›› 2017, Vol. 29 ›› Issue (8): 1375-1383.DOI: 10.3969/j.issn.1004-1524.2017.08.20

• Biosystems Engineering • Previous Articles     Next Articles

Tomato seed varieties recognition based on principal component analysis and LVQ neural network

ZHAO Xueguan1, 2, WANG Xiu1, 2, *, LI Cuiling1, 2, GAO Yuanyuan1, 2, 3, WANG Songlin1, 2, FENG Qingchun1, 2   

  1. 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China;
    2. National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, China;
    3.College of Electrical and Electronic Engineering, China Agricultural University, Beijing 100083, China
  • Received:2016-12-12 Online:2017-08-20 Published:2017-09-06

Abstract: In order to realize the real-time, accurate and no-damage mechanization identification of tomato seed varieties, according to the characteristics of tomato seeds and its image, the tomato varieties identification technology and algorithm were studied. This paper proposed a tomato seed varieties identification method, which is a kind of optimization by LVQ neural network based on principal components analysis, extracting the shape characteristics, texture feature and seven moment invariants of the tomato seeds. Four principal components as the input of artificial neural network were chosen through the principal components analysis. The identification test was conducted on five varieties of Heidi, Hongdi, Jiafen18, Jindi and Cupid. The number of competitive layer neurons and training trials were determined according to the test, which were 20 and 96. Under the condition, the average time of each seed identification was the shortest, and the recognition accuracy was the highest, which were 0.2 s and 90.5% respectively. The research showed that the method of identification and detection of tomato seed varieties based on machine vision is feasible.

Key words: tomato seed, variety recognition, computer vision, neural networks

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