Oil palm tree detection and counting in aerial images based on faster R-CNN
Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil pal...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer, Singapore
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30346/ http://umpir.ump.edu.my/id/eprint/30346/1/Oil%20palm%20tree%20detection%20and%20counting%20in%20aerial%20images%20%20.pdf |
| Summary: | Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection. |
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