Abstract：Seed purity reflects the degree of seed varieties in typical consistent characteristics, so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds. Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time, which has been widely used in nondestructive detection of agricultural products. The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products. Since the spectral information is easily affected by the sample growth environment, the stability and generalization of model would weaken when the test samples harvested from different origin and year. Active learning algorithm was investigated to add representative samples to expand the sample space for the original model, so as to implement the rapid update of the model’s ability. Random selection (RS) and Kennard- Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm. The experimental results indicated that in the division of different proportion of sample set (1∶1, 3∶1, 4∶1), the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%, 33.75%, 49% to 98.89%, 98.33%, 98.33%. For the updated purity detection model of 2011 year, its prediction accuracy for 2010 new samples increased by 50.83%，54.58%，53.75% to 94.57%，94.02%，94.57% after adding 56 new samples from 2010 year. Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS. Therefore, the update for purity detection model of maize seeds is feasible by active learning algorithm.
[1] YI Xun, Qinghua Yang, Guanjun Bao, et al. Image and Signal Processing, 2009. CISP’09. 2nd International Congress on, Tianjin, China, 2009: 1. [2] ZHU Qi-bing, FENG Zhao-li, HUANG Min, et al(朱启兵, 冯朝丽, 黄 敏, 等). Transactions of the Chinese Society for Agricultural Engineering(农业工程学报), 2012，28(23): 271. [3] XUE Long, LI Jing, LIU Mu-hua(薛 龙, 黎 静, 刘木华). Acta Photonica Sinica(光子学报), 2008, 28(12): 2277. [4] LI Dan, HE Jian-guo, LIU Gui-shan, et al(李 丹, 何建国, 刘贵珊, 等). Infrared and Laser Engineering(红外与激光工程), 2014, 143(7): 2393. [5] LIU Yan-de, YING Yi-bin, CHENG Fang(刘燕德, 应义斌, 成 芳). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2003，34(5): 161. [6] SU Dong-lin, LI Gao-yang, HE Jian-xin, et al(苏东林, 李高阳, 何建新, 等). Science and Technology of Food Industry(食品工业科技), 2012, 33(6): 460. [7] WEN Dong-dong, LI Xiao-yu, ZHAO Zheng, et al(文东东, 李小昱, 赵 政, 等). Journal of Food Safety and Quality(食品安全质量检测学报), 2012, 3(6): 621. [8] XIE Li-juan, YING Yi-bin(谢丽娟，应义斌). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2012, 33(5): 538. [9] Vapnik V N. Statistical Learning Theory. John Wiley and Sons, New York, USA, 1998. [10] Tuia D, Volpi M, Copa L, et al. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 606. [11] Macho S, Callao M P, Larrechi M S, et al. Analytica Chimica Acta, 2001, 445(2): 213.
