Young and mature oil palm tree detection and counting using convolutional neural network deep learning method

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

Download Full-text

Automatic Detection of Oil Palm Tree from UAV Images Based on the Deep Learning Method

Applied Artificial Intelligence ◽

10.1080/08839514.2020.1831226 ◽

2020 ◽

Vol 35 (1) ◽

pp. 13-24

Author(s):

Xinni Liu ◽

Kamarul Hawari Ghazali ◽

Fengrong Han ◽

Izzeldin Ibrahim Mohamed

Keyword(s):

Deep Learning ◽

Oil Palm ◽

Automatic Detection ◽

Palm Tree ◽

Learning Method ◽

Uav Images

Download Full-text

Deep Learning- Based Surveillance System using Face Recognition

ITM Web of Conferences ◽

10.1051/itmconf/20203203011 ◽

2020 ◽

Vol 32 ◽

pp. 03011

Author(s):

Divya Kapil ◽

Aishwarya Kamtam ◽

Akhil Kedare ◽

Smita Bharne

Keyword(s):

Neural Network ◽

Image Processing ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Network ◽

Surveillance System ◽

Surveillance Systems ◽

Learning Method ◽

Security Systems ◽

The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

Download Full-text

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

IJIREEICE ◽

10.17148/ijireeice.2021.91103 ◽

2021 ◽

Vol 9 (11) ◽

Author(s):

K. Johnathan ◽

T.S.Y. Moh

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Oil Palm ◽

Unmanned Aircraft ◽

Stem Rot ◽

Palm Tree ◽

Unmanned Aircraft System ◽

Large Area ◽

Basal Stem Rot ◽

Aircraft System

Download Full-text

Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2019.1.2.20 ◽

2019 ◽

Vol 1 (2) ◽

pp. 6-9

Author(s):

Chee Cheong Lee ◽

See Yee Tan ◽

Tien Sze Lim ◽

Voon Chet Koo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Template Matching ◽

Window Size ◽

Ground Truth ◽

Frequency Domain Analysis ◽

Image Resolution ◽

Palm Tree ◽

Tree Detection ◽

High Image Resolution

We propose a method to combine several image processing methods with Convolutional Neural Network (CNN) to perform palm tree detection and counting. This paper focuses on drone imaging, which has a high image resolution and is widely deployed in the plantation industry. Analyzing drone images is challenging due to variable drone flying altitudes, resulting in inconsistent tree sizes in images captured. Counting by template matching or fixed sliding window size method often produces an inaccurate count. Instead, our method employs frequency domain analysis to estimate tree size before CNN. The method is evaluated using two images, ranging from a few thousand trees to a few hundred thousand trees per image. We have summarized the accuracy of the proposed method by comparing the results with manually labelled ground truth.

Download Full-text

Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2019.1.2.2 ◽

2019 ◽

Vol 1 (2) ◽

pp. 6-9

Author(s):

Chee Cheong Lee ◽

See Yee Tan ◽

Tien Sze Lim ◽

Voon Chet Koo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Template Matching ◽

Window Size ◽

Ground Truth ◽

Frequency Domain Analysis ◽

Image Resolution ◽

Palm Tree ◽

Tree Detection ◽

High Image Resolution

We propose a method to combine several image processing methods with Convolutional Neural Network (CNN) to perform palm tree detection and counting. This paper focuses on drone imaging, which has a high image resolution and is widely deployed in the plantation industry. Analyzing drone images is challenging due to variable drone flying altitudes, resulting in inconsistent tree sizes in images captured. Counting by template matching or fixed sliding window size method often produces an inaccurate count. Instead, our method employs frequency domain analysis to estimate tree size before CNN. The method is evaluated using two images, ranging from a few thousand trees to a few hundred thousand trees per image. We have summarized the accuracy of the proposed method by comparing the results with manually labelled ground truth.

Download Full-text

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218778324 ◽

2018 ◽

Vol 41 (5) ◽

pp. 1383-1394 ◽

Cited By ~ 8

Author(s):

Xuan Yao ◽

Zhaobo Chen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sliding Mode Control ◽

Convolutional Neural Network ◽

Sliding Mode ◽

Magnetic Bearing ◽

Trajectory Control ◽

Active Magnetic Bearing ◽

Learning Method ◽

Mode Control

Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

Download Full-text