Interpretability in the Field of Plant Disease Detection: A Review
The early detection of diseases in plants through artificial intelligence techniques has been a very important technological advance for agriculture since, through machine learning and optimization algorithms, it has been possible to increase the yield of various crops in several countries around th...
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2021
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13495 |
| _version_ | 1801706093874774016 |
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| author | Leal-Lara, Daniel-David Barón-Velandia, Julio Rocha-Calderón, Camilo-Enrique |
| author_facet | Leal-Lara, Daniel-David Barón-Velandia, Julio Rocha-Calderón, Camilo-Enrique |
| author_sort | Leal-Lara, Daniel-David |
| collection | OJS |
| description | The early detection of diseases in plants through artificial intelligence techniques has been a very important technological advance for agriculture since, through machine learning and optimization algorithms, it has been possible to increase the yield of various crops in several countries around the world. Different researchers have focused their efforts on developing models that allow supporting the task of detecting diseases in plants as a solution to the traditional techniques used by farmers. In this systematic literature review, an analysis of the most relevant articles is presented, in which image processing techniques and machine learning were used to detect diseases by means of images of the leaves of different crops. In turn, an analysis of the interpretability and precision of these methods is carried out, considering each phase of the image processing, segmentation, feature extraction and learning processes of each model. In this way, there is evidence of a void in the field of interpretability since the authors have focused mainly on obtaining good results in their models, beyond providing the user with a clear explanation of the characteristics of the model. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-13495 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2021 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-134952023-05-31T16:25:31Z Interpretability in the Field of Plant Disease Detection: A Review Interpretabilidad en el campo de la detección de enfermedades en las plantas: Una revisión Leal-Lara, Daniel-David Barón-Velandia, Julio Rocha-Calderón, Camilo-Enrique Machine Learning Classification Early detection of diseases Interpretability Image processing Aprendizaje Automático Clasificación Detección temprana de enfermedades Interpretabilidad Procesamiento de imágenes The early detection of diseases in plants through artificial intelligence techniques has been a very important technological advance for agriculture since, through machine learning and optimization algorithms, it has been possible to increase the yield of various crops in several countries around the world. Different researchers have focused their efforts on developing models that allow supporting the task of detecting diseases in plants as a solution to the traditional techniques used by farmers. In this systematic literature review, an analysis of the most relevant articles is presented, in which image processing techniques and machine learning were used to detect diseases by means of images of the leaves of different crops. In turn, an analysis of the interpretability and precision of these methods is carried out, considering each phase of the image processing, segmentation, feature extraction and learning processes of each model. In this way, there is evidence of a void in the field of interpretability since the authors have focused mainly on obtaining good results in their models, beyond providing the user with a clear explanation of the characteristics of the model. La detección temprana de enfermedades en las plantas mediante técnicas de inteligencia artificial, ha sido un avance tecnológico muy importante para la agricultura, ya que por medio del aprendizaje automático y algoritmos de optimización, se ha logrado incrementar el rendimiento de diversos cultivos en varios países alrededor del mundo. Distintos investigadores han enfocado sus esfuerzos en desarrollar modelos que permitan apoyar la tarea de detección de enfermedades en las plantas como solución a las técnicas tradicionales utilizadas por los agricultores. En esta revisión sistemática de literatura se presenta un análisis de los artículos más relevantes, en los que se usaron técnicas de procesamiento de imágenes y aprendizaje automático, para detectar enfermedades por medio de imágenes de las hojas de diferentes cultivos, y a su vez se lleva a cabo un análisis de interpretabilidad y precisión de estos métodos, teniendo en cuenta cada fase las fases de procesamiento de imágenes, segmentación, extracción de características y aprendizaje, de cada uno de los modelos. De esta manera se evidencia vacío en el campo de la interpretabilidad, ya que los autores se han enfocado principalmente en obtener buenos resultados en sus modelos, más allá de brindar al usuario una explicación clara de las características propias del modelo. Universidad Pedagógica y Tecnológica de Colombia 2021-11-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13495 10.19053/01211129.v30.n58.2021.13495 Revista Facultad de Ingeniería; Vol. 30 No. 58 (2021): October-December 2021 (Continuous Publication); e13495 Revista Facultad de Ingeniería; Vol. 30 Núm. 58 (2021): Octubre-Diciembre 2021 (Publicación Continua) ; e13495 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13495/11176 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13495/11300 Copyright (c) 2021 Daniel-David Leal-Lara, Julio Barón-Velandia, Camilo-Enrique Rocha-Calderón http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Machine Learning Classification Early detection of diseases Interpretability Image processing Aprendizaje Automático Clasificación Detección temprana de enfermedades Interpretabilidad Procesamiento de imágenes Leal-Lara, Daniel-David Barón-Velandia, Julio Rocha-Calderón, Camilo-Enrique Interpretability in the Field of Plant Disease Detection: A Review |
| title | Interpretability in the Field of Plant Disease Detection: A Review |
| title_alt | Interpretabilidad en el campo de la detección de enfermedades en las plantas: Una revisión |
| title_full | Interpretability in the Field of Plant Disease Detection: A Review |
| title_fullStr | Interpretability in the Field of Plant Disease Detection: A Review |
| title_full_unstemmed | Interpretability in the Field of Plant Disease Detection: A Review |
| title_short | Interpretability in the Field of Plant Disease Detection: A Review |
| title_sort | interpretability in the field of plant disease detection a review |
| topic | Machine Learning Classification Early detection of diseases Interpretability Image processing Aprendizaje Automático Clasificación Detección temprana de enfermedades Interpretabilidad Procesamiento de imágenes |
| topic_facet | Machine Learning Classification Early detection of diseases Interpretability Image processing Aprendizaje Automático Clasificación Detección temprana de enfermedades Interpretabilidad Procesamiento de imágenes |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13495 |
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