Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente
ilustraciones, diagrama
- Autores:
-
Restrepo-Arias, Juan F.
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83849
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Tecnología agrícola
Agricultural technology
Agricultura Inteligente
Clasificación de imágenes
Inteligencia Artificial
Internet de las Cosas
Smart agriculture
Image classification
Artificial Intelligence
Internet of things
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| dc.title.translated.eng.fl_str_mv |
Image classification method, using artificial intelligence techniques, integrated into a smart farming IoT platform |
| title |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| spellingShingle |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Tecnología agrícola Agricultural technology Agricultura Inteligente Clasificación de imágenes Inteligencia Artificial Internet de las Cosas Smart agriculture Image classification Artificial Intelligence Internet of things |
| title_short |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| title_full |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| title_fullStr |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| title_full_unstemmed |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| title_sort |
Método de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligente |
| dc.creator.fl_str_mv |
Restrepo-Arias, Juan F. |
| dc.contributor.advisor.none.fl_str_mv |
Branch Bedoya, John Willian Awad Aubad, Gabriel |
| dc.contributor.author.none.fl_str_mv |
Restrepo-Arias, Juan F. |
| dc.contributor.researchgroup.spa.fl_str_mv |
Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial |
| dc.contributor.orcid.spa.fl_str_mv |
Restrepo Arias, Juan Felipe [0000-0002-9689-1017] Branch Bedoya, John Willian [0000-0002-0378-028X] |
| dc.contributor.cvlac.spa.fl_str_mv |
Restrepo. Felipe [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000487007] |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Tecnología agrícola Agricultural technology Agricultura Inteligente Clasificación de imágenes Inteligencia Artificial Internet de las Cosas Smart agriculture Image classification Artificial Intelligence Internet of things |
| dc.subject.lemb.spa.fl_str_mv |
Tecnología agrícola |
| dc.subject.lemb.eng.fl_str_mv |
Agricultural technology |
| dc.subject.proposal.spa.fl_str_mv |
Agricultura Inteligente Clasificación de imágenes Inteligencia Artificial Internet de las Cosas Smart agriculture |
| dc.subject.proposal.eng.fl_str_mv |
Image classification Artificial Intelligence Internet of things |
| description |
ilustraciones, diagrama |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-24T14:40:41Z |
| dc.date.available.none.fl_str_mv |
2023-05-24T14:40:41Z |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_db06 |
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Text |
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http://purl.org/redcol/resource_type/TD |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/83849 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/83849 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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RedCol LaReferencia |
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Mukhtar, “Robust Optimization of MobileNet for Plant Disease Classification with Fine Tuned Parameters,” in 2021 International Conference on Artificial Intelligence, ICAI 2021, Apr. 2021, pp. 146–151. doi: 10.1109/ICAI52203.2021.9445261. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018, doi: 10.1109/CVPR.2018.00474. P. Liashchynskyi and P. Liashchynskyi, “Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS,” no. 2017, pp. 1–11, 2019, [Online]. Available: http://arxiv.org/abs/1912.06059. URL:http://arxiv.org/abs/1912.06059 H. Alibrahim and S. A. Ludwig, “Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization,” in 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings, 2021, pp. 1551–1559. doi: 10.1109/CEC45853.2021.9504761. J. González, “Still optimizing in the dark ? Bayesian optimization for model configuration and experimental design,” 2016.- University of Sheffield, Sheffield, UK February 18, 2016. Groningen, The Netherlands. URL: http://javiergonzalezh.github.io/presentations/talk_groningen2016.pdf E. Bochinski, T. Senst, and T. Sikora, “Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms,” Proceedings - International Conference on Image Processing, ICIP, vol. 2017-Septe, no. September, pp. 3924–3928, 2018, doi: 10.1109/ICIP.2017.8297018. L. Tani, D. Rand, C. Veelken, and M. 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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Branch Bedoya, John Willian8373bc4285cc9e2e59e8f540f737e1db600Awad Aubad, Gabriel07cb0803e188649cb488cc638deed5a1600Restrepo-Arias, Juan F.ca76523fb1667cccecdda2d08f8d92b0600Gidia: Grupo de Investigación YyDesarrollo en Inteligencia ArtificialRestrepo Arias, Juan Felipe [0000-0002-9689-1017]Branch Bedoya, John Willian [0000-0002-0378-028X]Restrepo. Felipe [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000487007]2023-05-24T14:40:41Z2023-05-24T14:40:41Z2023https://repositorio.unal.edu.co/handle/unal/83849Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramaLa mayor parte de las variables que se miden en un cultivo agrícola solo pueden ser detectadas de manera visual, por ejemplo, el inventario de plantas y frutos, el desarrollo y las etapas fenológicas de un cultivo o la presencia de plagas y enfermedades. La llegada de las tecnologías de la Industria 4.0 a la agricultura, le ha dado la posibilidad a este dominio de resolver muchos de sus problemas. Una de las herramientas que actualmente vienen siendo implementadas son las plataformas basadas en el Internet de las Cosas (IoT por sus siglas en inglés), mejor conocidas como plataformas de Agricultura Inteligente. Sin embargo, muchas veces las explotaciones agrícolas cubren áreas muy grandes y/o remotas, en las cuales no es fácil acceder a recursos de energía o conectividad. Por lo tanto, implementar tecnologías como las ofrecidas por la Industria 4.0 algunas veces se convierte en un desafío. Este trabajo de investigación tiene como objetivo principal aportar en la solución de este tipo de problemas, al proponer un método de clasificación de imágenes integrado a una plataforma de agricultura inteligente, que busca reducir el costo computacional del proceso de clasificación de imágenes digitales, aplicado en un contexto rural con dispositivos que tienen limitaciones en su capacidad de cómputo local. La primera parte de esta investigación se enfoca en la revisión de trabajos previos, con el fin de reconocer cuales son las estrategias arquitectónicas que otros investigadores han propuesto para resolver esta problemática, y en qué tipo de aplicaciones se han enfocado. Con base en esta revisión se seleccionó una arquitectura IoT de referencia, que posteriormente fue usada en la implementación de la solución. Esta arquitectura se basó en el uso de la tecnología de comunicación LoRa (Long Range), especialmente creada para trabajar en contextos con limitaciones de conectividad y energía. Luego se seleccionó el caso de aplicación de la clasificación de enfermedades en plantas, por ser uno de los que más impacto tiene en la economía y productividad de los agricultores, para lo cual se generó un conjunto de datos de imágenes digitales, basado en el conjunto de datos (dataset). PlantVillage, uno de los más usados en investigaciones de este tipo. Posteriormente, con base en los resultados que otros investigadores han obtenido en el entrenamiento de algoritmos de Inteligencia Artificial con el conjunto de datos seleccionado, se hizo una preselección de métodos basados en redes neuronales convolucionales que combinan dos características: (1) un desempeño con exactitud en la clasificación por encima del 90 % y (2) un numero de parámetros de entrenamiento menor de cinco millones. El método seleccionado fue MobileNet, con los siguientes resultados de desempeño: exactitud (Accuracy) del 96,31 %, precisión (Precision) del 95,55 %, sensibilidad (Recall) del 95,93 %, F1—score del 95,72 %, con 3.762.056 de parámetros y un tamaño de 28,7 MB. Finalmente, el método seleccionado fue evaluado en tres escenarios de reducción de su arquitectura, para conocer su robustez al tener que adaptarse a condiciones con limitadas capacidades de cómputo. Para la evaluación se implementó una plataforma de agricultura inteligente en condiciones reales de trabajo, en dos unidades productivas de cultivo de tomate bajo invernadero, obteniendo métricas por encima del 90 % en todos los casos. (Texto tomado de la fuente)Most of the variables measured in an agricultural crop can only be detected visually, for example, the inventory of plants and fruits, the development and phenological stages of a crop, or the presence of pests and diseases. The arrival of industry 4.0 technologies in agriculture has allowed this domain to solve many of its problems. One of the tools currently being implemented is the platforms based on the Internet of Things (IoT), better known as smart agriculture platforms. However, farms often cover very large and/or remote areas where it is not easy to access energy resources or connectivity. Therefore, implementing technologies like those offered by Industry 4.0 sometimes becomes challenging. Therefore, the main objective of this research is to contribute to the solution of this type of problem by proposing an image classification method integrated into a smart agriculture platform. The proposed method seeks to reduce the computational cost of the digital image classification process applied in a rural context with devices that have limitations in their local computing capacity. The very first part of this research focuses on reviewing previous works to recognize the architectural strategies that other researchers have proposed to solve this problem and what type of applications they have focused on. Based on this review, a reference IoT architecture was selected and later used in implementing the solution. This architecture was based on LoRa (Long Range) communication technology, specially created to work in contexts with connectivity and energy limitations. Then, the case of application of the disease classification in plants was selected, as it is one of those that have the greatest impact on the economy and productivity of farmers. Next, a data set of digital images was generated based on the dataset PlantVillage, one of the most used in research of this type. Subsequently, based on the results from previous research works whit plant village dataset, a preselection of methods based on convolutional neural networks was made that combine two characteristics: (1) accurate performance in the classification above 90 % and (2) the number of training parameters less than five million. The selected method was MobileNet, with the following performance results: 96,31 % accuracy, 95,55 % precision, 95,93 % recall, and 95,72 % F1-score, with 3,762,056 parameters and a size of 28.7 MB. Finally, the selected method was evaluated in three reduction scenarios of its architecture to know its robustness when adapting to conditions with limited computing capabilities. For the evaluation, a smart agriculture platform was implemented in real working conditions in two productive units of greenhouse tomato cultivation, obtaining metrics above 90 % in all cases.DoctoradoDoctor en IngenieríaAgricultura inteligenteÁrea Curricular de Ingeniería de Sistemas e Informáticax, 147 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - Sistemas630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materialesTecnología agrícolaAgricultural technologyAgricultura InteligenteClasificación de imágenesInteligencia ArtificialInternet de las CosasSmart agricultureImage classificationArtificial IntelligenceInternet of thingsMétodo de clasificación de imágenes, empleando técnicas de inteligencia artificial, integrado a una plataforma IoT de agricultura inteligenteImage classification method, using artificial intelligence techniques, integrated into a smart farming IoT platformTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDRedColLaReferenciaL. 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URL:https://ubidots.com/EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83849/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL71756752.2023.pdf71756752.2023.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf4692337https://repositorio.unal.edu.co/bitstream/unal/83849/2/71756752.2023.pdfd454656b5d4661b026a0a8a1f2a8e4b3MD52THUMBNAIL71756752.2023.pdf.jpg71756752.2023.pdf.jpgGenerated Thumbnailimage/jpeg4053https://repositorio.unal.edu.co/bitstream/unal/83849/3/71756752.2023.pdf.jpg25b4218fdef3afe6337382e72021dff5MD53unal/83849oai:repositorio.unal.edu.co:unal/838492023-08-05 23:04:03.141Repositorio Institucional Universidad Nacional de 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