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.

SPP2021 Proceedings Cover

Downloads

Published

2021-10-05

Issue

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

Section

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

License

By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.

How to Cite

[1]
“Deep learning approach for Cassava Phytoplasma Disease image classification”, Proc. SPP, vol. 39, no. 1, p. SPP-2021-PB-17, Oct. 2021, Accessed: Mar. 27, 2026. [Online]. Available: https://proceedings.spp-online.org/article/view/SPP-2021-PB-17