Aprendizaje Profundo aplicado a la clasificación de granos de cacao (Theobroma cacao L.) según la calidad de fermentación
Resumen
La fermentación del grano de Theobroma cacao L. es un proceso postcosecha importante para el desarrollo de sus propiedades y del aroma. Aunque se trata de un proceso complejo, los agricultores utilizan métodos empíricos/tradicionales para determinar su grado de fermentación, como es la “Prueba de corte” realizada manualmente. No obstante, este tipo de técnicas podría tener una alternativa basada en computadores. Por ello, en este estudio se analizó el uso de redes neuronales convolucionales (CNN) basadas en el aprendizaje profundo, para determinar el grado de fermentación de los granos de cacao. Para el efecto, se desarrolló un modelo cuyo rendimiento fue comprobado en términos de precisión y matriz de confusión. Este modelo alcanzó una exactitud positiva del 82 % y una matriz de confusión con números favorables en los elementos diagonales. Estos resultados dan muestra de que CNN es una opción viable para la clasificación de granos de cacao según su fermentación.
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OLIVEIRA, Marciano M., CERQUEIRA, Breno V., BARBON, Sylvio and BARBIN, Douglas F. Classification of fermented cocoa beans (cut test) using computer vision. Journal of Food Composition and Analysis. 1 April 2021. Vol. 97, p. 103771. DOI 10.1016/J.JFCA.2020.103771.
HUE, C., GUNATA, Z., BREYSSE, A., DAVRIEUX, F., BOULANGER, R. and SAUVAGE, F. X. Impact of fermentation on nitrogenous compounds of cocoa beans (Theobroma cacao L.) from various origins. Food Chemistry. 1 February 2016. Vol. 192, p. 958–964. DOI 10.1016/J.FOODCHEM.2015.07.115.
CAMU, Nicholas, DE WINTER, Tom, ADDO, Solomon K., TAKRAMA, Jemmy S., BERNAERT, Herwig and DE VUYST, Luc. Fermentation of cocoa beans: influence of microbial activities and polyphenol concentrations on the flavour of chocolate. Journal of the Science of Food and Agriculture. Online. 1 October 2008. Vol. 88, no. 13, p. 2288–2297. [Accessed 16 December 2021]. DOI 10.1002/JSFA.3349.
MORÁN, Bajaña, ANGEL, Luis, GALLARDO, Paladines and AARÓN, Josué. Diseño e implementación de un prototipo clasificador de granos de cacao usando Visión Artificial y Machine Learning. Online. 2021. [Accessed 11 December 2021]. Available from: http://dspace.ups.edu.ec/handle/123456789/21405
VAN DURME, Jim, INGELS, Isabel and DE WINNE, Ann. Inline roasting hyphenated with gas chromatography–mass spectrometry as an innovative approach for assessment of cocoa fermentation quality and aroma formation potential. Food Chemistry. 15 August 2016. Vol. 205, p. 66–72. DOI 10.1016/J.FOODCHEM.2016.03.004.
KONGOR, JE, TAKRAMA, JF, BUDU, AS and MENSAH-BROWN, H. Effects of fermentation and drying on the fermentation index and cut test of pulp pre-conditioned Ghanaian cocoa (Theobroma cacao) beans. Online. 2013. [Accessed 24 December 2021]. Available from: http://csirspace.csirgh.com/handle/123456789/528
EL NAQA, Issam and MURPHY, Martin J. What Is Machine Learning? Machine Learning in Radiation Oncology. Online. 2015. P. 3–11. [Accessed 13 February 2022]. DOI 10.1007/978-3-319-18305-3_1.
ADHITYA, Yudhi, PRAKOSA, Setya Widyawan, KÖPPEN, Mario and LEU, Jenq Shiou. Convolutional Neural Network Application in Smart Farming. Springer. Online. 28 August 2019. Vol. 1100, p. 287–297. [Accessed 11 December 2021]. DOI 10.1007/978-981-15-0399-3_23.
ZHOU, Bolei, KHOSLA, Aditya, LAPEDRIZA, Agata, OLIVA, Aude and TORRALBA, Antonio. Object Detectors Emerge in Deep Scene CNNs. Computer Vision and Pattern Recognition (cs.CV). Online. 22 December 2014. [Accessed 16 December 2021]. Available from: https://arxiv.org/abs/1412.6856v2
SANTOS, Luís, SANTOS, Filipe N., OLIVEIRA, Paulo Moura and SHINDE, Pranjali. Deep Learning Applications in Agriculture: A Short Review. Advances in Intelligent Systems and Computing. Online. 20 November 2019. Vol. 1092 AISC, p. 139–151. [Accessed 16 December 2021]. DOI 10.1007/978-3-030-35990-4_12.
SIDDIQUI, Zahid Ali, PARK, Unsang, LEE, Sang Woong, JUNG, Nam Joon, CHOI, Minhee, LIM, Chanuk and SEO, Jang Hun. Robust Powerline Equipment Inspection System Based on a Convolutional Neural Network. Sensors. Online. 8 November 2018. Vol. 18, no. 11, p. 3837. [Accessed 16 December 2021]. DOI 10.3390/S18113837.
SRINIVAS, Suraj, SARVADEVABHATLA, Ravi Kiran, MOPURI, Konda Reddy, PRABHU, Nikita, KRUTHIVENTI, Srinivas S.S. and BABU, R. Venkatesh. A taxonomy of deep convolutional neural nets for computer vision. Frontiers Robotics AI. 1 January 2016. P. 36. DOI 10.3389/FROBT.2015.00036/BIBTEX.
HOWARD, Andrew G. Some Improvements on Deep Convolutional Neural Network Based Image Classification. Computer Vision and Pattern Recognition. Online. 19 December 2013. [Accessed 16 December 2021]. Available from: https://arxiv.org/abs/1312.5402v1
JMOUR, Nadia, ZAYEN, Sehla and ABDELKRIM, Afef. Convolutional neural networks for image classification. 2018 International Conference on Advanced Systems and Electric Technologies, IC_ASET 2018. 11 June 2018. P. 397–402. DOI 10.1109/ASET.2018.8379889.
FERRANCOL FERRAN, Zhaira Marianne, TAMAYO ONGQUIT, Luisa Ann and ROMEROSO ARBOLEDA, Edwin. A review of machine learning applications in Cacao Post-harvest management. International Journal of Science and Research Archive. Online. 2024. Vol. 2024, no. 01, p. 1540–1550. [Accessed 19 August 2024]. DOI 10.30574/ijsra.2024.11.1.0186.
OMAS-AS, Andrey M., ARBOLEDA, Edwin R. and DAANG, Jewelle Abbygale M. Machine Learning as a Strategic Tool: A Comprehensive Literature Review for Advancing Agricultural Analysis, with Emphasis on the Cocoa Bean Quality Assessment. International Journal of Scientific Research and Engineering Development. Online. 2024. Vol. 7, no. 1. [Accessed 19 August 2024]. DOI 10.5281/zenodo.10552894.
JHA, Kirtan, DOSHI, Aalap, PATEL, Poojan and SHAH, Manan. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 1 June 2019. Vol. 2, p. 1–12. DOI 10.1016/J.AIIA.2019.05.004.
ZHININ-VERA, Luis, ZHIMINAICELA-CABRERA, Jonathan, PRETEL, Elena, SUÁREZ, Pamela, CHANG, Oscar, CASTRO, Francesc Antón and LÓPEZ DE LA ROSA, Francisco. Artificial Vision Technique to Detect and Classify Cocoa Beans. In : International Work-Conference on Artificial Neural Networks. Online. Cham: Springer Nature Switzerland, 2023. p. 217–228. [Accessed 19 August 2024]. DOI 10.1007/978-3-031-43078-7_18.
LECUN, Yann, BOTTOU, Léon, BENGIO, Yoshua and HAFFNER, Patrick. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 1998. Vol. 86, no. 11, p. 2278–2323. DOI 10.1109/5.726791.
GU, Jiuxiang, WANG, Zhenhua, KUEN, Jason, MA, Lianyang, SHAHROUDY, Amir, SHUAI, Bing, LIU, Ting, WANG, Xingxing, WANG, Gang, CAI, Jianfei and CHEN, Tsuhan. Recent advances in convolutional neural networks. Pattern Recognition. 1 May 2018. Vol. 77, p. 354–377. DOI 10.1016/J.PATCOG.2017.10.013.
HERNÁNDEZ PACHECO, Denis. Análisis de textura basado en la matriz de ocurrencia de niveles de gris. Online. 25 May 2008. [Accessed 26 December 2021]. Available from: http://dspace.uclv.edu.cu:8089/xmlui/handle/123456789/7625Facultad de Matemática, Física y Computación. Departamento de Ciencias de la Computación
INSTITUTO ECUATORIANO DE NORMALIZACIÓN. Cacao en grano. Requisitos. Quito, 2006.
HERNÁNDEZ-CALVARIO, Omar, FLORIÁN, Frida, SÁNCHEZ, María Guadalupe, ÁVILA-GEORGE, Himer, HERNÁNDEZ-CALVARIO, Omar, FLORIÁN, Frida, SÁNCHEZ, María Guadalupe and ÁVILA-GEORGE, Himer. Conteo de plantas de agave usando redes neuronales convolucionales e imágenes adquiridas desde un vehículo aéreo no tripulado. RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação. Online. 2022. Vol. 2022, no. 45, p. 64–76. [Accessed 20 April 2023]. DOI 10.17013/RISTI.45.64-76.
ALQUDAH, Ali Mohammad, ALQURAN, Hiam and QASMIEH, Isam Abu. Segmented and Non-Segmented Skin Lesions Classification Using Transfer Learning and Adaptive Moment Learning Rate Technique Using Pretrained Convolutional Neural Network. Journal of Biomimetics, Biomaterials and Biomedical Engineering. Online. 2019. Vol. 42, p. 67–78. [Accessed 27 February 2022]. DOI 10.4028/WWW.SCIENTIFIC.NET/JBBBE.42.67.
GAO, Feng, WANG, Wei, TAN, Miaomiao, ZHU, Lina, ZHANG, Yuchen, FESSLER, Evelyn, VERMEULEN, Louis and WANG, Xin. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis. Online. 16 August 2019. Vol. 8, no. 9, p. 1–12. [Accessed 20 February 2022]. DOI 10.1038/s41389-019-0157-8.
KIM, Phil. MATLAB Deep Learning. Springer. Online. 2017. [Accessed 20 February 2022]. DOI 10.1007/978-1-4842-2845-6.
BRESSEM, Keno K., ADAMS, Lisa C., GAUDIN, Robert A., TRÖLTZSCH, Daniel, HAMM, Bernd, MAKOWSKI, Marcus R., SCHÜLE, Chan Yong, VAHLDIEK, Janis L. and NIEHUES, Stefan M. Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports. Bioinformatics. Online. 29 January 2021. Vol. 36, no. 21, p. 5255–5261. [Accessed 20 February 2022]. DOI 10.1093/BIOINFORMATICS/BTAA668.
JOSHI, Ramchandra, GOEL, Purvi and JOSHI, Raviraj. Deep Learning for Hindi Text Classification: A Comparison. Springer. Online. 12 December 2019. Vol. 11886 LNCS, p. 94–101. [Accessed 20 February 2022]. DOI 10.1007/978-3-030-44689-5_9.
REYES, Julio, RAMIREZ, Diego and PACIELLO, Julio. Automatic classification of source code archives by programming language: A deep learning approach. International Conference on Computational Science and Computational Intelligence (CSCI). 17 March 2017. P. 514–519. DOI 10.1109/CSCI.2016.0103.
PAOLETTI, ME, HAUT, JM, PLAZA, J and PHOTOGRAMMETRY, A Plaza. Deep learning classifiers for hyperspectral imaging: A review. ISPRS Journal of Photogrammetry and Remote Sensing. Online. [Accessed 20 February 2022]. DOI https://doi.org/10.1016/j.isprsjprs.2019.09.006.
ZHAO, Minghao, HU, Chengquan, WEI, Fenglin, WANG, Kai, WANG, Chong and JIANG, Yu. Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network. Sensors. Online. 16 January 2019. Vol. 19, no. 2, p. 350. [Accessed 20 February 2022]. DOI 10.3390/S19020350.
CHOW, Jun Kang, SU, Zhaoyu, WU, Jimmy, LI, Zhaofeng, TAN, Pin Siang, LIU, Kuan fu, MAO, Xin and WANG, Yu Hsing. Artificial intelligence-empowered pipeline for image-based inspection of concrete structures. Automation in Construction. 1 December 2020. Vol. 120, p. 103372. DOI 10.1016/J.AUTCON.2020.103372.
SHIDDIEQY, Hasbi Ash, HARIADI, Farkhad Ihsan and ADIONO, Trio. Implementation of deep-learning based image classification on single board computer. International Symposium on Electronics and Smart Devices (ISESD). 1 July 2017. Vol. 2018-January, p. 133–137. DOI 10.1109/ISESD.2017.8253319.
LI, Jiahao, LI, Bin, XU, Jizheng, XIONG, Ruiqin and GAO, Wen. Fully Connected Network-Based Intra Prediction for Image Coding. IEEE Transactions on Image Processing. 1 July 2018. Vol. 27, no. 7, p. 3236–3247. DOI 10.1109/TIP.2018.2817044.
DOGO, E. M., AFOLABI, O. J., NWULU, N. I., TWALA, B. and AIGBAVBOA, C. O. A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks. Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018. 1 December 2018. P. 92–99. DOI 10.1109/CTEMS.2018.8769211.
VALLEJO, William, DÍAZ-URIBE, Carlos and FAJARDO, Catalina. Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students. ACS Omega. Online. 18 February 2022. P. acsomega.2c00362. [Accessed 25 February 2022]. DOI 10.1021/ACSOMEGA.2C00362.
RADIUK, Pavlo M. Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets. Information Technology and Management Science. 11 January 2018. Vol. 20, no. 1. DOI 10.1515/ITMS-2017-0003.
KESKAR, Nitish Shirish, NOCEDAL, Jorge, TANG, Ping Tak Peter, MUDIGERE, Dheevatsa and SMELYANSKIY, Mikhail. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. Online. 15 September 2016. [Accessed 27 February 2022]. Available from: https://arxiv.org/abs/1609.04836v2
LI, Wenmei, LIU, Haiyan, WANG, Yu, LI, Zhuangzhuang, JIA, Yan and GUI, Guan. Deep Learning-Based Classification Methods for Remote Sensing Images in Urban Built-Up Areas. IEEE Access. 2019. Vol. 7, p. 36274–36284. DOI 10.1109/ACCESS.2019.2903127.
PANWAR, Harsh, GUPTA, P. K., SIDDIQUI, Mohammad Khubeb, MORALES-MENENDEZ, Ruben and SINGH, Vaishnavi. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons & Fractals. 1 September 2020. Vol. 138, p. 109944. DOI 10.1016/J.CHAOS.2020.109944.
MAXWELL, Aaron E., WARNER, Timothy A. and GUILLÉN, Luis Andrés. Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review. Remote Sensing. 1 July 2021. Vol. 13, no. 13. DOI 10.3390/RS13132450.
TANER, Alper, BENAL ÖZTEKIN, sim, DURAN, Hüseyin, PAMPURO, Niccolò, BIDDOCCU, Marcella and MANUEL MIRÁS-AVALOS, José. Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut. Sustainability. Online. 8 June 2021. Vol. 13, no. 12, p. 6527. [Accessed 26 February 2022]. DOI 10.3390/SU13126527.
VINAYAHALINGAM, Shankeeth, KEMPERS, Steven, LIMON, Lorenzo, DEIBEL, Dionne, MAAL, Thomas, HANISCH, Marcel, BERGÉ, Stefaan and XI, Tong. Classification of caries in third molars on panoramic radiographs using deep learning. Scientific Reports. Online. 15 June 2021. Vol. 11, no. 1, p. 1–7. [Accessed 27 February 2022]. DOI 10.1038/s41598-021-92121-2.
CRYSTAL, Maria, OROZCO, E and REBONG, Corazon B. Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model. DOI 10.5013/IJSSST.a.20. S2.11.
DANDAVATE, Rucha and PATODKAR, Vineet. CNN and data augmentation-based fruit classification model. Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020. 7 October 2020. P. 784–787. DOI 10.1109/I-SMAC49090.2020.9243440.
LINGWAL, Surabhi, BHATIA, Komal Kumar and TOMER, Manjeet Singh. Image-based wheat grain classification using convolutional neural network. Multimedia Tools and Applications. Online. 1 November 2021. Vol. 80, no. 28–29, p. 35441–35465. [Accessed 21 September 2024]. DOI 10.1007/S11042-020-10174-3/METRICS.
HUANG, Nen Fu, CHOU, Dong Lin, LEE, Chia An, WU, Feng Ping, CHUANG, An Chi, CHEN, Yi Hsien and TSAI, Yin Chun. Smart agriculture: real-time classification of green coffee beans by using a convolutional neural network. IET Smart Cities. Online. 1 December 2020. Vol. 2, no. 4, p. 167–172. [Accessed 21 September 2024]. DOI 10.1049/IET-SMC.2020.0068.
Derechos de autor 2024 Ariosto Eugenio Vicuña Pino; Jorge Farouk Molina Noboa, Luis Miguel Espín Carrasco, Orlando Emiro Erazo Moreta
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