Water Imaging of Living Corn Leaves Based on Near-Infrared Hysperspectral Imaging
YANG Yu-qing1, 2, ZHANG Tian-tian1,2, LI Jun-hui1, 2*, LU Meng-yao1, 2, LIU Hui1, 2, ZHAO Long-lian1, 2, ZHANG Ye-hui1, 2
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
Abstract：Non-destructive detection of plant leaves water content is of significance to plant physiological, biochemical research, irrigation management and drought monitoring. In this paper, Gaia Sorter Near-Infrared Spectrometer (900~1 700 nm) was used to detect the water content of 60 fresh corn leaves in different growth stages using PLS and SMLR models. The results demonstrated that the R2/SEP of validation set were 0.975/1.18, 0.980/1.02, all achieving better predictive results, which could bring out the determination of a single corn leaf average water content. The results of the SMLR model established with the preferred characteristic wavelength (1 406 nm, 1 692 nm) indicated that the use of high-throughput near-infrared camera combining filter method achieved the feasibility of corn leaves canopy or high-altitude remote sensing measurement. Simultaneously, the imaging analysis of the water content in different regions of the leaves was carried out, and the results revealed that the correlation coefficients between the measured mean values and the predictive mean values of the mesophyll and the main vein of the six leaves were 0.85, and the predictive results were in accordance with the actual situation.
Key words：Corn leaves; Near-infrared hysperspectral; Water imaging
杨玉清，张甜甜，李军会，鲁梦瑶，刘 慧，赵龙莲，张晔晖. 近红外高光谱的活体玉米叶片水分成像研究[J]. 光谱学与光谱分析, 2018, 38(12): 3743-3747.
YANG Yu-qing, ZHANG Tian-tian, LI Jun-hui, LU Meng-yao, LIU Hui, ZHAO Long-lian, ZHANG Ye-hui. Water Imaging of Living Corn Leaves Based on Near-Infrared Hysperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(12): 3743-3747.
[1] LI He-sheng(李合生). Modern Plant Physiology(现代植物生理学). Beijing: Higher Education Press(北京: 高等教育出版社), 2002. 39.
[2] CHEN Xiang, LI Min-zan, SUN Hong(陈 香，李民赞，孙 红). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(21): 137.
[3] Mizukami Y, Sawai Y, Yamaguchi Y, et al. Biosystems Engineering, 2006，93(3): 293.
[4] JIN Shu-de(金树德). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 1999, 15(3)：91.
[5] FAN Yang-yang, QIU Zheng-jun, CHEN Jian, et al(樊阳阳, 裘正军, 陈 俭, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(3): 836.
[6] Xie C Q, Yang C, He Y, et al. Computers and Electronics in Agriculture, 2017, 135: 154.
[7] Kamruzzaman M, Makino Y, Oshita S, et al. Journal of Food Engineering, 2016, 170: 8.
[8] ElMasry G, Sun D W, Allen P, et al. Journal of Food Engineering, 2012, 100(1): 127.
[9] Qin J W, Thomas F B, Mark A R, et al. Journal of Food Engineering, 2009, 93(2): 183.
[10] Ariana D P, Lu R F, Guyer D E, et al. Computers and Electronics in Agriculture, 2006, 53(1): 60.
[11] CAI Jia-yue, LIANG Miao, WEN Ya-dong, et al(蔡嘉月，梁 淼，温亚东，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2014, 34(10): 2758.
[12] ZHENG Jian-hong, WU Rui-mei, XIONG Jun-fei, et al(郑建鸿，吴瑞梅，熊俊飞，等). Laser Journal(激光杂志)，2016，37(6)：57.
[13] ZHAO Jie-wen, ZHAO Xiao-bing, et al(赵杰文, 周晓兵, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 1999, 15(3): 39.
[14] Higa S, Kobori H, Tsuchikawa S, et al. Appl. Spectrosc., 2013, 67(11): 1302.
[15] LIU Yan-de, JIANG Xiao-gang, ZHOU Yan-hua, et al(刘燕德, 姜小刚，周衍华，等). Journal of Chinese Agricultural Mechanization(中国农机化学报), 2016, (3): 218.
[16] Sarmento Junior Mazivila，Felipe Bachion de Santana, Hery Mitsutake, et al. Fuel, 2015, 142: 222.
[17] Latt Z Z, Wittenberg H. Water Resources Management, 2014, 28(8): 2109.
[18] YAN Yan-lu(严衍禄). Principle, Technology and Application of NIR Spectra Analysis(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2013.
