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Classifying Rice Bacterial Panicle Blight by Combining YOLOv5 Model and Convolutional Neural Network

Authors:

Khang Quoc Nguyen,

Anh Quynh Nguyen,

Hoang Ngoc Tran,

Luyl-Da QuachAuthors Info & Claims

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

Pages 172 - 176

https://doi.org/10.1145/3591569.3591600

Published: 13 July 2023 Publication History

Abstract

Rice Bacterial Panicle Blight is a highly destructive and rapidly spreading disease on rice, reducing rice yield. Therefore, this study has identified the diseased rice variety on the plant, from which it is possible to assess the extent of damage caused by the disease on rice. Because of conducting to collect the deceased rice in the field, healthy rice was mixed with diseased rice. Therefore, the study was carried out using the YOLOv5 model to detect all rice seeds in images. Then, this research performed the separation of healthy and diseased rice seeds into 2 data sets. Including 417 photos of healthy rice seeds and 314 photos of diseased rice seeds. Using the CNN model for classification, the accuracy on the train is almost 100%, and 98.68% on the test set.

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Cited By

Nguyen ATran MNguyen QHuynh HLe LQuach L(2024)Classification of Rice Plant Disease Based on Descriptive Information with DistilBERT's ArchitectureProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654568(155-163)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654568
Quach LQuoc KQuynh ANgoc HThai-Nghe N(2024)Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++IEEE Access10.1109/ACCESS.2024.335180512(9719-9737)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351805

Index Terms

Classifying Rice Bacterial Panicle Blight by Combining YOLOv5 Model and Convolutional Neural Network
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Models of learning

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cover image ACM Other conferences

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

February 2023

310 pages

ISBN:9781450399616

DOI:10.1145/3591569

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2023

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ICIIT 2023

ICIIT 2023: 2023 8th International Conference on Intelligent Information Technology

February 24 - 26, 2023

Da Nang, Vietnam

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Cited By

Nguyen ATran MNguyen QHuynh HLe LQuach L(2024)Classification of Rice Plant Disease Based on Descriptive Information with DistilBERT's ArchitectureProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654568(155-163)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654568
Quach LQuoc KQuynh ANgoc HThai-Nghe N(2024)Tomato Health Monitoring System: Tomato Classification, Detection, and Counting System Based on YOLOv8 Model With Explainable MobileNet Models Using Grad-CAM++IEEE Access10.1109/ACCESS.2024.335180512(9719-9737)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351805

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