Citation

Tatini, Narendra Babu and Lu, Guan Yu and Tan, Tan Hsu and Alkhaleefah, Mohammad and Koo, Voon Chet and Chan, Yee Kit and Chang, Yang Lang (2022) Yolov4 Based Rice Fields Classification from High-Resolution Images Taken by Drones. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July 2022, Kuala Lumpur, Malaysia.

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Abstract

In recent years, artificial intelligence (AI) technology has been used in computer vision to extract and analyze specific information from images. This research aims to apply AI in the field of agriculture. In this study, the you only look once version4 (YOLOv4) based on scaled-up feature fusion (YOLOv4-SUFF) has been implemented for rice fields detection from high-resolution images taken by an unmanned aerial vehicle (UAV). YOLOv4-SUFF consists of an extra layer which can extract special feature maps for the detector. This makes the proposed model YOLOv4-SUFF get more information during the stage of feature fusion. The experimental results show that the YOLOv4-SUFF has provided the best performance in terms of an average precision at 86.78% compared with other models.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep Learning, Object detection, Computer Vision, YOLOv4
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	10 Jan 2023 01:09
Last Modified:	10 Jan 2023 01:09
URII:	http://shdl.mmu.edu.my/id/eprint/10870

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