Improved Plant Image Segmentation Method using Vegetation Indices and Automatic Thresholds
(주)코리아스칼라
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- 2016.04.02
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- 2015.10
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서지정보
ㆍ발행기관 : 경상대학교 농업생명과학연구원
ㆍ수록지정보 : 농업생명과학연구 / 49권 / 5호
ㆍ저자명 : Seong-Heon Kim, Chan-Seok Ryu, Ye-Seong Kang, Young-Bong Min
영어 초록
We address improved plant image segmentation based on histograms which requires using a vegetation index and threshold. Image segmentation is the most important step for extracting targets, such as vegetation, from images; this affects successful detection of plant information. Forty-two field images were acquired from a soybean field using an RGB camera. Through K-means clustering analysis, we built a new vegetation index and generated gray-scale images. Otsu and Triangle thresholds were used to convert contrast images to binary. Optimal threshold values were generally located between the Otsu and Triangle threshold values. The combined threshold method shows 98.79% and 0.95% of mean accuracy and standard deviation, respectively, whereas the Otsu and Triangle method results show 98.17±1.71% and 97.85±1.87%, respectively. These results show that the combined method has significant segmentation potential through one-way ANOVA. Then we compared the results with K-means clustering using two-sample t-test. The K-means method’s mean accuracy is 98.18±1.79%, with no significant difference between the proposed and K-means methods. However, the proposed method’s processing time is 0.60±0.01 s, i.e., twice faster than the K-means method (1.72±0.24 s).
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