Detection of dry and wet cocoa beans to improve quality using Convolutional Neural Network-based You Only Look Once Architecture
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Keywords
Cocoa bean drying, Convolutional Neural Network (CNN), You Only Look Once (YOLO)
Abstract
Cocoa is one of Indonesia’s leading export commodities crucial in supporting the national economy. However, a significant challenge in the post-harvest processing of cocoa lies in the drying stage, where uneven drying often leads to inconsistent bean quality. While previous studies have predominantly focused on classifying cocoa beans based on surface defects such as faded, non-faded, fragmented, moldy, and damaged beans, limited research has addressed classification based on moisture levels—specifically distinguishing between dry and wet beans, which is essential for ensuring optimal fermentation, proper storage, and overall product quality. This study presents a classification model based on a Convolutional Neural Network (CNN) employing the You Only Look Once (YOLO) architecture to detect and classify dry and wet cocoa beans by analyzing visual features, particularly color and shape. A dataset of 2,880 labeled images was compiled and used to train and evaluate the model. The proposed system demonstrated strong performance, achieving an accuracy of 99.8%, a precision of 99.15%, and a recall of 99.8%. These results indicate that the model can be a reliable and efficient tool for detecting moisture content in cocoa beans, thereby enhancing quality control, reducing human subjectivity in the inspection process, and supporting automation in the cocoa processing industry to ensure product consistency and added value in the export market.
References
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[20] A. Santoso and G. Ariyanto, “Implementasi Deep Learning berbasis Keras untuk Pengenalan Wajah,” Emitor Jurnal Teknik Elektro, vol. 18, no. 1, pp. 15–21, 2018, doi: 10.23917/emitor.v18i01.6235.
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[24] A. Febriana, K. Muchtar, R. Dawood, and C. Y. Lin, “USK-COFFEE Dataset: A Multi-Class Green Arabica Coffee Bean Dataset for Deep Learning,” Proceedings -2022 IEEE International Conference Cybernetics and Computational Intelligence CybernetucsCom 2022, pp. 469–473, 2022, doi: 10.1109/CyberneticsCom55287.2022.9865489
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[27] D. Reis, J. Kupec, J. Hong, and A. Daoudi, “Real-Time Flying Object Detection with YOLOv8,” 2023, [Online]. Available: https://arxiv.org/abs/2305.09972
[28] J. Terven and D. Cordova-Esparza, “A Comprehensive Review of YOLO: From YOLOv1 and Beyond,” pp. 1–33, 2023.
[29] Y. Li et al., “SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images,” Remote Sensing, vol. 16, no. 16, Aug. 2024, doi: 10.3390/rs16163057.
[30] N. León-Roque, M. Abderrahim, L. Nuñez-Alejos, S. M. Arribas, and L. Condezo-Hoyos, “Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks,” Talanta, vol. 161, pp. 31–39, 2016, doi: 10.1016/j.talanta.2016.08.022.
[31] C. D. Anggraini, A. W. Putranto, Z. Iqbal, H. Firmanto, and D. F. Al Riza, “Preliminary study on development of cocoa beans fermentation level measurement based on computer vision and artificial intelligence,” IOP Conference Series Earth Environmental Science, vol. 924, no. 1, 2021, doi: 10.1088/1755-1315/924/1/012019.
[32] O. Eric, R. M. O. M. Gyening, O. Appiah, K. Takyi, P. Appiahene, "Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model, Engineering Applications of Artificial Intelligence," Engineering Applications of Artificial Intelligent, Vol 125, 2023, 106736, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2023.106736.
[33] Y. Adhitya, S. W. Prakosa, M. Köppen, and J. S. Leu, “Feature extraction for cocoa bean digital image classification prediction for smart farming application,” Agronomy, vol. 10, no. 11, 2020, doi: 10.3390/agronomy10111642.
[2] J. Langkong, N. Abdullah, M. Mahendradatta, M. Tahir M., and M. Reski, “Pemanfaatan Kulit Biji Kakao (Theobroma Cacao L) Menjadi Produk Cookies Coklat,” Canrea Journal, vol. 2, no. 1, pp. 26–31, 2019.
[3] F. D. Banboye, M. N. Ngwabie, S. A. Eneighe, and D. B. Nde, “Assessment of greenhouse technologies on the drying behavior of cocoa beans,” no. February, pp. 2748–2757, 2020, doi: 10.1002/fsn3.1565
[4] E. Teye, E. Anyidoho, R. Agbemafle, L. K. Sam-, and C. Elliott, “Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A Review,” Infrared Physics Technology, p. 103127, 2019, doi: 10.1016/j.infrared.2019.103127.
[5] B. F. Dzelagha, N. M. Ngwa, and D. N. Bup, “Review Article A Review of Cocoa Drying Technologies and the Effect on Bean Quality Parameters,” International Journal of Food Science, vol. 2020, 2020, doi: 10.1155/2020/8830127.
[6] M. A. Zulkarnain, M. K. Shahriman, and A. S. M. Yudin,“Experimental study of drying characteristics of cocoa bean in a swirling fluidized bed dryer,” IOP Conference Series Materials Science Engineering, vol. 863, no. 1, 2020, doi:10.1088/1757-899X/863/1/012048.
[7] M. M. Oliveira, B. V Cerqueira, S. Barbon, and D. F. Barbin, “Journal of Food Composition and Analysis Classification of fermented cocoa beans ( cut test ) using computer vision,” Journal Food Composition and Analysis, vol. 97, no. November 2020, p. 103771, 2021, doi: 10.1016/j.jfca.2020.103771.
[8] A. K. Jean, M. Diarra, B. A. Bakary, G. Pierre, and A. K. Jérôme, “Application based on Hybrid CNN-SVM and PCA-SVM Approaches for Classification of Cocoa Beans,” International Journal Advanced Computer Science Applications, vol. 13, no. 9, pp. 231–238, 2022,45 doi: 10.14569/IJACSA.2022.0130927.
[9] F. Gao et al., “Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN,” Computer Electronics in Agriculture, vol. 176, no. May, p. 105634, 2020, doi: 10.1016/j.compag.2020.105634.
[10] S. Wan and S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision system,” Computer Networks, vol. 168, p. 107036, 2020, doi: 10.1016/j.comnet.2019.107036
[11] J. Naranjo-Torres, M. Mora, R. Hernández-García, R. J. Barrientos, C. Fredes, and A. Valenzuela, “A review of convolutional neural network applied to fruit image processing,” Applications Science, vol. 10, no. 10, 2020, doi: 10.3390/app10103443.
[12] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
[13] R. Khan and R. Debnath, “Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques,” International Journal Image, Graphics Signal Process., vol. 11, no. 8, pp. 1–18, 2019, doi: 10.5815/ijigsp.2019.08.01.
[14] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” 2018, [Online]. Available: https://arxiv.org/abs/1804.02767
[15] R. Dwiyanto, D. W. Widodo, and P. Kasih, “Implementasi Metode You Only Look Once ( YOLOv5 ) Untuk Klasifikasi Kendaraan Pada CCTV Kabupaten Tulungagung,” pp. 102–104, 2022.
[16] A. B. Sudjono, M. Munadi, and D. J. Setiawan, “Aplikasi Model Object Detection Pada Ayam Boiler Berbasis Deep Learning Pada Kandang Closed House,” vol. 11, no. 2, pp. 83–88, 2023.
[17] D. F. A. Karim, D. P. M. Ludong, and L. C. C. E. Lengkey, “Uji Kinerja Alat Pengering Biji Kakao (Theobroma cacao L.) Tipe Bak DI PT. PP. London Sumatera Indonesia Tbk. Pungkol Estate,” Cocos, vol. 14, no. 1, 2021.
[18] A. Budi, T. Sari, H. Firmanto, and T. Wahyudi, “Small-Scale Fermentation of Cocoa Beans and the Parameter for on-Process Monitoring,” vol. 37, no. 1, pp. 76–84, 2021, doi: 10.22302/iccri.jur.pelitaperkebunan.v37i1.444.
[19] T. Diwan, G. Anirudh, and J. V Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimedia Tools Applications, vol 82, pp. 9243-9275, 2022.
[20] A. Santoso and G. Ariyanto, “Implementasi Deep Learning berbasis Keras untuk Pengenalan Wajah,” Emitor Jurnal Teknik Elektro, vol. 18, no. 1, pp. 15–21, 2018, doi: 10.23917/emitor.v18i01.6235.
[21] M. Nielsen, Neural Networks and Deep Learning, 2021, doi: 10.1108/978-1-83909-694-520211010.
[22] J. Gupta, S. Pathak, and G. Kumar, “Deep Learning (CNN) and Transfer Learning: A Review,” Journal Physics Conference Series, vol. 2273, no. 1, 2022, doi: 10.1088/1742-6596/2273/1/012029.
[23] N. Hanum Harani, C. Prianto, and M. Hasanah, “Deteksi Objek Dan Pengenalan Karakter Plat Nomor Kendaraan Indonesia Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Python,” Jurnal Teknik Informatika, vol. 11, no. 3, pp. 47–53, 2019
[24] A. Febriana, K. Muchtar, R. Dawood, and C. Y. Lin, “USK-COFFEE Dataset: A Multi-Class Green Arabica Coffee Bean Dataset for Deep Learning,” Proceedings -2022 IEEE International Conference Cybernetics and Computational Intelligence CybernetucsCom 2022, pp. 469–473, 2022, doi: 10.1109/CyberneticsCom55287.2022.9865489
[25] W. Tan, C. Zhao, and H. Wu, “Intelligent alerting for fruit-melon lesion image based on momentum deep learning,” Multimedia Tools Applications, vol. 75, no. 24, pp. 16741–16761, 2016, doi: 10.1007/s11042-015-2940-7.
[26] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning and deep learning,” Pattern Recognition Letters, Vol. 141, pp. 61–67, 2020. https://doi.org/10.1016/j.patrec.2020.07.042
[27] D. Reis, J. Kupec, J. Hong, and A. Daoudi, “Real-Time Flying Object Detection with YOLOv8,” 2023, [Online]. Available: https://arxiv.org/abs/2305.09972
[28] J. Terven and D. Cordova-Esparza, “A Comprehensive Review of YOLO: From YOLOv1 and Beyond,” pp. 1–33, 2023.
[29] Y. Li et al., “SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images,” Remote Sensing, vol. 16, no. 16, Aug. 2024, doi: 10.3390/rs16163057.
[30] N. León-Roque, M. Abderrahim, L. Nuñez-Alejos, S. M. Arribas, and L. Condezo-Hoyos, “Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks,” Talanta, vol. 161, pp. 31–39, 2016, doi: 10.1016/j.talanta.2016.08.022.
[31] C. D. Anggraini, A. W. Putranto, Z. Iqbal, H. Firmanto, and D. F. Al Riza, “Preliminary study on development of cocoa beans fermentation level measurement based on computer vision and artificial intelligence,” IOP Conference Series Earth Environmental Science, vol. 924, no. 1, 2021, doi: 10.1088/1755-1315/924/1/012019.
[32] O. Eric, R. M. O. M. Gyening, O. Appiah, K. Takyi, P. Appiahene, "Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model, Engineering Applications of Artificial Intelligence," Engineering Applications of Artificial Intelligent, Vol 125, 2023, 106736, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2023.106736.
[33] Y. Adhitya, S. W. Prakosa, M. Köppen, and J. S. Leu, “Feature extraction for cocoa bean digital image classification prediction for smart farming application,” Agronomy, vol. 10, no. 11, 2020, doi: 10.3390/agronomy10111642.
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Published
Mar 29, 2025
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How to Cite
Muhammad Khalis Fikri, Adriman, R., Roslidar Roslidar, & Syahrial. (2025). Detection of dry and wet cocoa beans to improve quality using Convolutional Neural Network-based You Only Look Once Architecture. Jurnal Nasional Teknik Elektro, 14(1), 24–31. https://doi.org/10.25077/jnte.v14n1.1178.2025
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