Classifying Barako coffee leaf diseases using deep convolutional models

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Classifying Barako coffee leaf diseases using deep convolutional models
 
Creator Montalbo, Francis Jesmar Perez
Hernandez, Alexander Arsenio
 
Subject Deep learning; Convolutional neural networks; Classification; Leaf disease; Barako coffee
 
Description This work presents the application of recent Deep Convolutional Models (DCM) to classify Barako leaf diseases. Several selected DCMs performed image classification tasks using Transfer Learning and Fine-Tuning, together with data preprocessing and augmentation. The collected dataset used totals to 4,667. Each labeled into four different classes, which included Coffee Leaf Rust (CLR), Cercospora Leaf Spots (CLS), Sooty Molds (SM), and Healthy Leaves (HL). The DCMs were trained using the partial 4,023 images and validated with the remaining 644. The classification results of the trained models VGG16, Xception, and ResNetV2-152 attained overall accuracies of 97%, 95%, and 91%, respectively. By comparing in terms of True Positive Rate (TPR), we found that Xception has the highest number of correct classifications of CLR, VGG16 with SM, and CLS, while ResNetV2-152 with the lowest TPR for CLR. The evaluated results indicate that the use of Deep Convolutional Models with an adequate amount of data, proper fine-tuning, preprocessing, and transfer learning can yield efficient classifiers for identifying several Barako leaf diseases. This work primarily contributes to the growing field of deep learning, specifically for helping farmers improve their diagnostic process by providing a solution that can automatically classify Barako leaf diseases.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2020-07-12
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/495
10.26555/ijain.v6i2.495
 
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 2 (2020): July 2020; 197-209
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/495/ijain_v6i2_p197-209
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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