Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
Palabras clave:
Cacao, Clasificación de Imágenes, Detección de objetos, Madurez, Reconocimiento de Imágenes, YOLO, Raspberry Pi
Resumen
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.
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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
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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.
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.
Cómo citar
Heredia-Gómez, J. F., Rueda-Gómez, J. P., Talero-Sarmiento, L. H., Ramírez-Acuña, J. S., & Coronado-Silva, R. A. (2020). Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido. Revista Colombiana De Computación, 21(2), 42–55. Recuperado a partir de https://revistas.unab.edu.co/index.php/rcc/article/view/4030
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Publicado
2020-12-01
Sección
Artículo de investigación científica y tecnológica
