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Lookup NU author(s): Dr Duy NguyenORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf.
Author(s): Pham TC, Nguyen VD, Le CH, Packianather M, Hoang VD
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Conference on Marine Sustainable Development and Innovation 2023 (MSDI 2023)
Year of Conference: 2023
Pages: 012004
Online publication date: 12/12/2023
Acceptance date: 22/11/2023
Date deposited: 12/02/2024
Publisher: Institute of Physics Publishing Ltd.
URL: https://doi.org/10.1088/1755-1315/1278/1/012004
DOI: 10.1088/1755-1315/1278/1/012004
Library holdings: Search Newcastle University Library for this item
Series Title: IOP Conference Series: Earth and Environmental Science
ISBN: 17551315
