Hyperspectral Non-Destructive Detection of Heat-Damaged Maize Seeds
ZHANG Fu1, 2, YU Huang1, XIONG Ying3, ZHANG Fang-yuan1, WANG Xin-yue1, LÜ Qing-feng4, WU Yi-ge4, ZHANG Ya-kun1, FU San-ling5*
1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
2. Collaborative Innovation Center of Advanced Manufacturing for Machinery and Equipment of Henan Province, Luoyang 471003, China
3. College of Agriculture/Peon, Henan University of Science and Technology, Luoyang 471023, China
4. Henan Pingan Seed Industry Limited Company, Jiaozuo 454881, China
5. School of Physical Engineering, Henan University of Science and Technology, Luoyang 471023, China
Abstract：Maize is one of the three major food crops in the world, and the use of substandard seeds that do not meet the national standards will seriously affect the yield of maize crops, so how to identify substandard maize seeds quickly, accurately and efficiently is particularly important. The hyperspectral image system to obtain the 900~1 700 nm spectral curves of 900 “Yu'an 3” corn seeds, in which the training set and test set ratio was 3∶2, 540 and 360 seeds respectively. The seeds were treated with an electric blast dryer to obtain corn seed samples with different degrees of damage, and the germination test was completed after collecting the spectra to determine the viability of the seeds. In order to improve the signal-to-noise ratio, the spectral bands of maize seeds in the range of 963.27~1 698.75 nm were intercepted as the effective bands. Standard Normal Variation (SNV) and Multiplicative Scatter Correction (MSC), were used to pre-process the raw spectral data. The Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract feature bands from the pre-processed spectral data, with wavelength reflectance as input matrix X and preset sample categories as output matrix Y. The Support Vector Machine (SVM) was used to model and analyze the data, and the results showed that the MSC-CARS-SVM model was the best model, with a model recognition success rate of 98.33% and a Kappa coefficient of 0.985. Genetic Algorithm (GA) was used to optimize the penalty coefficient c and kernel function parameter g in the SVM, and the model accuracy was improved to 100% for the identification of heat-damaged counterfeit and poor-quality maize seeds. This study provides a new idea and method for rapidly identifying the pseudo-inferior quality of maize seeds and seeds of other crops.
[1] LI Sheng-jun(李圣军). Journal of Heilongjiang Grain(黑龙江粮食), 2020，(4): 18.
[2] WANG Xin-yan(王新燕). Seed Quality Detection Technology(种子质量检测技术). Beijing: China Agricultural University Press(北京：中国农业大学出版社), 2008.
[3] KONG Guang-chao, CAO Lian-pu(孔广超, 曹连莆). Seed(种子), 2000，(3): 26.
[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] 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.
[6] CHENG Xue, HE Bing-yan, HUANG Yao-huan, et al(程 雪, 贺炳彦, 黄耀欢, 等). Remote Sensing Technology and Application(遥感技术与应用), 2019, 34(4): 775.
[7] Chao X, Sai Y, Min H, et al. Infrared Physics & Technology, 2019, 103: 103077.
[8] Zhou Q, Huang W, Tian X, et al. Journal of the Science of Food and Agriculture, 2021, 101(11): 4532.
[9] Walter C, Onisimo M, Chandrashekhar B. Journal of Applied Remote Sensing, 2019, 13(1): 017504.
[10] YANG Huan, LUO Bin, ZHANG Han, et al(杨 欢, 罗 斌, 张 晗, 等). Journal of Jiangsu University（Natural Science Edition[江苏大学学报(自然科学版)], 2023, 44(2): 159.
[11] TIAN Xi, HUANG Wen-qian, LI Jiang-bo, et al(田 喜, 黄文倩, 李江波, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(10): 3237.
[12] WU Jing-zhu, ZHANG Le, LI Jiang-bo, et al(吴静珠, 张 乐, 李江波, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2022, 53(5): 302.
[13] Ashabahebwa A, Lalit M K, Moon S K, et al. Infrared Physics & Technology, 2016, 75: 173.
[14] Cui H W, Cheng Z S, Li P, et al. Sensors, 2020, 20(17): 4744.
[15] Feng L, Zhu S, Zhang C, et al. Molecules, 2018, 23(12): 3078.
[16] SUN Jun, ZHANG Lin, ZHOU Xin, et al(孙 俊, 张 林, 周 鑫, 等). Transactions of the Chinese Society for Agricultural Engineering(农业工程学报), 2021, 37(14): 171.
[17] PENG Yan-kun, ZHAO Fang, BAI Jing, et al(彭彦昆, 赵 芳, 白 京, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(2): 327.
[18] Zhang L, Rao Z H, Ji H Y. Spectroscopy Letters, 2020, 53(3): 207.
