo simplemente detectando la enfermedad con los síntomas que aparecen en la hoja. Objetivo— Este artículo presenta la aplicación de técnicas de aprendizaje automático destinadas a detectar la enfermedad del tizón tardío utilizando métodos de aprendizaje no supervisados como K-Means y agrupamiento jerárquico. Método— La metodología utilizada está compuesta por las siguientes fases— adquisición del dataset, procesamiento de la imagen, extracción de características, selección de características, implementación del modelo de aprendizaje, medición del rendimiento del algoritmo, finalmente se obtuvo una tasa de acierto del 68.24% siendo este el mejor resultado de los algoritmos de aprendizaje no supervisados implementados, usando 3 clusters para el agrupamiento. Resultados— De acuerdo con los resultados obtenidos, se puede evaluar el desempeño del algoritmo K-Means, es decir, 202 aciertos y 116 errores. Conclusiones— Los algoritmos de aprendizaje no supervisado son muy eficientes al momento de procesar una gran cantidad de datos, en este caso una gran cantidad de imágenes sin necesidad de etiquetas predefinidas, su uso para solucionar problemas locales como afectaciones de tizón tardío en cultivos de papa es novedoso. |
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2022 |
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2023-04-17T20:24:48Z |
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J. García-Ariza, M. Suarez-Barón, E. Junco-Orduz & González-Sanabria, “Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing”, INGECUC, vol. 18, no. 2, pp. 89–100. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.07 |
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J. García-Ariza, M. Suarez-Barón, E. Junco-Orduz & González-Sanabria, “Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing”, INGECUC, vol. 18, no. 2, pp. 89–100. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.07 0122-6517 10.17981/ingecuc.18.2.2022.07 2382-4700 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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INGE CUC |
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[1] Minagricultura, Estrategia de ordenamiento de la producción cadena productiva de la papa y su industria. BOG, CO: Minagricultura, 2019. Recuperado de https://sioc.minagricultura.gov.co/Papa/Normatividad/Plan%20de%20Ordenamiento%20papa%202019-2023.pdf [2] C. Ortiz, “Desarrollo de una herramienta computacional basada en redes neuronales para el diagnóstico del tizón tardío en cultivos de papa”, Proyecto de grado, Fac Ing Mec Electron Biomed, UAN, BOG, CO, 2021. Disponible en http://repositorio.uan.edu.co/handle/123456789/5156 [3] D. Rodríguez, M. Rico, L. Rodríguez y C. Ñústez, “Efecto de diferentes niveles y épocas de defoliación sobre el rendimiento de la papa (Solanum tuberosum cv. Parda Pastusa),” Rev Fac Nal Agr MED, vol. 63, no. 2, pp. 5521–5531, Sept. 2009. Disponibl en https://repositorio.unal.edu.co/handle/unal/37086 [4] A.-K. Mahlein, E.-C. Oerke, U. Steiner & H.-W. Dehne, “Recent advances in sensing plant diseases for precision crop protection,” Eur J Plant Pathol, vol. 133, no. 1, pp. 197–209, Mar. 2012. https://doi. org/10.1007/s10658-011-9878-z [5] S. Maity, S. Sarkar, A. Tapadar, A. Dutta, S. Biswas, S. Nayek & P. Saha, “Fault Area Detection in Leaf Diseases Using K-Means Clustering,” presented 2nd International Conference on Trends in Electronics and Informatics, ICOEI, TIRUN, IN, 11-12 May. 2018. https://doi.org/10.1109/ICOEI.2018.8553913 [6] J. Johnson, G. Sharma, S. Srinivasan, S. Masakapalli, S. Sharma, J. Sharma & V. Dua, “Enhanced field-based detection of potato blight in complex backgrounds using deep learning,” Plant Phenomics, pp. 1–13, May. 2021. https://doi.org/10.34133/2021/9835724 [7] P. Sharma, Singh, B. & R. Singh, “Prediction of Potato Late Blight Disease Based Upon Weather Parameters Using Artificial Neural Network Approach,” presented 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT, BLR, IND, 10-12 July 2018. https://doi. org/10.1109/ICCCNT.2018.8494024 [8] R. Hasan, S. Yusuf & L. Alzubaidi, “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, vol. 9, no. 10, pp. 1–25, Oct. 2020. https://doi.org/10.3390/ plants9101302 [9] L. Li, S. Zhang & B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, vol. 9, pp. 56683–56698, Apr. 2021. https://doi.org/10.1109/ACCESS.2021.3069646 [10] H. Pardede, E. Suryawati, R. Sustika & V. Zilvan, “Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases,” presented 2018 International Conference on Computer, Control, Informatics and its Applications, IC3INA, TANG, ID, 1-2 Nov. 2018. https://doi. org/10.1109/IC3INA.2018.8629518 [11] B. Małysiak-Mrozek, D. Mrozek & S. Kozielski, “Data Grouping Process in Extended SQL Language Containing Fuzzy Elements,” in K.A. Cyran, S. Kozielski, J. F. Peters, U. Stańczyk & A. Wakulicz-Deja, Man-Machine Interactions, vol. 59, BE, DE, Springer, 2009, pp. 247–256. https://doi.org/10.1007/978-3- 642-00563-3_25 [12] Z. Khan, T. Akram, S. Naqvi, S. Haider, M. Kamran & N. Muhammad, “Automatic detection of plant diseases; utilizing an unsupervised cascaded design,” presented 15th International Bhurban Conference on Applied Sciences and Technology, IBCAST, ISB, PK, 9-13 Jan. 2018. https://doi.org/10.1109/IBCAST.2018.8312246 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Derechos de autor 2022 INGE CUChttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Garcia Ariza, Juana ValentinaSUAREZ BARON, MARCO JAVIERJUNCO ORDUZ, EDMUNDO ARTUROGonzález-Sanabria, Juan-Sebastián2023-04-17T20:24:48Z2023-04-17T20:24:48Z2022J. García-Ariza, M. Suarez-Barón, E. Junco-Orduz & González-Sanabria, “Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing”, INGECUC, vol. 18, no. 2, pp. 89–100. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.070122-6517https://hdl.handle.net/11323/998810.17981/ingecuc.18.2.2022.072382-4700Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Introducción— La detección automática puede ser útil en la búsqueda de grandes campos de cultivo simplemente detectando la enfermedad con los síntomas que aparecen en la hoja. Objetivo— Este artículo presenta la aplicación de técnicas de aprendizaje automático destinadas a detectar la enfermedad del tizón tardío utilizando métodos de aprendizaje no supervisados como K-Means y agrupamiento jerárquico. Método— La metodología utilizada está compuesta por las siguientes fases— adquisición del dataset, procesamiento de la imagen, extracción de características, selección de características, implementación del modelo de aprendizaje, medición del rendimiento del algoritmo, finalmente se obtuvo una tasa de acierto del 68.24% siendo este el mejor resultado de los algoritmos de aprendizaje no supervisados implementados, usando 3 clusters para el agrupamiento. Resultados— De acuerdo con los resultados obtenidos, se puede evaluar el desempeño del algoritmo K-Means, es decir, 202 aciertos y 116 errores. Conclusiones— Los algoritmos de aprendizaje no supervisado son muy eficientes al momento de procesar una gran cantidad de datos, en este caso una gran cantidad de imágenes sin necesidad de etiquetas predefinidas, su uso para solucionar problemas locales como afectaciones de tizón tardío en cultivos de papa es novedoso.Introduction— Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. Objective— This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. Method— The methodology used is composed by the following phases— acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering. Results— According to the results obtained, the performance of the K-Means algorithm can be evaluated, i.e. 202 hits and 116 misses. Conclusions— Unsupervised learning algorithms are very efficient when processing a large amount of data, in this case a large amount of images without the need for predefined labels, its use to solve local problems such as late blight affectations in potato crops are novel.12 páginasapplication/pdfengCorporación Universidad de la CostaColombiahttps://revistascientificas.cuc.edu.co/ingecuc/article/view/4468Application of unsupervised learning in the early detection of late blight in potato crops using image processingAplicación del aprendizaje no supervisado en la detección temprana del tizón tardío en cultivos de papa mediante procesamiento de imágenesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85INGE CUC[1] Minagricultura, Estrategia de ordenamiento de la producción cadena productiva de la papa y su industria. BOG, CO: Minagricultura, 2019. Recuperado de https://sioc.minagricultura.gov.co/Papa/Normatividad/Plan%20de%20Ordenamiento%20papa%202019-2023.pdf[2] C. Ortiz, “Desarrollo de una herramienta computacional basada en redes neuronales para el diagnóstico del tizón tardío en cultivos de papa”, Proyecto de grado, Fac Ing Mec Electron Biomed, UAN, BOG, CO, 2021. Disponible en http://repositorio.uan.edu.co/handle/123456789/5156[3] D. Rodríguez, M. Rico, L. Rodríguez y C. Ñústez, “Efecto de diferentes niveles y épocas de defoliación sobre el rendimiento de la papa (Solanum tuberosum cv. Parda Pastusa),” Rev Fac Nal Agr MED, vol. 63, no. 2, pp. 5521–5531, Sept. 2009. Disponibl en https://repositorio.unal.edu.co/handle/unal/37086[4] A.-K. Mahlein, E.-C. Oerke, U. Steiner & H.-W. Dehne, “Recent advances in sensing plant diseases for precision crop protection,” Eur J Plant Pathol, vol. 133, no. 1, pp. 197–209, Mar. 2012. https://doi. org/10.1007/s10658-011-9878-z[5] S. Maity, S. Sarkar, A. Tapadar, A. Dutta, S. Biswas, S. Nayek & P. Saha, “Fault Area Detection in Leaf Diseases Using K-Means Clustering,” presented 2nd International Conference on Trends in Electronics and Informatics, ICOEI, TIRUN, IN, 11-12 May. 2018. https://doi.org/10.1109/ICOEI.2018.8553913[6] J. Johnson, G. Sharma, S. Srinivasan, S. Masakapalli, S. Sharma, J. Sharma & V. Dua, “Enhanced field-based detection of potato blight in complex backgrounds using deep learning,” Plant Phenomics, pp. 1–13, May. 2021. https://doi.org/10.34133/2021/9835724[7] P. Sharma, Singh, B. & R. Singh, “Prediction of Potato Late Blight Disease Based Upon Weather Parameters Using Artificial Neural Network Approach,” presented 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT, BLR, IND, 10-12 July 2018. https://doi. org/10.1109/ICCCNT.2018.8494024[8] R. Hasan, S. Yusuf & L. Alzubaidi, “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, vol. 9, no. 10, pp. 1–25, Oct. 2020. https://doi.org/10.3390/ plants9101302[9] L. Li, S. Zhang & B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, vol. 9, pp. 56683–56698, Apr. 2021. https://doi.org/10.1109/ACCESS.2021.3069646[10] H. Pardede, E. Suryawati, R. Sustika & V. Zilvan, “Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases,” presented 2018 International Conference on Computer, Control, Informatics and its Applications, IC3INA, TANG, ID, 1-2 Nov. 2018. https://doi. org/10.1109/IC3INA.2018.8629518[11] B. Małysiak-Mrozek, D. Mrozek & S. Kozielski, “Data Grouping Process in Extended SQL Language Containing Fuzzy Elements,” in K.A. Cyran, S. Kozielski, J. F. Peters, U. Stańczyk & A. Wakulicz-Deja, Man-Machine Interactions, vol. 59, BE, DE, Springer, 2009, pp. 247–256. https://doi.org/10.1007/978-3- 642-00563-3_25[12] Z. Khan, T. Akram, S. Naqvi, S. Haider, M. Kamran & N. Muhammad, “Automatic detection of plant diseases; utilizing an unsupervised cascaded design,” presented 15th International Bhurban Conference on Applied Sciences and Technology, IBCAST, ISB, PK, 9-13 Jan. 2018. https://doi.org/10.1109/IBCAST.2018.831224610089218Aprendizaje automáticoAprendizaje no supervisadoAgrupamiento jerárquicoTizón tardíoK-MeansMachine learningUnsupervised learningHierarchical clusteringLate blightPublicationORIGINALApplication of Unsupervised Learning.pdfApplication of Unsupervised Learning.pdfArtículoapplication/pdf1869424https://repositorio.cuc.edu.co/bitstreams/79d23cb9-3bb7-4a8f-9774-14775398a8c4/downloadec569bfd55f67ee213dd414950bcc7deMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/856d67a2-06f8-4a34-b12a-f82fc151040c/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTApplication of Unsupervised Learning.pdf.txtApplication of Unsupervised Learning.pdf.txtExtracted 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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