Rice Leaf Diseases Detection Using YOLOv8
Main Article Content
Abstract
The development of rice plants holds immense importance today as it impacts crucial aspects of life such as food security, agricultural advancement, and the economy of nations. Consequently, research on disease detection in rice plants, particularly using machine learning, is gaining popularity. Several diseases pose a threat to rice leaves, with Blast leaf, leaf folder, and brown spot being the most common ones, directly affecting crop cultivation and causing yield loss. In this study, we propose the utilization of deep learning, the state-of-the-art image processing solution, to address this issue. Our proposed method consists of two steps: first, collecting reliable dataset by approaching and capturing direct images of rice leaf diseases in the fields, and second, designing and training an Artificial Intelligence (AI) model using the YOLOv8 algorithm to detect and classify the three aforementioned diseases. The data set used in this study includes 3175 images, divided into three parts, of which the training part is 2608 images, the validation part is 326 images and the test part is 241 images. Our experimental results demonstrate an accuracy up to 88.9% for the proposed model.
Keywords
Blast leaf, leaf folder, brown spot, YOLOv8, deep learning
Article Details
References
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based on the convolution neural network algorithm,
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295-301, 2021.
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M. R. A. Mamun, F. M. Ruhad, A. Parven,
N. M. Mubarak, S. L. K., I. Md. Meftaul, Tea leaf
disease detection and identification based on YOLOv7
(YOLO-T), Nature, Scientific Report, 6078, 2023
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detection in bell pepper plant using YOLO v5,
springer, Signal, Image and Video Processing, vol. 16,
pp. 841 – 847, 2021.
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detection in apple leaves using deep convolutional
neural networks: apple leaves disease detection using
EfficientNet and DenseNet, MDPI, Agriculture, Vol.
11, issue 7, 617, 2021.
https://doi.org/10.3390/agriculture11070617
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B. Subedi, H. B. Abraha, V. E. Sathishkum, Rice leaf
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transfer learning, Environ Res. ,198:111275, 2021.
https://doi.org/10.1016/j.envres.2021.111275
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M. Wang, Y. Liu, Predicting rice diseases using
advanced technologies at different scales: present
status and future perspectives, Springer, aBIOTECH,
vol. 4, pp.359 – 371, 2023.
https://doi.org/10.1007/s42994-023-00126-4
[7] M. E. Haque, A. Rahman, I. Junaeid, S. U. Hoque,
M. Paul, Rice leaf disease classification and detection
using YOLOv5, arXiv:2209.01579v1,2022.
https://doi.org/10.48550/arXiv.2209.01579
[8] J. J. Muhammad, A. S. Riaz, A. S. Noor, R. Samina,
H. A. Rafaqat, H. C. Ghulam, Q. B. Abdul,
S. Hidayatullah, H. S. Kashif, Deep learning-based rice
leaf diseases detection using Yolov5, Sukkur IBA
Journal of Computing and Mathematical Science –
SJCMS, vol. 6, no. 1, pp. 49-61, 2022.
https://doi.org/10.30537/sjcms.v6i1.1009
[9] G. Jocher, A. Chaurasia, J. Qiu.YOLOv8. Ultralytics.
Feb.3,2024.
[10] G. Jocher and S. Waxmann. YOLOv7: Trainable bagof-freebies. Ultralytics. July. 1,2024.
[11] Performance benchmark of YOLOv5, YOLOv7 and
v8. Jan. 12,2023.
[12] Y. Lee, J.W. Hwang, S. Lee, Y. Bae, An energy and
GPU-computation efficient backbone network for realtime object detection, arXiv:1904.09730v1, 2019.
https://doi.org/10.1109/CVPRW.2019.00103
[13] C. Feng, Y. Zhong, Y. Gao, M.R. Scott, W. Huang,
TOOD: Task-aligned one-stage object detection,
arXiv:2108.07755v3, 2021.
https://doi.org/10.1109/ICCV48922.2021.00349
[14] Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu,
W. Zuo, Enhancing geometric factors in model
learning and inference for object detection and
instance Segmentation, IEEE Transactions on
Cybernetics, vol. 52, no. 8, pp. 8574-8586, Aug. 2022.
https://doi.org/10.1109/TCYB.2021.3095305
[15] X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang,
J. Yang, Generalized Focal Loss: Learning qualified
and distributed bounding boxes for dense object
detection, arXiv:2006.04388v1, 2020.
https://doi.org/10.1109/CVPR46437.2021.01146
[16] H. Ershadul, P. Manoranjan, R. Ashikur, T. Faranak,
Md. Islam, Rice leaf disease detection and
classification using lightly trained YOLOv7 active
deep learning approach, Digital Image Computing:
Techniques and Applications (DICTA), 2023.