Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*
1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
2. Collaborative Innovation Center of Advanced Manufacturing of Machinery and Equipment of Henan Province, Luoyang 471003, China
3. College of Agriculture/Tree Peong, Henan University of Science and Technology, Luoyang 471023, China
4. College of Physical Engineering, Henan University of Science and Technology, Luoyang 471023, China
Abstract：Maize is one of the important food source, which is widely planted in China. The selection of excellent maize varieties is the key to agricultural production and breeding. However, there are wide varieties of maize on the market at present. In this paper, the extreme learning machine (ELM) model of maize varieties identification based on hyperspectral image technology was proposed to solve the problem of maize varieties identification. In this study, eight varieties of maize seeds were regarded as research objects, and 480 experiment samples were divided into training sets and test sets in a 2∶1 ratio, with 320 and 160 samples respectively. The images of maize seeds in the 935.61~1 720.23 nm were obtained by a hyperspectral acquisition system. Regions of interest (ROI) of 10×10 pixels in germ were selected after correction, and the average spectrum in the region was extracted as the original spectral data. Due to the large noise at both ends and less effective information of the original spectrum, in order to enhance the signal-to-noise ratio, spectral bands of maize seeds in the range of 949~1 700 nm were selected as effective bands for analysis. Due to the strong interference of irrelevant information during data collection, the spectral bands information after denoising was processed by Savitzky-Golay smoothing. The smoothing point was set to 3. Maximum normalization (MN) was used to pretreat based on SG smoothing. After pretreatment, feature wavelength variables were extracted by competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and CARS+SPA, CARS-SPA. The wavelength reflectance was used as the input matrix X, and the sample varieties 1, 2, 3, 4, 5, 6, 7, 8 were used as the output matrix Y. (SG+MN)-ELM, (SG+MN)-CARS-ELM, (SG+MN)-SPA-ELM, (SG+MN)-(CARS+SPA)-ELM, (SG+MN)-(CARS-SPA)-ELM were established. The experiment results showed that (SG+MN)-(CARS-SPA)-ELM model had the best identification performance compared with others, and the average identification accuracy of training sets and test sets was 98.13%, indicating that CARS-SPA secondary screening feature wavelength variables were more sensitive, which could represent all wavelengths information. The ELM model had better qualitative identification performance. It could realize the identification of maize varieties. This study provides a new idea and method for rapid and accurate identification of maize and other crop seeds.
[1] YUE Hai-wang, WEI Jian-wei, XIE Jun-liang, et al(岳海旺, 魏建伟, 谢俊良, 等). Journal of China Agricultural University(中国农业大学学报), 2022, 27(4): 31.
[2] LI Hui, WU Jing-zhu, LIU Cui-ling, et al(李 慧, 吴静珠, 刘翠玲, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2019, 34(2): 125.
[3] CHENG Xue, HE Bing-yan, HUANG Yao-huan, et al(程 雪, 贺炳彦, 黄耀欢, 等). Remote Sensing Technology and Application, 2019, 34(4): 775.
[4] WU Yong-qing, LI Ming, ZHANG Bo, et al(吴永清, 李 明, 张 波, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2021, 36(5): 165.
[5] ZHANG Hang, YAO Chuan-an, JIANG Meng-meng, et al(张 航, 姚传安, 蒋梦梦, 等). Journal of Triticeae Crops(麦类作物学报), 2019, 39(1): 96.
[6] Huang M, He C J, Zhu Q B, et al. Applied Sciences, 2016, 6(6): 183.
[7] Xia C, Yang S, Huang M, et al. Infrared Physics & Technology, 2019, 103: 103077.
[8] Chivasa W, Mutanga O, Biradar C. Journal of Applied Remote Sensing, 2019, 13(1): 017504.
[9] Zhou Q, Huang W Q, Tian X, et al. Journal of the Science of Food and Agriculture, 2021, 101(11): 4532.
[10] Sun H, Zhang L, Li H, et al. Journal of Food Process Engineering, 2021, 44(8): 13769.
[11] Singh T, Garg N M, Iyengar S R S. Journal of Food Process Engineering, 2021, 44(10): 13821.
[12] SHAO Qi, CHEN Yun-hao, YANG Shu-ting, et al(邵 琦, 陈云浩, 杨淑婷, 等). Geography and Geo-Information Science(地理与地理信息科学), 2019, 35(5)：34.
[13] WU Xiang, ZHANG Wei-zheng, LU Jiang-feng, et al(吴 翔, 张卫正, 陆江锋, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(2): 511.
[14] HUANG Min, ZHU Xiao, ZHU Qi-bing, et al(黄 敏, 朱 晓, 朱启兵, 等). Journal of Data Acquisition and Processing(数据采集与处理), 2013, 28(3): 289.
[15] HUANG Min, XIA Chao, ZHU Qi-bing, et al(黄 敏, 夏 超, 朱启兵, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(18): 153.
[16] WU Yong-qing, LI Ming, HE Yuan-yuan, et al(吴永清, 李 明, 贺媛媛, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2021, 36(4): 133.
[17] DENG Xiao-qin, ZHU Qi-bing, HUANG Min(邓小琴, 朱启兵, 黄 敏). Laser & Optoelectronics Progress(激光与光电子学进展), 2015, 52(2): 021001.
[18] Wu J, Mohamed D, Dowhanik S, et al. Plant Cell, 2020, 32(6): 1886.
