多光谱图像的玉米叶片含水量检测

doi:10.3964/j.issn.1000-0593(2020)04-1257-06

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Abstract Water is part of the essential elements for the normal growth and development of plants. The ability to detect and obtain plant leaf moisture quickly is of great importance to the study of crop irrigation production management and the physiological water demand characteristics of crops in the field. Using RedEdge-M multispectral camera, 55 groups of maize leaves at different growth stages were selected as the test objects, and the test maize leaf samples were photographed in a mellow light environment without shading. During the photographing process, the influence of solar elevation angle on spectral reflection was eliminated by directly connecting down light sensors, and TIFF images in 5 bands of blue, green, red, near-infrared and red edges were obtained by photographing each group of maize leaf samples. With the help of image processing software ENVI 5.3, the region of interest (ROI) of maize leaf samples was constructed, and the average reflection spectrum of maize leaf samples within the ROI range was used as the reflection spectrum of the samples to reduce the error caused by lens edge dimming phenomenon. According to the calibration reflectivity of the standard white board, the average reflection spectrum in the ROI range of the white board and the average reflection spectrum in the ROI range of the maize leaf sample white board, the ratio was converted to obtain the spectral reflectivity of each group of maize leaves at five bands. At the same time, using YLS-D chlorophyll meter, using five-point sampling method. The average water thickness of maize leaf samples was measured in five areas of maize leaf as the measurement index of leaf water content. Randomly selected spectral reflectance of 43 sets of maize leaf samples as training samples, using BP neural network to build an inversion model of maize leaf water content, which was based on multi-spectral image, and the Levenberg-Marquardt method was introduced to improve the existing shortcomings of classical neural network. The number of input neurons was 5, that is, the reflectance corresponding to the five and images of blue, green, red, near-infrared and red-edged, and the output neurons were one, that is, the moisture content of the maize leaves. The remaining 12 sets of samples were invoked as verification samples for correlation verification analysis of model inversion data. The results showed that the multispectral image spectral information combined with the improved BP neural network based on the Levenberg-Marquardt method can be utilized to retrieve the water content of the maize leaf. The fitting correlation coefficient of the model inversion can reach 0.896 37. As a verification of the 12 groups of maize leaves moisture reference value and the inversion value of the correlation coefficient R² reaches 0.894 8, the inversion result is ideal. It can realize the rapid and accurate detection of the moisture content of maize leaves, and provides a method and reference for the promotion and application of precision agriculture.

Key words：Multispectral images; Maize leaves; Water content; BP Neural network

Received: 2019-02-15 Accepted: 2019-06-26

ZTFLH:

O657.3

Corresponding Authors: ZHENG Yong-jun E-mail: zyj@cau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)04-1257-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I04/1257

[1] Liang J, Xia J, Liu L, et al. Journal of Plant Ecology, 2013, 6(6): 437.
[2] CHEN Xiang, LI Min-zan, SUN Hong, et al(陈香，李民赞，孙红，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017，(S1): 137.
[3] WU Qiong, ZONG Ying, XU Zhi, et al(吴琼，宗迎，徐志，等). Chinese Journal of Tropical Agriculture(热带农业科学), 2013, 33(1): 58.
[4] GUO Wen-chuan, LIU Dong-xue, ZHOU Chao-chao, et al(郭文川，刘东雪，周超超，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2014, 45(10): 288.
[5] Dadshani S, Kurakin A, Amanov S, et al. Plant Methods, 2015, 11(1): 8.
[6] Salas E A L, Boykin K G, Valdez R. Remote Sensing, 2016, 8(1): 78.
[7] Bedka K M, Khlopenkov K V. Journal of Applied Meteorology & Climatology, 2016, 55(9): 1983.
[8] Dai X, Zhuang D. Journal of Coastal Research, 2019, 93(S1): 524.
[9] Martinez-Domingo M A, Valero E M, Hernández-Andrés J, et al. Optics Express, 2017, 25(24): 30073.
[10] Sebastian C, Fabio R, Michaela D G, et al. Remote Sensing, 2015, 7(4): 4026.
[11] Yurovskaga M, Kudryavtsev V, Chapron B, et al. Remote Sensing of Environment, 2019, 234: 111468.
[12] Bhateja V, Moin A, Srivastava A, et al. Review of Scientific Instruments, 2016, 87(7): 1.
[13] Cai F, Lu W, Shi W, et al. Scientific Reports, 2017, 7(1): 15602.
[14] CHEN Si-ming, ZOU Shuang-quan, MAO Yan-ling, et al(陈思明，邹双全，毛艳玲，等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2018，38(3): 912.
[15] YANG Min, LIN Jie, GU Zhe-yan, et al(杨敏，林杰，顾哲衍，等). Science of Soil and Water Conservation(中国水土保持科学)，2015，4：90.
[16] Potgieter A B, Georgejaeggli B, Chapman S C, et al. Frontiers in Plant Science, 2017, 8: 1532.
[17] Backoulou G F, Elliott N C, Giles K L, et al. Computers & Electronics in Agriculture, 2015, 115: 34.
[18] FENG Hai-kuan, LI Zhen-hai, JIN Xiu-liang, et al(冯海宽，李振海，金秀良，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(12): 165.
[19] Cao W, Wang X, Ming Z, et al. Neurocomputing, 2018, 275: 278..
[20] Mahmoudimeimand H, Nazif S, Abbaspour R A, et al. Surveyor, 2016, 61(1): 233.