Main Article Content


The application of Convolutional Neural Networks and Deep Learning Techniques in the detection of "Cocoa Swollen Shoot" disease in Ghanaian cocoa trees have demonstrated its effectiveness and reliability. This approach provides a valuable tool for cocoa farmers and agricultural authorities to promptly identify and manage the disease, contributing to the sustainable production of cocoa and the preservation of Ghana's cocoa industry. Recent advances in diagnostics have made image analysis one of the main areas of research and development. Selecting and calculating these characteristics of a disease is a difficult task. Among deep learning techniques, deep convolutional neural networks are actively used for image analysis. This includes areas of application such as segmentation, anomaly detection, disease classification, and computer-aided diagnosis. The objective, which we aim for in this article, is to extract information in an effective way for a better diagnosis of the plants attending the disease of “swollen shoot”.


Drone Convolutional Neural Networks Image Recognition Feature Detection Deep learning Ghana

Article Details

How to Cite
Atianashie, M. (2023). Detection of “Cocoa Swollen Shoot Disease” in Ghanaian Cocoa Trees Based on Convolutional Neural Network (CNN) and Deep Learning Technique. Journal of Engineering Applied Science and Humanities, 8(3), 179–188.


  1. CNRA (2019) Bulletin d’information et de liaison du Centre National de Recherche Agronomique N° 18 Août 2006.
  2. Deng, L., Yu, D., et al. (2019) Deep Learning: Methods and Applications, Founda- tions and Trends R in Signal Processing, 7, 197-387.
  3. Premaladha, J. and Ravichandran, K. (2016) Novel Approaches for Diagnosing Me- lanoma Skin Lesions through Supervised and Deep Learning Algorithms. Journal of Medical Systems, 40, 96.
  4. Kharazmi, P., Zheng, J., Lui, H., Wang, Z.J. and Lee, T.K. (2018) A Comput- er-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images via Deep Feature Learning. Journal of Medical Systems, 42, 33.
  5. Wang, S.-H., Phillips, P., Sui, Y., Liu, B., Yang, M. and Cheng, H. (2018) Classifica- tion of Alzheimers Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling. Journal of Medical Systems, 42, 85.
  6. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86, 2278-2324.
  7. Delalleau, O. (2012) Apprentissage machine efficace: Théorie et pratique. Thèse de doctorat, Université de Montreal, Montreal.
  8. Labelle, J. (1981) Theorie des graphes. Modulo, Montréal.
  9. Zeiler, M. and Fergus, R. (2020) Visualizing and Understanding Convolutional Networks. arXiv:1311.2901
  10. LeCun, Y. (1988) A Theoretical Framework for Back-Propagation. In: Touretzky, D., Hinton, G. and Sejnowski, T., Eds., Proceedings of the 1988 Connectionist Models Summer School, CMU, Pittsburg, PA.
  11. Copeland, M. (2006) what is the Difference between Artificial Intelligence, Machine Learning, and Deep Learning? The Canadian Press.
  12. Lerman, L. (2023) Les systemes de detection d’intrusion bases sur du machine learning.
  13. Casanova, V. S., & Atianashie Miracle, A. (2021). Communal Fixed Point in Fuzzy 2-Metric Spaces.
  14. Atianashie Miracle, A., Armah, E. D., & Mohammed, N. (2021). A portable gui based sleep disorder system classification based on convolution neural networks (cnn) in raspberry pi. J. Eng. Appl Sci. Humanities, 6, 13-23.
  15. Eneji, S. E., Ibe, W. E., Angib, M. U., & Atianashie Miracle, A. (2020). The Significance Role of Programming in Information Technology (IT).
  16. Adaobi, C. C. (2020) Biological, Psychological, Sociological, and Mythical Accounts of The Origin Of Ethics.
  17. Atianashie Miracle, A., Armah, E. D. A., Mohammed, N., & Sackey-Sam, S. (202) The Antithetical Effect of Cybercrime on Small Medium Enterprise SMES: Public Assessment.