International Journal of Advances in Intelligent Informatics ISSN 2442-6571 Vol. 6, No. 2, July 2020, pp. 197-209 197 https://doi.org/10.26555/ijain.v6i2.495 http://ijain.org ijain@uad.ac.id Classifying barako coffee leaf diseases using deep convolutional models Francis Jesmar Perez Montalbo a,b,1,* , Alexander Arsenio Hernandez a,2 a Technological Institute of the Philippines, 363 Pascual Casal St, Quiapo, Manila, Metro Manila, 1001, Philippines b Batangas State University, Rizal Ave, Extension, Batangas City, 4200, Philippine 1 mfjpmontalbo@tip.edu.ph; 2 alexander.hernandez@tip.edu.ph * corresponding author 1. Introduction The coffee industry made immense global contributions to society, placing as the second most traded commodity next to crude oil worldwide. With an estimated amount of 15 billion trees planted, it supports the demand of 25 million producers around several countries [1]. In most Asian territories, the coffee industry paves employment for several families to cope up with their daily needs. In the Philippines, the Coffea Liberica is a popular coffee variant referred to as Barako. The sought-after product possesses a distinct flavor and aroma that interest most consumers. Unlike other varieties, the Barako tree is difficult to grow as it consumes a larger land area, making it a less encouraging option for farmers. Also, Barako cultivation is greatly affected by widespread diseases. According to the Philippines coffee industry roadmap, in 2015, Liberica only yielded 257 metric tons (MT) of coffee beans, contributing only 1% to the whole coffee production. At the same time, Robusta produced an average of 24,924 MT, providing 69%, followed by Arabica with 8717 MT at 24%, and Excelsa with 2273 MT at 6%. Since the rust invasion of 1896, Barako became less enticing to grow due to farmers opting for other alternatives. Considering that Excelsa being less vulnerable against drought and most infections [2][3]. Even today, it remains a challenge for experts to provide an immediate diagnosis for plant diseases. The lengthy procedure frequently turns to a massive spread of infection that causes excessive losses [4]. ARTICLE INFO ABSTRACT Article history Received April 9, 2020 Revised May 29, 2020 Accepted June 8, 2020 Available online July 12, 2020 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. This is an open access article under the CC–BY-SA license. Keywords Deep learning Convolutional neural networks Classification Leaf disease Barako coffee