Open Access

ARTICLE

A Hybrid Model of Transfer Learning and Convolutional Neural Networks for Accurate Coffee Leaf Miner (CLM) Classification

Nameer Baht^1,*, Enrique Domínguez^1,2,*

1 Department of Computer Science, University of Malaga, Malaga, 29071, Spain
2ITIS Software, Universidad de Málaga, Malaga, 29071, Spain

* Corresponding Authors: Nameer Baht. Email: ; Enrique Domínguez. Email:

(This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)

Computers, Materials & Continua 2025, 85(3), 4441-4455. https://doi.org/10.32604/cmc.2025.069528

Received 25 June 2025; Accepted 26 August 2025; Issue published 23 October 2025

Abstract

Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected. Leveraging the JMuBEN1 dataset, the proposed hybrid VGG19 model achieved exceptional performance, reaching 97% accuracy on both training and validation data. Additionally, high scores for precision, recall, and F1-score. The confusion matrix shows that all the test samples were correctly classified, which indicates the model’s strong performance on this dataset, demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves. This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management, and adapting it for practical deployment in agricultural settings. As well as supporting farmers in detecting diseases using modern, inexpensive methods that do not require specialists, and utilising deep learning technologies.

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