Recognition and feature extraction of kiwifruit in natural environment based on machine vision

被引：0
作者：
机构：
[1] College of Mechanical and Electronic Engineering, Northwest A and F University, Yangling
来源：
Cui, Y. (Cuiyongjie@nwsuaf.edu.cn) | 1600年 / Chinese Society of Agricultural Machinery卷 / 44期
关键词：
Feature extraction; Image recognition; Kiwifruit; Machine vision;
D O I：
10.6041/j.issn.1000-1298.2013.05.043
中图分类号：
学科分类号：
摘要：
A method for fruit recognition and feature extraction based on the color and shape features of kiwifruit in nature was studied. It could reduce the influences of complicated background, different kiwi growth state and natural lighting condition. First, R-G color component was chosen by comparing different color spaces. Then the optimum partition coefficient of nR-G color characteristics was determined according to the image evaluation method of error segmentation pixel, and 0.9R-G was selected finally. The Otsu method was used for threshold segmentation and morphological operation was employed to remove residual noise, and then the regions of target fruits and backgrounds were successfully separated. The image boundary was extracted by Canny operator and consequent elliptic Hough transform, which made the target fruit be recognized separately. Also, fruit features as centroid coordinates, long axis end coordinates, long axis length and short axis length were extracted. By using this method, 49 images including 110 fruits were tested. Test results demonstrated that the recognition ratio of separate fruit, adjacency fruit, partial sheltering fruit and overlapped fruit were 96.9%, 92.0%, 86.6% and 81.6%, respectively.
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页码：247 / 252
页数：5
相关论文
共 15 条
  • [1] Yuan T., Zhang J., Li W., Et al., Feature acquisition of cucumber fruit in unstructured environment using machine vision, Transactions of the Chinese Society for Agricultural Machinery, 40, 8, (2009)
  • [2] Henten E.J., Tuijl B.J., Hoogakker G.J., Et al., An autonomous robot for de-leafing cucumber plants grown in a high wire cultivation system, Biosystems Engineering, 94, 3, pp. 317-323, (2006)
  • [3] Arman A., Asad M.M., Kaveh M., Et al., Recognition and localization of ripen tomato based on machine vision, AJCS, 5, 10, pp. 1144-1149, (2011)
  • [4] Yin J., Mao H., Wang X., Et al., Automatic segmentation method for multi-tomato images under various growth conditions, Transactions of the CSAE, 22, 10, pp. 149-153, (2006)
  • [5] Xiang R., Ying Y., Jiang H., Et al., Recognition of verlapping tomatoes based on edge curvature analysis, Transactions of the Chinese Society for Agricultural Machinery, 43, 3, pp. 180-183, (2012)
  • [6] Stajnko D., Lakotaa M., Hoevar M., Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging, Computers and Electronics in Agriculture, 42, 1, pp. 31-42, (2004)
  • [7] Wachs J.P., Stern H.I., Burks T., Et al., Low and high-level visual feature-based apple detection from multi-modal images, Precision Agriculture, 11, 10, pp. 717-735, (2010)
  • [8] Rakuna J., Stajnkoa D., Zazulab D., Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry, Computers and Electronics in Agriculture, 76, 1, pp. 80-88, (2011)
  • [9] Zhang Y., Li M., Liu G., Et al., Separating adjoined apples based on machine vision and information fusion, Transactions of the Chinese Society for Agricultural Machinery, 40, 11, pp. 180-183, (2009)
  • [10] Si Y., Qiao J., Liu G., Et al., Recognition and shape features extraction of apples based on machine vision, Transactions of the Chinese Society for Agricultural Machinery, 40, 8, pp. 161-165, (2009)
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