Deep learning approach for Cassava Phytoplasma Disease image classification

Authors

Aleth Julianne O. Caringal Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Marisol P. Martinez Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Sarah Jane B. Manaday National Crop Protection Center, University of the Philippines Los Baños
Malec Renz Jeremiah B. Martirez National Crop Protection Center, University of the Philippines Los Baños
Gideon Aries S. Burgonio National Crop Protection Center, University of the Philippines Los Baños
Melvin D. Ebuenga National Crop Protection Center, University of the Philippines Los Baños
Jason R. Albia Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
Ranzivelle Marianne L. Roxas-Villanueva Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños

Abstract

Crop disease identification through image analysis is proven to be more cost effective and less time consuming than traditional approaches. In this study, deep learning is employed to predict the occurrence of Cassava Phytoplasma Disease (CPD). A pre-trained Convolutional Neural Network (CNN) architecture, Inception-ResNet-V2, was utilized on a cassava image dataset through transfer learning. The model has 83.78% accuracy, 80.95% precision, 89.47% recall, and 0.836 area under the curve (AUC). This rapid disease identification would be beneficial to farmers as they can isolate CPD-infected crops at a much earlier time.

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Issue

2021: Proceedings of the 39th Samahang Pisika ng Pilipinas Physics Conference

Article ID

SPP-2021-PB-17

Section

Poster Session B (Complex Systems, Photonics, and Interdisciplinary Topics)

Published

2021-10-05

How to Cite

[1]

AJO Caringal, MP Martinez, SJB Manaday, MRJB Martirez, GAS Burgonio, MD Ebuenga, JR Albia, and RML Roxas-Villanueva, Deep learning approach for Cassava Phytoplasma Disease image classification, Proceedings of the Samahang Pisika ng Pilipinas 39, SPP-2021-PB-17 (2021). URL: https://proceedings.spp-online.org/article/view/SPP-2021-PB-17.