Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9

Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9

Rice is pivotal in global food security and Rice seeding detection in precision agriculture is essential for optimizing crop productivity and efficient resource use. Currently, the spatial distribution and detection of rice seeding are manually done, which is time-consuming. As technology advances,...

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Bibliographic Details
Main Authors: Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning
precision agriculture
remote sensing
rice seedling detection
spatial attention
you only look once (YOLO)
Online Access:https://ieeexplore.ieee.org/document/10767286/
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