dc.contributor | Talero Sarmiento, Leonardo Hernán | |
dc.contributor | Parra Sánchez, Diana Teresa | |
dc.contributor | Moreno Corzo, Feisar Enrique | |
dc.contributor | Nieves Peña, Néstor Edsgardo | |
dc.contributor | Talero Sarmiento, Leonardo Hernán [0000031387] | |
dc.contributor | Parra Sánchez, Diana Teresa [0001476224] | |
dc.contributor | Moreno Corzo, Feisar Enrique [0001499008] | |
dc.contributor | Nieves Peña, Néstor Edsgardo [0001597250] | |
dc.contributor | Parra Sánchez, Diana Teresa [es&oi=ao] | |
dc.contributor | Moreno Corzo, Feisar Enrique [es&oi=ao] | |
dc.contributor | Talero Sarmiento, Leonardo Hernán [0000-0002-4129-9163] | |
dc.contributor | Parra Sánchez, Diana Teresa [0000-0002-7649-0849] | |
dc.contributor | Moreno Corzo, Feisar Enrique [0000-0002-5007-3422] | |
dc.contributor | Parra Sánchez, Diana Teresa [57195677014] | |
dc.contributor | Talero Sarmiento, Leonardo Hernán [Leonardo-Talero] | |
dc.contributor | Parra Sánchez, Diana Teresa [Diana-Parra-Sanchez-2] | |
dc.creator | Cala Pinzón, Karol Daniela | |
dc.creator | Hernández Flórez, Lisseth Andrea | |
dc.creator | Parra Muñoz, Cristian David | |
dc.date.accessioned | 2022-03-25T20:48:37Z | |
dc.date.available | 2022-03-25T20:48:37Z | |
dc.date.created | 2022-03-25T20:48:37Z | |
dc.date.issued | 2021 | |
dc.identifier | http://hdl.handle.net/20.500.12749/16073 | |
dc.identifier | instname:Universidad Autónoma de Bucaramanga - UNAB | |
dc.identifier | reponame:Repositorio Institucional UNAB | |
dc.identifier | repourl:https://repository.unab.edu.co | |
dc.description.abstract | Este proyecto, presenta el diseño y desarrollo de una aplicación móvil funcional capaz de estimar la producción de cacao, que propone la implementación de técnicas de visión por computador y aprendizaje profundo. Esto se debe a que la detección de objetos en la agricultura es importante para estimar la producción de un cultivo, porque incrementa la certeza en la toma de decisiones por parte de un agricultor, por consiguiente, el diseño propuesto realiza un conteo de las mazorcas de cacao que se encuentran en tres estados de sanidad, ya sea con presencia de monilia, fitóftora o completamente sanas. La aplicación planteada hace uso de una cámara de un dispositivo móvil y el sistema operativo Android. Los elementos presentes en el sistema, son un modelo de aprendizaje de máquina entrenado, un conjunto de datos, y tecnologías que apoyan el proceso de desarrollo de software. En primera instancia, se realiza una revisión de la literatura para profundizar sobre las técnicas, tecnologías, y métricas asociadas con visión artificial y que puedan ser aplicadas en el proyecto. Luego, se propone la selección de un conjunto de imágenes con Theobroma cacao. Asimismo, se plantea la adaptación de un modelo de aprendizaje profundo con una definición de parámetros e hiper parámetros, para posteriormente proponer un diseño y desarrollo de un prototipo móvil que detecta, clasifica y localiza las mazorcas de cacao con sus respectivos estados de sanidad, y a su vez estima la producción en términos de kilogramos de granos de cacao seco, teniendo en cuenta la variedad indicada por el usuario. Los resultados obtenidos dejan la evaluación de 8 modelos, en donde el mejor obtiene una mAP de 80.09% y se determina la incidencia de variables asociadas al balanceo sobre la precisión. | |
dc.language | spa | |
dc.publisher | Universidad Autónoma de Bucaramanga UNAB | |
dc.publisher | Facultad Ingeniería | |
dc.publisher | Pregrado Ingeniería de Sistemas | |
dc.relation | AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet). (2020). Retrieved May 5, 2021, from https://github.com/AlexeyAB/darknet | |
dc.relation | Ali Süzen, A., Gürfidan, R., Kayaalp, K., & Ali Şimşek, M. (2020). Information Technology and Computer Science. Information Technology and Computer Science, 2, 18–23. https://doi.org/10.5815/ijitcs.2020.02.02 | |
dc.relation | Andriod Developers. (2020). Android Studio. https://developer.android.com/studio/ | |
dc.relation | Android Studio Developer. (2020). Introducción a Android Studio. https://developer.android.com/studio/intro#top_of_page | |
dc.relation | Argout, X., Salse, J., Aury, J.-M., Guiltinan, M. J., Droc, G., Gouzy, J., Allegre, M., Chaparro, C., Legavre, T., Maximova, S. N., Abrouk, M., Murat, F., Fouet, O., Poulain, J., Ruiz, M., Roguet, Y., Rodier-Goud, M., Barbosa-Neto, J. F., Sabot, F., … Lanaud, C. (2011). The genome of Theobroma cacao. Nature Genetics, 43(2), 101–108. https://doi.org/10.1038/ng.736 | |
dc.relation | Bargoti, S., & Underwood, J. P. (2017). Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards. Journal of Field Robotics, 34(6), 1039– 1060. https://doi.org/https://doi.org/10.1002/rob.21699 | |
dc.relation | Beitzel, S. M., Jensen, E. C., & Frieder, O. (2009). MAP. Encyclopedia of Database Systems. (2009). In Encyclopedia of Database Systems. Springer US. https://doi.org/10.1007/978-0-387-39940 | |
dc.relation | Bhardwaj, K. K., Banyal, S., & Sharma, D. K. (2019). Chapter 7 - Artificial Intelligence Based Diagnostics, Therapeutics and Applications in Biomedical Engineering and Bioinformatics (V. E. Balas, L. H. Son, S. Jha, M. Khari, & R. B. T.-I. of T. in B. E. Kumar (eds.); pp. 161–187). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-817356-5.00009-7 | |
dc.relation | Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (n.d.). YOLOv4: Optimal Speed and Accuracy of Object Detection. Retrieved May 1, 2021, from https://arxiv.org/abs/2004.10934 | |
dc.relation | Bresilla, K., Perulli, G. D., Boini, A., Morandi, B., Grappadelli, L. C., & Manfrini, L. (2020). Comparing deep-learning networks for apple fruit detection to classical hard-coded algorithms. Acta Horticulturae, 1279, 209–216. https://doi.org/10.17660/ActaHortic.2020.1279.31 | |
dc.relation | Burger, W., & Burge, M. J. (2016). Digital image processing: an algorithmic introduction using Java. Springer. | |
dc.relation | Buzzy, M., Thesma, V., Davoodi, M., & Mohammadpour Velni, J. (2020). Real-Time Plant Leaf Counting Using Deep Object Detection Networks. In Sensors (Vol. 20, Issue 23). https://doi.org/10.3390/s20236896 | |
dc.relation | Cai, Y., Li, H., Yuan, G., Niu, W., Li, Y., Tang, X., & Wang, Y. (2021). YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design. www.aaai.org | |
dc.relation | Cecotti, H., Rivera, A., Farhadloo, M., & Pedroza, M. A. (2020). Grape detection with convolutional neural networks. Expert Systems with Applications, 159, 113588. https://doi.org/10.1016/j.eswa.2020.113588 | |
dc.relation | CMake. (2021). Retrieved May 5, 2021, from https://cmake.org/ | |
dc.relation | Colorizer. (2020). http://colorizer.org/ | |
dc.relation | CUDA Zone | NVIDIA Developer. (2021). Retrieved May 5, 2021, from https://developer.nvidia.com/cuda-zone | |
dc.relation | De La Cruz Medina, J., Vargas Ortiz, M., Del Angel Coronel, O. (2011). CACAO: Operaciones Poscosecha. http://www.fao.org/3/a-au995s | |
dc.relation | Dostert, N., Asunción Cano, José Roque., La Torre, María I., W. M. (2011, October). Hoja botánica: Cacao. http://www.botconsult.com/downloads/Hoja_Botanica_Cacao_2012.pdf | |
dc.relation | Everingham, M., Luc, ·, Gool, V., Christopher, ·, Williams, K. I., Winn, J., Zisserman, A., Everingham, M., Van Gool, L., Leuven, K. U., Williams, B. C. K. I., Winn, J., & Zisserman, A. (2010). The PASCAL Visual Object Classes (VOC) Challenge. Retrieved May 5, 2021, from http://www.flickr.com/ | |
dc.relation | Factors influencing smallholder cocoa production: a management analysis of behavioural decision-making processes of technology adoption and application. (1996). Retrieved September 4, 2020, from https://library.wur.nl/WebQuery/wurpubs/35987 | |
dc.relation | FAOSTAT. (n.d.). Retrieved April 13, 2021, from http://www.fao.org/faostat/en/#data/QC/metadat | |
dc.relation | FEDECACAO y los Cacaocultores, le apuestan a la certificación en Buenas Prácticas Agrícolas. (Abril de 2019). Colombia Cacaotera, p.6. Recuperado de www.fedecacao.com.co | |
dc.relation | Fedecacao. (2005). CARACTERIZACIÓN FISÍCOQUÍMICA Y BENEFICIO DEL GRANO DE CACAO (Theobroma cacao L.) EN COLOMBIA. https://www.fedecacao.com.co/portal/images/recourses/pub_doctecnicos/fe decacao-pub-doc_09B.pdf | |
dc.relation | Fountain, A., & Hütz-Adams, F. (2015). 2015 Cocoa Barometer (USA edition). Retrieved from Barometer Consortium website: http://evols.library.manoa.hawaii.edu/handle/10524/48573 | |
dc.relation | Fountain, A., & Hütz-Adams, F. (2018). Cocoa Barometer 2018. Retrieved from VOICE Network website: https://www.voicenetwork.eu/wpcontent/uploads/2019/07/2018-Cocoa-Barometer.pdf | |
dc.relation | Fountain, A., & Hütz-Adams, F. (2020). Cocoa Barometer 2020. Retrieved from VOICE Network website: https://www.voicenetwork.eu/wpcontent/uploads/2021/03/2020-Cocoa-Barometer-EN.pdf | |
dc.relation | Franceschetti, D. R. (Ed.). (2018). Principles of Robotics & Artificial Intelligence | |
dc.relation | Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., & Zhang, Q. (2020, October 1). Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review. Computers and Electronics in Agriculture, Vol. 177, p. 105687. https://doi.org/10.1016/j.compag.2020.105687 | |
dc.relation | Gayi, S. K., & Tsowou, K. (2017). Cocoa Industry. https://doi.org/10.18356/cfb75b0e-en | |
dc.relation | Gené-Mola, J., Gregorio, E., Auat Cheein, F., Guevara, J., Llorens, J., SanzCortiella, R., Escolà, A., & Rosell-Polo, J. R. (2020). Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow. Computers and Electronics in Agriculture, 168. https://doi.org/10.1016/j.compag.2019.105121 | |
dc.relation | Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (Fourth). | |
dc.relation | Google Colaboratory. (2020). Google Colaboratory. https://colab.research.google.com/notebooks/intro.ipynb#recent=true | |
dc.relation | Habibi Aghdam, H., & Jahani Heravi, E. (2017). Guide to Convolutional Neural Networks. Springer International Publishing. https://doi.org/10.1007/978-3319-57550-6 | |
dc.relation | Heredia Gómez, J. F., & Talero Sarmiento, L. H. (2020). Cocoa Ripeness Dataset TCS 01. https://www.kaggle.com/juanfelipeheredia/cocoa-ripeness-datasettcs-01 | |
dc.relation | Hùng, V. (2020). Tensorflow-yolov4-tflite. https://github.com/hunglc007/tensorflowyolov4-tflite | |
dc.relation | ILRF (2014). The fairness gap: Farmer incomes and solutions to child labor in cocoa. Washington, DC, International Labor Rights Forum | |
dc.relation | Jhuria, M., Kumar, A., & Borse, R. (2013). Image processing for smart farming: Detection of disease and fruit grading. 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), 521–526. https://doi.org/10.1109/ICIIP.2013.6707647 | |
dc.relation | Jiang, Z., Zhao, L., Li, S., Jia, Y., & Liquan, Z. (n.d.). Real-time object detection method for embedded devices. | |
dc.relation | Kang, H., & Chen, C. (2020). Fast implementation of real-time fruit detection in apple orchards using deep learning. Computers and Electronics in Agriculture, 168, 105108. https://doi.org/10.1016/j.compag.2019.105 | |
dc.relation | Kim, P. (2017). Deep Learning BT - MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. Apress. https://doi.org/10.1007/978-1-4842-2845-6_5 | |
dc.relation | Kirk, R., Cielniak, G., & Mangan, M. (2020). L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with OneStage Deep Learning Networks. In Sensors (Vol. 20, Issue 1). https://doi.org/10.3390/s20010275 | |
dc.relation | Koirala, A., Walsh, K. B., Wang, Z., & McCarthy, C. (2019). Deep learning – Method overview and review of use for fruit detection and yield estimation. In Computers and Electronics in Agriculture (Vol. 162, pp. 219–234). Elsevier B.V. https://doi.org/10.1016/j.compag.2019.04.017 | |
dc.relation | Konica Minolta. (2020). Entendiendo El Espacio de Color CIE L*A*B*. https://sensing.konicaminolta.us/mx/blog/entendiendo-el-espacio-de-colorcie-lab/ | |
dc.relation | Krig, S. (2014). Computer Vision Metrics: Survey, Taxonomy, and Analysis (Vol. 9781430259305). https://doi.org/10.1007/978-1-4302-5930-5 | |
dc.relation | Liu, X., Chen, S. W., Liu, C., Shivakumar, S. S., Das, J., Taylor, C. J., … Kumar, V. (2019). Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association. IEEE Robotics and Automation Letters, 4(3), 2296–2303. https://doi.org/10.1109/LRA.2019.2901987 | |
dc.relation | Maldonado, W., & Barbosa, J. C. (2016). Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture, 127, 572–581. https://doi.org/10.1016/j.compag.2016.07. | |
dc.relation | Martínez Guerrero, N. (2016). Aportes de la investigación de FEDECACAO –Fondo Nacional del Cacao al incremento de la productividad y reconocimiento de la calidad del cacao de Colombia | |
dc.relation | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Science/Engineering/Math | |
dc.relation | Mite-Baidal, K., Solís-Avilés, E., Martínez-Carriel, T., Marcillo-Plaza, A., Cruz-Ibarra, E., & Baque-Bustamante, W. (2019). Analysis of Computer Vision Algorithms to Determine the Quality of Fermented Cocoa (Theobroma Cacao): Systematic Literature Review. Advances in Intelligent Systems and Computing, 901, 79–87. https://doi.org/10.1007/978-3-030-10728-4_9 | |
dc.relation | Mordvintsev, A., & Abid K. (2013). Fourier Transform. https://opencv-pythontutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_transforms/py_f ourier_transform/py_fourier_transform.html | |
dc.relation | Motamayor, J. C., Lachenaud, P., da Silva e Mota, J. W., Loor, R., Kuhn, D. N., Brown, J. S., & Schnell, R. J. (2008). Geographic and Genetic Population Differentiation of the Amazonian Chocolate Tree (Theobroma cacao L). PLoS ONE, 3(10), e3311. https://doi.org/10.1371/journal.pone.0003311 | |
dc.relation | NVIDIA cuDNN | NVIDIA Developer. (2021). Retrieved May 5, 2021, from https://developer.nvidia.com/cudnn | |
dc.relation | OpenCV - OpenCV. (2021). Retrieved May 5, 2021, from https://opencv.org | |
dc.relation | OpenCV. (2015). Introduction to SURF (Speeded-Up Robust Features). https://docs.opencv.org/master/df/dd2/tutorial_py_surf_intro.html | |
dc.relation | Pacto por Colombia, pacto por la equidad. (2018). Plan Nacional de Desarrollo. Recuperado de https://colaboracion.dnp.gov.co/CDT/Prensa/ResumenPND2018-2022-final.pdf | |
dc.relation | Patel, H., Jiménez, A., Ceres, R., Annamalai, P., & Lee, W. (2013). A Survey of Computer Vision Methods for Counting Fruits and Yield Prediction | |
dc.relation | PDD-Santander. (2020). Santander siempre contigo y para el mundo Plan de Desarrollo Departamental 2020-2023. Planeación Departamental, 53(9), 1689. | |
dc.relation | Python Software Foundation. (2016). Python Package Index. LabelImg. https://pypi.org/project/labelImg/ | |
dc.relation | Rahnemoonfar, M., & Sheppard, C. (2017). Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors, 17(4), 905. https://doi.org/10.3390/s17040905 | |
dc.relation | Redacción Economía. (20 de Agosto de 2020). El Espectador: Gobierno presentó proyecto de ley para incluir a Colombia en Organización Internacional del Cacao. Recuperado de https://www.elespectador.com/ | |
dc.relation | Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2018). You Only Look Once: Unified, Real-Time Object Detection. Retrieved from http://pjreddie.com/yolo/ | |
dc.relation | Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., Edwards, D. D., Jonathan, S. J. S., & Norvig, P. (1995). Arificial Intelligence: A Modern Approach. Alan Apt | |
dc.relation | Sánchez, V., Z ambrano, J., & Iglesias, C. (2019). La cadena de valor del cacao en América Latina y el Caribe. Retrieved from: http://repositorio.iniap.gob.ec/handle/41000/5382 | |
dc.relation | Schuld, M., & Petruccione, F. (2018). Machine Learning. In Supervised Learning with Quantum Computers (pp. 21–73). Springer International Publishing. https://doi.org/10.1007/978-3-319-96424-9_2 | |
dc.relation | Serrano, S., & Heredia Gómez, J. F. (2020). Cocoa Diseases (YOLOv4). https://www.kaggle.com/serranosebas/enfermedades-cacao-yolov4 | |
dc.relation | Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. https://doi.org/10.1186/s40537-019-0197-0 | |
dc.relation | Solomon, C., & Breckon, T. (2011). Fundamentals of Digital Image Processing. https://doi.org/doi:10.1002/9780470689776. | |
dc.relation | Tan, D. S., Leong, R. N., Laguna, A. F., Ngo, C. A., Lao, A., Amalin, D., & Alvindia, D. (2016). A framework for measuring infection level on cacao pods. 2016 IEEE Region 10 Symposium (TENSYMP), 384–389. https://doi.org/10.1109/TENCONSpring.2016.7519437 | |
dc.relation | Vasconez, J. P., Delpiano, J., Vougioukas, S., & Auat Cheein, F. (2020). Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation. Computers and Electronics in Agriculture, 173, 105348. https://doi.org/10.1016/j.compag.2020.105348 | |
dc.relation | Voora, V., Bermúdez, S., & Larrea, C. (2019). Global Market Report: Cocoa. International Institute for Sustainable Development. | |
dc.relation | Wan, S., & Goudos, S. (2020). Faster R-CNN for multi-class fruit detection using a robotic vision system. Computer Networks, 168, 107036. https://doi.org/10.1016/j.comnet.2019.107036 | |
dc.relation | Wang, C.-Y., Liao, H.-Y. M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., & Hsieh, J.-W. (2019). CSPNET: A NEW BACKBONE THAT CAN ENHANCE LEARNING CAPABILITY OF CNN A PREPRINT. https://github.com/WongKinYiu/CrossStagePartialNetworks. | |
dc.relation | Wang, Q., Nuske, S., Bergerman, M., & Singh, S. (2013). Automated Crop Yield Estimation for Apple Orchards. In J. P. Desai, G. Dudek, O. Khatib, & V. Kumar (Eds.), Experimental Robotics: The 13th International Symposium on Experimental Robotics (pp. 745–758). Springer International Publishing. https://doi.org/10.1007/978-3-319-00065-7_50 | |
dc.relation | White, S., & Kennedy, J. (2018). CMY and CMYK Color Spaces. https://docs.microsoft.com/en-us/windows/win32/wcs/cmy-and-cmyk-colorspaces | |
dc.relation | Zhang, B., Gao, Y., Zhao, S., & Liu, J. (2010). Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor. IEEE Transactions on Image Processing, 19(2), 533–544. https://doi.org/10.1109/TIP.2009.2035882 | |
dc.relation | Zhou, Z., Song, Z., Fu, L., Gao, F., Li, R., & Cui, Y. (2020). Real-time kiwifruit detection in orchard using deep learning on AndroidTM smartphones for yield estimation. Computers and Electronics in Agriculture, 179, 105856. https://doi.org/10.1016/j.compag.2020.105856 | |
dc.relation | Zou, H., Lu, H., Li, Y., Liu, L., & Cao, Z. (2020). Maize tassels detection: a benchmark of the state of the art. Plant Methods, 16(1), 1–15. https://doi.org/10.1186/s13007-020-00651-z | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
dc.rights | Abierto (Texto Completo) | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Atribución-NoComercial-SinDerivadas 2.5 Colombia | |
dc.title | Desarrollo de un prototipo funcional de software para estimar la producción de cacao, haciendo uso de herramientas de aprendizaje profundo y visión por computador | |