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JACIII Vol.27 No.3 pp. 333-339
doi: 10.20965/jaciii.2023.p0333
(2023)

Research Paper:

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Non-Destructive Classification of Paddy Rice Leaf Disease Infected by Bacterial and Fungal Species Using Vision-Based Deep Learning

Amir A. Bracino*,† ORCID Icon, Danielle Grace D. Evangelista** ORCID Icon, Ronnie S. Concepcion II* ORCID Icon, Elmer P. Dadios* ORCID Icon, and Ryan Rhay P. Vicerra* ORCID Icon

*Department of Manufacturing Engineering and Management Department, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

†Corresponding author

**Department of Chemical Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Received:
April 22, 2022
Accepted:
July 10, 2022
Published:
May 20, 2023
Creative Commons License
Keywords:
deep learning, EfficientNet-b0, MobileNet-v2, paddy rice leaf diseases, Places365-GoogLeNet
Abstract

Rice is a plant with rounded hollow articulated culms, flat, well-attached leaf blades, and terminal spikes. Its cultivation and consumption shape the culture, diet, and economy of different groups, especially in Asia. However, farmers suffer great financial losses each year due to rice disease. Therefore, the identification and classification of rice diseases are very important. Prompt, timely, and accurate disease diagnosis prevents product loss and improves crop quality. This study focuses on the classification of whether rice paddy leaf is normal or has a disease (one of the following: bacterial leaf blight (BLB), bacterial leaf streaks (BLS), bacterial panicle blight (BPB): heart, downy mildew, hispa, and rice tungro disease (RTD)) using deep learning-based algorithms such as EfficientNet-b0, MobileNet-v2, and Places365-GoogLeNet. The best model for this simulation was found to be EfficientNet-b0 with an average accuracy of 97.74%.

Cite this article as:
A. Bracino, D. Evangelista, R. Concepcion II, E. Dadios, and R. Vicerra, “Non-Destructive Classification of Paddy Rice Leaf Disease Infected by Bacterial and Fungal Species Using Vision-Based Deep Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 333-339, 2023.
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Last updated on Apr. 22, 2024