A Deep Learning-Based System for Plant Disease Detection and Classification in Arabica Coffee Leaves

Poolakanda Somanna, Harshitha (2023) A Deep Learning-Based System for Plant Disease Detection and Classification in Arabica Coffee Leaves. Masters thesis, Dublin, National College of Ireland.

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Abstract

Coffee leaf disease is a growing concern to coffee agroforestry, predominantly caused by pathogenic fungi and, to a lesser extent, bacteria and viruses reducing the yield and adversely affecting the quality of the coffee. Detecting and controlling these diseases in their early stages represent formidable challenges, since traditional methods rely on visual observation by experts and often fail in accurate diagnosis. Machine learning (ML) techniques are alternative solutions for automating the classification of plant diseases and with the rapid advancements in deep learning methods, there is a potential to identify and recognize coffee leaf diseases at early stages, thereby supporting efforts to enhance crop yield. However, there is a notable gap in research, particularly regarding the detection of coffee leaf diseases on a larger dataset. This study employs deep learning models and transfer learning approaches, including EfficientNetB0, MobileNetV2, CNN and VGG16, to address multi-class labeling complexities in Arabica coffee leaf disease detection. Utilizing the "JMuBEN" dataset with 58,405 images across five classes, Phoma, Cercospora, Leaf Rust, Miner and one set of healthy leaf images, the research aims to comprehensively assess each model's efficacy. The test accuracy of the models ranged from 32.49% to 99.7% and the best performing model, EfficientNetB0 outperformed all the models in this study giving a test accuracy of 99.72% and an overall F1 score, Recall and Precision of 99.7%. Beyond Arabica, the findings may extend to Robusta and have broader applications in crop disease detection.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Muntean, Christina Hava UNSPECIFIED
Uncontrolled Keywords:	Arabica Coffee; EfficientNetB0; MobileNetV2; CNN; VGG16
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SB Plant culture H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry > Beverage industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 May 2025 14:29
Last Modified:	20 May 2025 14:29
URI:	https://norma.ncirl.ie/id/eprint/7591

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