UAV-based deep learning with Tiny-YOLOv9 for revolutionizing paddy rice disease detection
The agricultural sector serves as a cornerstone in the socioeconomic landscape of nations worldwide, with paddy rice standing as a vital staple crop in many regions. However, the proliferation of common paddy leaf diseases presents significant challenges to the global quality and quantity of ric...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/121439/1/121439.pdf http://psasir.upm.edu.my/id/eprint/121439/ https://ieeexplore.ieee.org/document/10788368/ |
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| Summary: | The agricultural sector serves as a cornerstone in
the socioeconomic landscape of nations worldwide, with paddy
rice standing as a vital staple crop in many regions. However, the
proliferation of common paddy leaf diseases presents significant
challenges to the global quality and quantity of rice crop
yields. Early detection of these diseases is imperative to mitigate
their impact on crop production. Leveraging future-tech UAVs
(Unmanned Aerial Vehicles) network for remote sensing coupled
with Deep Learning (DL) holds promise in addressing this
issue. This paper introduces Tiny-YOLOv9, a novel lightweight
architecture derived from YOLOv9, explicitly tailored for realtime
leaf disease detection across various plant species. Tiny-
YOLOv9 integrates cutting-edge components such as the 3D Feature
Adaptation Module (3D-FAM), DeepWise Point Convolution
(DWC), Coordinate Attention Module (CAM), and Convolutional
Block Attention Modules (CBAM) to enhance feature extraction
precision and attention. The proposed methodology exhibits
superior performance and detection capabilities compared to
the state-of-the-art (SOTA), as evidenced by metrics such as
Average Precision (AP), Average recall (AR), F1-Score, and mean
Average Precision (mAP). The minimal resource utilization and
enhanced detection accuracy make the proposed Tiny-YOLOv9
a better alternative for UAV (Unmanned Arial Vehicles) onboard
intelligence for paddy agronomy. |
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