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An yield estimation in citrus orchards via fruit detection and counting using image processing
Dorj, Ulzii-Orshikh,Lee, Malrey,Yun, Sang-seok Elsevier 2017 Computers and electronics in agriculture Vol.140 No.-
<P><B>Abstract</B></P> <P>The overall goal of this study is to develop an effective, simple, aptly computer vision algorithm to detect and count citrus on the tree using image processing techniques, to estimate the yield, and to compare the yield estimation results obtained through several methods. This new citrus recognition and counting algorithm was utilized the color features (or schemes) to present an estimate of the citrus yield, and the corresponding models are developed to provide an early estimation of the citrus yield. Citrus images were taken from Jeju, South Korea during daylight and the citrus recognition and counting algorithm were tested on 84 images which were collected from 21 trees. The citrus counting algorithm consisted of the following steps: convert RGB image to HSV, thresholding, orange color detection, noise removal, watershed segmentation, and counting. Distance transform and marker-controlled watershed algorithms were evaluated for automated watershed segmentation in citrus fruits to obtain good result. A correlation coefficient R<SUP>2</SUP> of 0.93 was obtained between the citrus counting algorithm and counting performed through human observation. The proposed algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The overall goal of this study is to develop an effective, simple, aptly computer vision algorithm to detect and count citrus on the tree using image processing techniques, to estimate the yield, and to compare the yield estimation results obtained through several methods. </LI> <LI> This new citrus recognition and counting algorithm was utilized the color features (or schemes) to present an estimate of the citrus yield, and the corresponding models are developed to provide an early estimation of the citrus yield. </LI> <LI> Citrus images were taken from Jeju, South Korea during daylight and the citrus recognition and counting algorithm were tested on 84 images, which were collected from 21 trees. </LI> <LI> The citrus counting algorithm consisted of the following steps: convert RGB image to HSV, thresholding, orange color detection, noise removal, watershed segmentation, and counting. </LI> <LI> Distance transform and marker-controlled watershed algorithms were evaluated for automated watershed segmentation in citrus fruits to obtain good result. </LI> <LI> A correlation coefficient R<SUP>2</SUP> of 0.93 was obtained between the citrus counting algorithm and counting performed through human observation. </LI> <LI> The proposed algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops. </LI> </UL> </P>
The Intelligent Healthcare Data Management System Using Nanosensors
Dorj, Ulzii-Orshikh,Lee, Malrey,Choi, Jae-young,Lee, Young-Keun,Jeong, Gisung Hindawi Limited 2017 Journal of sensors Vol.2017 No.-
<P>We developed a design of Intelligent Healthcare Data Management System using nanosensors (IHDMS) and composed an application for mobile device. The proposed IHDMS will coordinate the healthcare data of the patients from nanosensors and transforms it into a worldwide consumed standard HL7 (Health Level Seven) for conversion of healthcare data. This converted data dispatches to a server of its system. The battery lifetime of the facility is feasible to increase, the memory usage is less than 100 KB, and it operates all data by employing few and far between resources. Moreover, the proposed system decreases the waiting time in the transposing data, and secured channel was used for the server of the healthcare center in the running HL7 format data.</P>