LS-SVM Recognition of Fruit Using in Harvesting Robot Based on RIO-HOG Feature

Authors

Wang Zhi-qiang, College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China\\ College of Informational and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaFollow
Li Li-jun, College of Informational and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaFollow

Author Country (or Countries)

China

Abstract

To solve the problems occurred in the recognition of oil-tea camellia, such as the big error, lack of universality and much time consuming, the paper propose a new algorithm for fruit recognition, where Region Of Interest (RIO), Histogram of Oriented Gradients (HOG) temperature and Least Square Support Vector Machine (LS-SVM) are applied. First, the images are detected from HSV (hue, saturation, value) color information. The HOG temperature, calculated using four regions of interest (ROI), is input to an LS-SVM classifier, which detects the fruit. The performance of the model was verified by experiments. The vector sizes were effectively reduced and a higher detection speed was achieved without compromising accuracy (relative to conventional approaches). The detection accuracy can respectively achieve 95.5%, 89.4% and 96.7% for isolated fruit, overlapped fruit and background, which is shown the excellent performance of the proposed algorithm.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/110218

Recommended Citation

Zhi-qiang, Wang and Li-jun, Li (2017) "LS-SVM Recognition of Fruit Using in Harvesting Robot Based on RIO-HOG Feature," Applied Mathematics & Information Sciences: Vol. 11: Iss. 2, Article 18.
DOI: http://dx.doi.org/10.18576/amis/110218
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss2/18