Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificult...

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Autores:: Heredia Gómez, Juan F.
Rueda Gómez, Juan P.
Talero Sarmiento, Leonardo H.
Ramírez Acuña, Juan S.
Coronado Silva, Roberto A.

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2020

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.spa.fl_str_mv	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
dc.title.translated.eng.fl_str_mv	Cocoa pods ripeness estimation, using convolutional neural networks in an embedded system
title	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
spellingShingle	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido Cacao Clasificación de Imágenes Detección de objetos Madurez Reconocimiento de Imágenes YOLO Raspberry Pi Cocoa Image classification Object detection Image classification YOLO Ripeness Raspberry pi
title_short	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
title_full	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
title_fullStr	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
title_full_unstemmed	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
title_sort	Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
dc.creator.fl_str_mv	Heredia Gómez, Juan F. Rueda Gómez, Juan P. Talero Sarmiento, Leonardo H. Ramírez Acuña, Juan S. Coronado Silva, Roberto A.
dc.contributor.author.none.fl_str_mv	Heredia Gómez, Juan F. Rueda Gómez, Juan P. Talero Sarmiento, Leonardo H. Ramírez Acuña, Juan S. Coronado Silva, Roberto A.
dc.subject.spa.fl_str_mv	Cacao Clasificación de Imágenes Detección de objetos Madurez Reconocimiento de Imágenes YOLO Raspberry Pi
topic	Cacao Clasificación de Imágenes Detección de objetos Madurez Reconocimiento de Imágenes YOLO Raspberry Pi Cocoa Image classification Object detection Image classification YOLO Ripeness Raspberry pi
dc.subject.keywords.eng.fl_str_mv	Cocoa Image classification Object detection Image classification YOLO Ripeness Raspberry pi
description	Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificulta su estandarización. Con el fin de simplificar la cantidad de variables y presentar un método automatizado, el presente trabajo propone desarrollar una herramienta portable, de bajo costo, y hecha a medida, la cual hace uso de una red neuronal convolucional para indicar si una mazorca de cacao se encuentra en el momento oportuno para ser cosechada. Entre los principales resultados del presente trabajo se encuentran: 1) la construcción de tres conjuntos de datos etiquetados (1992 imágenes cada uno), y 2) un sistema embebido con una precisión de 34.83% mAP (mean Average Precision). Finalmente, se demuestra estadísticamente que el tamaño de las imágenes (4033x4033 p, 1009x1009 p y 505x505 p) no incide sobre la eficacia del entrenamiento.
publishDate	2020
dc.date.issued.none.fl_str_mv	2020-10-19
dc.date.accessioned.none.fl_str_mv	2024-09-06T15:04:13Z
dc.date.available.none.fl_str_mv	2024-09-06T15:04:13Z
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dc.type.local.spa.fl_str_mv	Artículo
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dc.identifier.issn.spa.fl_str_mv	ISSN: 1657-2831 e-ISSN: 2539-2115
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/26394
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga UNAB
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dc.identifier.doi.none.fl_str_mv	https://doi.org/10.29375/25392115.4030
identifier_str_mv	ISSN: 1657-2831 e-ISSN: 2539-2115 instname:Universidad Autónoma de Bucaramanga UNAB repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/26394 https://doi.org/10.29375/25392115.4030
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.spa.fl_str_mv	https://revistas.unab.edu.co/index.php/rcc/article/view/4030/3341
dc.relation.uri.spa.fl_str_mv	https://revistas.unab.edu.co/index.php/rcc/issue/view/267
dc.relation.references.none.fl_str_mv	Alston, J., Pardey, P., & Ruttan, V. (2008). Research Lags Revisited: Concepts and Evidence from U.S. Agriculture. University of Minnesota, Department of Applied Economics, Staff Papers. Arenga, D. Z. H., Dela Cruz, J. C., & Arenga, D. Z. H. (2017). Ripeness classification of cocoa through acoustic sensing and machine learning. 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018-Janua, 1–6. https://doi.org/10.1109/HNICEM.2017.8269438 Arguello Castellanos, O., Mejia Florez, L. A., Contreras Mayorga, N., & Toloza Ochoa, J. A. (1999). Manual de caracterización morfoagronómica de clones elite de cacao (Theobroma cacao L.) en el noriente colombiano. CAOBISCO/ECA/FCC. (2015). Cocoa Beans : Chocolate & Cocoa Industry Quality Requirements. Caragea, C. (2009). Mean Average Precision. En L. Liu & M. T. Özsu (Eds.), Encyclopedia of Database Systems (p. 1703). Springer US. https://doi.org/10.1007/978-0-387-39940-9_3032 Chamo, A., D, A., Babura, B., & Karaye, A. K. (2017). Influence of Agronomic Practices on Crop Production. International Journal of Sciences: Basic and Applied Research (IJSBAR), Vol. 31, 61–66. https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/6688 CORPOICA. (2015). Misión para la transformación del campo. Diagnóstico. 1–71. CORPOICA. (2020). Teobroma Corpoica la Suiza. Cubillos, A., Garcia, M., S., A., R, G., & Tarazona Díaz, M. (2019). Study of the physical and chemical changes during the maturation of three cocoa clones, EET8, CCNN51 and ICS60. Journal of the Science of Food and Agriculture, 99. https://doi.org/10.1002/jsfa.9882 El-Bendary, N., El Hariri, E., Hassanien, A. E., & Badr, A. (2015). Using machine learning techniques for evaluating tomato ripeness. Expert Systems with Applications, 42(4), 1892–1905. https://doi.org/10.1016/j.eswa.2014.09.057 Elhariri, E., El-Bendary, N., Hussein, A. M. M., Hassanien, A. E., & Badr, A. (2014). Bell pepper ripeness classification based on support vector machine. 2014 International Conference on Engineering and Technology (ICET), 1–6. https://doi.org/10.1109/ICEngTechnol.2014.7016802 Hamza, R., & Chtourou, M. (2018). Apple Ripeness Estimation Using Artificial Neural Network. 2018 International Conference on High Performance Computing & Simulation (HPCS), 229–234. https://doi.org/10.1109/HPCS.2018.00049 Huffman, W. (2009). Technology and Innovation in World Agriculture: Prospects for 2010-2019. Iowa State University, Department of Economics, Staff General Research Papers. Kipli, K., Zen, H., Sawawi, M., Mohamad Noor, M. S., Julai, N., Junaidi, N., Shafiq Mohd Razali, M. I., Chin, K. L., & Wan Masra, S. M. (2018). Image Processing Mobile Application For Banana Ripeness Evaluation. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 1–5. https://doi.org/10.1109/ICASSDA.2018.8477600 Lakens, D., Fockenberg, D. A., Lemmens, K. P. H., Ham, J., & Midden, C. J. H. (2013). Brightness differences influence the evaluation of affective pictures. Cognition & Emotion, 27(7), 1225–1246. https://doi.org/10.1080/02699931.2013.781501 Le, T.-T., Lin, C.-Y., & Piedad, E. J. (2019). Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers. Postharvest Biology and Technology, 156, 110922. https://doi.org/https://doi.org/10.1016/j.postharvbio.2019.05.023 León-Roque, N., Abderrahim, M., Nuñez-Alejos, L., Arribas, S. M., & Condezo-Hoyos, L. (2016). Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks. Talanta, 161, 31–39. https://doi.org/10.1016/j.talanta.2016.08.022 Lin, T. (2015). LabelImg. https://github.com/tzutalin/labelImg Machado Cuellar, L., Ordoñez Espinosa, C., Katherine, Y., Cruz, L., & Suárez Salazar, J. (2018). Organoleptic quality assessment of Theobroma cacao L. in cocoa farms in northern Huila, Colombia. Acta Agronómica, 67. https://doi.org/10.15446/acag.v67n1.66572 Mazen, F. M. A., & Nashat, A. A. (2019). Ripeness Classification of Bananas Using an Artificial Neural Network. Arabian Journal for Science and Engineering, 44(8), 6901–6910. https://doi.org/10.1007/s13369-018-03695-5 Mhaski, R. R., Chopade, P. B., & Dale, M. P. (2015). Determination of ripeness and grading of tomato using image analysis on Raspberry Pi. 2015 Communication, Control and Intelligent Systems (CCIS), 214–220. https://doi.org/10.1109/CCIntelS.2015.7437911 Mustafa, N. B. A., Fuad, N. A., Ahmed, S. K., Abidin, A. A. Z., Ali, Z., Yit, W. B., & Sharrif, Z. A. M. (2008). Image processing of an agriculture produce: Determination of size and ripeness of a banana. 2008 International Symposium on Information Technology, 1–7. https://doi.org/10.1109/ITSIM.2008.4631636 Nguyễn, H. V. H., Lê, H. M., & Savage, G. P. (2018). Effects of maturity at harvesting and primary processing of cocoa beans on oxalate contents of cocoa powder. Journal of Food Composition and Analysis, 67, 86–90. https://doi.org/https://doi.org/10.1016/j.jfca.2018.01.007 O’Brien, J. F., & Farid, H. (2012). Exposing photo manipulation with inconsistent reflections. ACM Transactions on Graphics, 31(1), 1–11. https://doi.org/10.1145/2077341.2077345 Park, T. (2020). Darknet with NNPACK. https://github.com/digitalbrain79/darknet-nnpack Perez B, M. A., & Contreras M, J. D. (2017). Instructivo de buenas prácticas de cosecha y pos-cosecha. En Swisscontact Colombia. Polder, G., van der Heijden, G. W. A. M., & Young, I. T. (2002). Spectral Image Analysis for Measuring Ripeness of Tomatoes. Transactions of the ASAE, 45(4), 1155–1161. Ramos Ospino, A. del C., & Gómez Álvarez, M. S. (2019). Caracterización fenotípica y genotípica de aislados de cacao (Theobroma Cacao L.) de Dibulla, Guajira (Vol. 8, Número 5). Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Riskiawan, H. Y., Puspitasari, T. D., Hasanah, F. I., Wahyono, N. D., & Kurnianto, M. F. (2018). Identifying Cocoa ripeness using K-Nearest Neighbor (KNN) Method. 2018 International Conference on Applied Science and Technology (iCAST), 354–357. https://doi.org/10.1109/iCAST1.2018.8751633 Rupanagudi, S. R., Ranjani, B. S., Nagaraj, P., & Bhat, V. G. (2014). A cost effective tomato maturity grading system using image processing for farmers. 2014 International Conference on Contemporary Computing and Informatics (IC3I), 7–12. https://doi.org/10.1109/IC3I.2014.7019591 Saad, H., & Hussain, A. (2006). Classification for the Ripeness of Papayas Using Artificial Neural Network (ANN) and Threshold Rule. 2006 4th Student Conference on Research and Development, 132–136. https://doi.org/10.1109/SCORED.2006.4339325 Saadl, H., Ismaie, A. P., Othmanl, N., Jusohl, M. H., Naim, N. F., & Ahmad, N. A. (2009). Recognizing the ripeness of bananas using artificial neural network based on histogram approach. ICSIPA09 - 2009 IEEE International Conference on Signal and Image Processing Applications, Conference Proceedings, 536–541. https://doi.org/10.1109/ICSIPA.2009.5478715 Samui, P., Roy, S., & Balas, V. (2017). Handbook of Neural Computation 1st Edition. Santos Pereira, L. F., Barbon, S., Valous, N. A., & Barbin, D. F. (2018). Predicting the ripening of papaya fruit with digital imaging and random forests. Computers and Electronics in Agriculture, 145, 76–82. https://doi.org/10.1016/j.compag.2017.12.029 Simbolon, Z. K., Syakry, S. A., Mulyadi, & Syahroni, M. (2019). Separation of the Mature Level of Papaya Callina Fruit Automatically Based on Color (RGB) uses Digital Image Processing. IOP Conference Series: Materials Science and Engineering, 536, 12127. https://doi.org/10.1088/1757-899X/536/1/012127 Taiwo, A., & Bart-Plange, A. (2016). Factors Responsible For Post-Harvest Losses And Their Effects On Rice Producing Farmers: A Case Study Of Afife And Aveyime Rice Projectsin The Volta Region Of Ghana. International Research Journal of Engineering and Technology (IRJET), 3, 1014–1022. Tan, D. S., Leong, R. N., Laguna, A. F., Ngo, C. A., Lao, A., Amalin, D. M., & Alvindia, D. G. (2018). AuToDiDAC: Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot. Crop Protection, 103, 98–102. https://doi.org/https://doi.org/10.1016/j.cropro.2017.09.017 Taofik, A., Ismail, N., Gerhana, Y. A., Komarujaman, K., & Ramdhani, M. A. (2018). Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition. IOP Conference Series: Materials Science and Engineering, 288, 12018. https://doi.org/10.1088/1757-899X/288/1/012018 Třebický, V., Fialová, J., Kleisner, K., & Havlíček, J. (2016). Focal Length Affects Depicted Shape and Perception of Facial Images. PLOS ONE, 11(2), e0149313. https://doi.org/10.1371/journal.pone.0149313 Yen, D., & Nguyễn, H. (2018). Effects of maturity stages and fermentation of cocoa beans on total phenolic contents and antioxidant capacities in raw cocoa powder. Vietnam Journal of Biotechnology, 14, 743–752. https://doi.org/10.15625/1811-4989/14/4/12309 Zhang, L., Jia, J., Gui, G., Hao, X., Gao, W., & Wang, M. (2018). Deep Learning Based Improved Classification System for Designing Tomato Harvesting Robot. IEEE Access, 6, 67940–67950. https://doi.org/10.1109/ACCESS.2018.2879324 Zhu, M. (2004). Recall, precision and average precision.
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spelling	Heredia Gómez, Juan F.dd6cb2c1-cd8b-4b3f-88c3-3dcaa5a18a96Rueda Gómez, Juan P.4e2cf86b-5d70-4115-8420-4204c0e122d7Talero Sarmiento, Leonardo H.2652a2b6-e332-435e-9cf0-291da7a73cf5Ramírez Acuña, Juan S.ab3e036c-0cac-4fa0-876b-8537a0ffb53fCoronado Silva, Roberto A.68a8131d-9a83-4dfe-a25e-47c04de736202024-09-06T15:04:13Z2024-09-06T15:04:13Z2020-10-19ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26394instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.4030Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificulta su estandarización. Con el fin de simplificar la cantidad de variables y presentar un método automatizado, el presente trabajo propone desarrollar una herramienta portable, de bajo costo, y hecha a medida, la cual hace uso de una red neuronal convolucional para indicar si una mazorca de cacao se encuentra en el momento oportuno para ser cosechada. Entre los principales resultados del presente trabajo se encuentran: 1) la construcción de tres conjuntos de datos etiquetados (1992 imágenes cada uno), y 2) un sistema embebido con una precisión de 34.83% mAP (mean Average Precision). Finalmente, se demuestra estadísticamente que el tamaño de las imágenes (4033x4033 p, 1009x1009 p y 505x505 p) no incide sobre la eficacia del entrenamiento.A correct cocoa harvest involves determining a pod maturity. However, this farm activity is usually handmade, using criteria such as Size and Color of the pod; those characteristics differ according to the cocoa variety, making it difficult to standardize. For this reason, this work proposes an automated method to simplify the number of variables to develop a portable, low-cost, and custom-made tool, which makes use of a convolutional neural network to indicate whether a cocoa pod is found it at the right time to harvest. The main results of this work are: 1) the construction of three labeled data sets (1992 images each), and 2) we developed an embedded system with a 34.83% mAP (mean Average Precision) accuracy. Finally, variance analysis demonstrates that image size (i.e., 4033x4033 p, 1009x1009 p, and 505x505 p) does not affect accuracy.application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/4030/3341https://revistas.unab.edu.co/index.php/rcc/issue/view/267Alston, J., Pardey, P., & Ruttan, V. (2008). Research Lags Revisited: Concepts and Evidence from U.S. Agriculture. University of Minnesota, Department of Applied Economics, Staff Papers.Arenga, D. Z. H., Dela Cruz, J. C., & Arenga, D. Z. H. (2017). Ripeness classification of cocoa through acoustic sensing and machine learning. 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018-Janua, 1–6. https://doi.org/10.1109/HNICEM.2017.8269438Arguello Castellanos, O., Mejia Florez, L. A., Contreras Mayorga, N., & Toloza Ochoa, J. A. (1999). Manual de caracterización morfoagronómica de clones elite de cacao (Theobroma cacao L.) en el noriente colombiano.CAOBISCO/ECA/FCC. (2015). Cocoa Beans : Chocolate & Cocoa Industry Quality Requirements.Caragea, C. (2009). Mean Average Precision. En L. Liu & M. T. Özsu (Eds.), Encyclopedia of Database Systems (p. 1703). Springer US. https://doi.org/10.1007/978-0-387-39940-9_3032Chamo, A., D, A., Babura, B., & Karaye, A. K. (2017). Influence of Agronomic Practices on Crop Production. International Journal of Sciences: Basic and Applied Research (IJSBAR), Vol. 31, 61–66. https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/6688CORPOICA. (2015). Misión para la transformación del campo. Diagnóstico. 1–71.CORPOICA. (2020). Teobroma Corpoica la Suiza.Cubillos, A., Garcia, M., S., A., R, G., & Tarazona Díaz, M. (2019). Study of the physical and chemical changes during the maturation of three cocoa clones, EET8, CCNN51 and ICS60. Journal of the Science of Food and Agriculture, 99. https://doi.org/10.1002/jsfa.9882El-Bendary, N., El Hariri, E., Hassanien, A. E., & Badr, A. (2015). Using machine learning techniques for evaluating tomato ripeness. Expert Systems with Applications, 42(4), 1892–1905. https://doi.org/10.1016/j.eswa.2014.09.057Elhariri, E., El-Bendary, N., Hussein, A. M. M., Hassanien, A. E., & Badr, A. (2014). Bell pepper ripeness classification based on support vector machine. 2014 International Conference on Engineering and Technology (ICET), 1–6. https://doi.org/10.1109/ICEngTechnol.2014.7016802Hamza, R., & Chtourou, M. (2018). Apple Ripeness Estimation Using Artificial Neural Network. 2018 International Conference on High Performance Computing & Simulation (HPCS), 229–234. https://doi.org/10.1109/HPCS.2018.00049Huffman, W. (2009). Technology and Innovation in World Agriculture: Prospects for 2010-2019. Iowa State University, Department of Economics, Staff General Research Papers.Kipli, K., Zen, H., Sawawi, M., Mohamad Noor, M. S., Julai, N., Junaidi, N., Shafiq Mohd Razali, M. I., Chin, K. L., & Wan Masra, S. M. (2018). Image Processing Mobile Application For Banana Ripeness Evaluation. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 1–5. https://doi.org/10.1109/ICASSDA.2018.8477600Lakens, D., Fockenberg, D. A., Lemmens, K. P. H., Ham, J., & Midden, C. J. H. (2013). Brightness differences influence the evaluation of affective pictures. Cognition & Emotion, 27(7), 1225–1246. https://doi.org/10.1080/02699931.2013.781501Le, T.-T., Lin, C.-Y., & Piedad, E. J. (2019). Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers. Postharvest Biology and Technology, 156, 110922. https://doi.org/https://doi.org/10.1016/j.postharvbio.2019.05.023León-Roque, N., Abderrahim, M., Nuñez-Alejos, L., Arribas, S. M., & Condezo-Hoyos, L. (2016). Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks. Talanta, 161, 31–39. https://doi.org/10.1016/j.talanta.2016.08.022Lin, T. (2015). LabelImg. https://github.com/tzutalin/labelImgMachado Cuellar, L., Ordoñez Espinosa, C., Katherine, Y., Cruz, L., & Suárez Salazar, J. (2018). Organoleptic quality assessment of Theobroma cacao L. in cocoa farms in northern Huila, Colombia. Acta Agronómica, 67. https://doi.org/10.15446/acag.v67n1.66572Mazen, F. M. A., & Nashat, A. A. (2019). Ripeness Classification of Bananas Using an Artificial Neural Network. Arabian Journal for Science and Engineering, 44(8), 6901–6910. https://doi.org/10.1007/s13369-018-03695-5Mhaski, R. R., Chopade, P. B., & Dale, M. P. (2015). Determination of ripeness and grading of tomato using image analysis on Raspberry Pi. 2015 Communication, Control and Intelligent Systems (CCIS), 214–220. https://doi.org/10.1109/CCIntelS.2015.7437911Mustafa, N. B. A., Fuad, N. A., Ahmed, S. K., Abidin, A. A. Z., Ali, Z., Yit, W. B., & Sharrif, Z. A. M. (2008). Image processing of an agriculture produce: Determination of size and ripeness of a banana. 2008 International Symposium on Information Technology, 1–7. https://doi.org/10.1109/ITSIM.2008.4631636Nguyễn, H. V. H., Lê, H. M., & Savage, G. P. (2018). 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Recall, precision and average precision.Vol. 21 Núm. 2 (2020): Revista Colombiana de Computación (Julio-Diciembre); 42-55CacaoClasificación de ImágenesDetección de objetosMadurezReconocimiento de ImágenesYOLORaspberry PiCocoaImage classificationObject detectionImage classificationYOLORipenessRaspberry piDeterminación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebidoCocoa pods ripeness estimation, using convolutional neural networks in an embedded systeminfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf890641https://repository.unab.edu.co/bitstream/20.500.12749/26394/1/Art%c3%adculo.pdf0978bd4a0914bf652809c337664ea594MD51open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg10096https://repository.unab.edu.co/bitstream/20.500.12749/26394/3/Art%c3%adculo.pdf.jpga7297cc47cce02a1d2e8a87c18b6ca32MD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26394/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open access20.500.12749/26394oai:repository.unab.edu.co:20.500.12749/263942024-09-06 22:00:47.985open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

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