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Enhancing Coffee Leaf Disease Classification via Active Learning and Diverse Sample Selection

Lookup NU author(s): Dr Duy NguyenORCiD

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Abstract

Accurate classification of coffee leaf diseases is essential for early intervention and improved crop yield. However, training deep learning models for this task often requires large labeled datasets, which can be expensive and time-consuming to obtain. To address this challenge, we propose an iterative active learning framework with diverse expansion sampling for coffee leaf disease classification, aimed at the need to reduce annotation costs while maintaining high classification accuracy in agricultural disease detection. Our method iteratively selects the most informative and representative samples for labeling, reducing annotation costs while maintaining high classification performance. Diverse expansion sampling leverages a per-class diversity-based selection method inspired by Greedy Furthest-Point Selection, to effectively capture the data distribution. Experimental results demonstrate that our approach …

Publication metadata

Author(s): Pham TC, Nguyen TN, Hoang VD, Nguyen VD

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2025

Volume: 13

Pages: 161410-161422

Online publication date: 12/09/2025

Acceptance date: 03/09/2025

Date deposited: 29/04/2026

ISSN (electronic): 2169-3536

Publisher: IEEE

URL: https://doi.org/10.1109/ACCESS.2025.3609540

DOI: 10.1109/ACCESS.2025.3609540

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