IJBTT

Deep Learning For Coffee Disease Classification
	© 2021 by IJCTT Journal
	Volume-69 Issue-9
	Year of Publication : 2021
	Authors : Mikias Legesse
	DOI :  10.14445/22312803/IJCTT-V69I9P105

How to Cite?

Mikias Legesse, "Deep Learning For Coffee Disease Classification," International Journal of Computer Trends and Technology, vol. 69, no. 9, pp. 27-32, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I9P105

Abstract
Coffee production in Ethiopia has a long story and is also one of the major agricultural products that exported a huge amount. But coffee production faces different challenges from that widespread coffee diseases result in huge production loss in both quantity and quality. Even if different coffee diseases are still a major challenge, detection and classification of those diseases are performed only by experts, or there is not much technological advancement in this sector. This research aims to apply deep learning methods to overcome the existing problem. A deep learning model was fine-tuned and optimized to detect and classify different coffee diseases to assist farmers in applying appropriate treatment; for this research, 5600 images were captured and used for training and testing. And the best-performing model achieves 97% of accuracy. Proper tuning of hyperparameters can prevent overfitting and an appropriate choice of optimizers, resulting in an efficient classifier finally, as future research work indicated applying the model for real-life scenarios.

Keywords
Deep learning, CNN, Plant disease, CBD, CWD, CLR.

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