Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

Damar Novtahaning, Hasnain Ali Shah and Jae-Mo Kang ()
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Damar Novtahaning: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea
Hasnain Ali Shah: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea
Jae-Mo Kang: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea

Agriculture, 2022, vol. 12, issue 11, 1-16

Abstract: Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

Keywords: ensemble learning; coffee leaf disease; deep learning; image classification; transfer learning; fine-tuned CNN (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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