Hybrid Seed Recognition of Maize Based on Probability Clustering Model Using Visible Light Color Features
LIU Shuang-xi1, ZHANG Hong-jian1, WANG Jin-xing2*, WANG Zhen1, ZHANG Chun-qing3, LI Yan3
1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
2. Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai’an 271018, China
3. College of Agronomy, Shandong Agricultural University, Tai’an 271018, China
Abstract：Because the grain size of the same kind of hybrid maize seed is different and the maize color changes as the storage time varies, it is difficult to identify the species only by the machine vision method with its shape and color in a single region. Besides, the existing recognition algorithm mostly uses the hyper-spectral feature as the basis for classification, so for different periods, different types of hybrid maize seeds need to be trained by the classification equipment, and a lot of training is required before the identification. In order to improve the applicability of identification method for maize seed variety, a multi-model probabilistic clustering method was established based on the multi-regional wavelet color characteristics of maize seed in the visible light band as the recognition parameter. This method used a special equipment to extract the non-germinal and the topside color information of the single-grain maize seed, including the color information of RGB, HIS and Lab. Then the color information was enhanced, the feature selection was optimized and the 21-dimensional detail recognition vector was perfected by wavelet packet decomposition. Secondly, the clustering recognition of the optimized color feature was carried out by different clustering models. Three clustering models based on SOM, K-means and two-step method were thus established. Finally, based on the results of multiple clustering models, the maize seed variety identification via probability model was set up. Through the experiments on Zheng Dan 958, Xian Yu 335, Zheng 58 (Zheng Dan 958 female), Chang 7-2 (Zheng Dan 958 male), PH6WC (Xian Yu 335 female), PH4CV (Xian Yu 335 male), it was shown that the method was able to effectively identify maize seeds with non-genetic relationship and parental relationship, with the recognition rate reaching over 98%; While the recognition rate of female parent was 75%. This can provide scientific basis for on-line identification of hybrid seed purity. The method of probability clustering model can provide scientific basis for the identification of maize hybrid seed purity by using visible light multi-regional color characteristics.
Key words：Visible light; Hybrid seed of maize; Recognition; Clustering; Probability; Multi-region color
刘双喜，张宏建，王金星，王 震，张春庆，李 岩. 基于可见光波段的色彩概率聚类模型的玉米杂交种子识别[J]. 光谱学与光谱分析, 2018, 38(08): 2516-2523.
LIU Shuang-xi, ZHANG Hong-jian, WANG Jin-xing, WANG Zhen, ZHANG Chun-qing, LI Yan. Hybrid Seed Recognition of Maize Based on Probability Clustering Model Using Visible Light Color Features. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2516-2523.
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