Enhanced faster region-based convolutional neural network for oil palm tree detection

Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for...

Full description

Saved in:

Bibliographic Details
Main Author:	Liu, Xinni
Format:	Thesis
Language:	English
Published:	2021
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/34675/1/Enhanced%20faster%20region-based%20convolutional%20neural%20network.ir.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-ump-ir.34675
record_format	uketd_dc
spelling	my-ump-ir.346752022-10-14T03:31:25Z Enhanced faster region-based convolutional neural network for oil palm tree detection 2021-05 Liu, Xinni T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for oil palm tree plantation is manually counting using GIS software, which is tedious and inefficient for large scale plantation. To overcome this problem, researchers proposed automatic counting methods based on machine learning and image processing. However, traditional machine learning and image processing methods used handcrafted feature extraction methods. It can only extract low-middle level features from the image and lack of generalization ability. It’s applicable only for one application and will need reprogramming for other applications. The widely used feature extraction methods are local binary patterns (LBP), scale-invariant feature transform (SIFT), and the histogram of oriented gradients (HOG), which usually achieve low accuracy because of their limited feature representation ability and without generalization capability. Hence, this research aims to close the research gaps by exploring the deep learning-based object detection algorithm and the classical convolutional neural network (CNN) to build an automatic deep learning-based oil palm tree detection and counting framework. This study proposed a new deep learning method based on Faster RCNN for oil palm tree detection and counting. To reduce the overfitting problem during the training, this study uses the image processing method to augment the training dataset by random flipping the image and to increase the data’s contrast and brightness. The transfer learning model of ResNet50 was used as the CNN backbone and the Faster RCNN network was retrained to get the weight for automatic oil palm tree counting. To improve the performance of Faster RCNN, feature concatation method was used to integrate the high-level and low-level feature from ResNet50. The proposed model validated the testing dataset of three palm tree regions with mature, young, and mixed mature and young palm trees. The detection results were compared with two machine learning methods of ANN, SVM, image processing-based TM method, and the original Faster RCNN model respectively. The proposed enhanced Faster RCNN model shows a promising result of oil palm tree detection and counting. It achieved an overall accuracy of 97% in the testing dataset, 97.2% in the mixed palm tree region, and 96.9% in the mature and young palm tree region, while the traditional ANN, SVM, and TM methods are less than 90%. The accuracy of comparison reveals that the proposed EFRCNN model outperforms the Faster RCNN and the traditional ANN, SVM, and TM methods. It has the potential to apply in counting a large area of oil palm tree plantation. 2021-05 Thesis http://umpir.ump.edu.my/id/eprint/34675/ http://umpir.ump.edu.my/id/eprint/34675/1/Enhanced%20faster%20region-based%20convolutional%20neural%20network.ir.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering Technology
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	T Technology (General) T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) T Technology (General) Liu, Xinni Enhanced faster region-based convolutional neural network for oil palm tree detection
description	Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for oil palm tree plantation is manually counting using GIS software, which is tedious and inefficient for large scale plantation. To overcome this problem, researchers proposed automatic counting methods based on machine learning and image processing. However, traditional machine learning and image processing methods used handcrafted feature extraction methods. It can only extract low-middle level features from the image and lack of generalization ability. It’s applicable only for one application and will need reprogramming for other applications. The widely used feature extraction methods are local binary patterns (LBP), scale-invariant feature transform (SIFT), and the histogram of oriented gradients (HOG), which usually achieve low accuracy because of their limited feature representation ability and without generalization capability. Hence, this research aims to close the research gaps by exploring the deep learning-based object detection algorithm and the classical convolutional neural network (CNN) to build an automatic deep learning-based oil palm tree detection and counting framework. This study proposed a new deep learning method based on Faster RCNN for oil palm tree detection and counting. To reduce the overfitting problem during the training, this study uses the image processing method to augment the training dataset by random flipping the image and to increase the data’s contrast and brightness. The transfer learning model of ResNet50 was used as the CNN backbone and the Faster RCNN network was retrained to get the weight for automatic oil palm tree counting. To improve the performance of Faster RCNN, feature concatation method was used to integrate the high-level and low-level feature from ResNet50. The proposed model validated the testing dataset of three palm tree regions with mature, young, and mixed mature and young palm trees. The detection results were compared with two machine learning methods of ANN, SVM, image processing-based TM method, and the original Faster RCNN model respectively. The proposed enhanced Faster RCNN model shows a promising result of oil palm tree detection and counting. It achieved an overall accuracy of 97% in the testing dataset, 97.2% in the mixed palm tree region, and 96.9% in the mature and young palm tree region, while the traditional ANN, SVM, and TM methods are less than 90%. The accuracy of comparison reveals that the proposed EFRCNN model outperforms the Faster RCNN and the traditional ANN, SVM, and TM methods. It has the potential to apply in counting a large area of oil palm tree plantation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Liu, Xinni
author_facet	Liu, Xinni
author_sort	Liu, Xinni
title	Enhanced faster region-based convolutional neural network for oil palm tree detection
title_short	Enhanced faster region-based convolutional neural network for oil palm tree detection
title_full	Enhanced faster region-based convolutional neural network for oil palm tree detection
title_fullStr	Enhanced faster region-based convolutional neural network for oil palm tree detection
title_full_unstemmed	Enhanced faster region-based convolutional neural network for oil palm tree detection
title_sort	enhanced faster region-based convolutional neural network for oil palm tree detection
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronics Engineering Technology
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/34675/1/Enhanced%20faster%20region-based%20convolutional%20neural%20network.ir.pdf
_version_	1783732203158503424

Enhanced faster region-based convolutional neural network for oil palm tree detection

Similar Items