Effect of Near Infrared Hyperspectral Imaging Scanning Speed on Prediction of Water Content in Arabidopsis
LÜ Meng-qi1, SONG Yu-jie4, WENG Hai-yong1, 3, SUN Da-wei1, 3, DONG Xiao-ya2, FANG Hui1, 3, CEN Hai-yan1, 3*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2. School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
3. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
4. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Abstract：Hyperspectral imaging technology can non-destructively detect physicochemical information of plants with different dimensions. Existing researches often focus on analyzing the average spectrum of hyperspectral images, ignoring the information of their spatial dimensions. In this study, the model plant Arabidopsis thaliana was used as the research object to explore the influence of spatial resolution difference caused by different scanning speeds of hyperspectral imaging on the measurement of plant canopy moisture content, and to provide optimization for rapid online detection of plant canopy moisture content by hyperspectral imaging program. An open-line hyperspectral image of the Arabidopsis canopy was extracted using an indoor online hyperspectral imaging system at 20, 30 and 40 mm·s-1, and the average of the Arabidopsis thaliana canopy reflectance spectrum was extracted. Secondly, the quantitative analysis model of canopy water content and the average reflectance spectrum of Arabidopsis thaliana was established by Partial Least Squares Regression (PLSR). The determination coefficient (R2), root mean square error (root), mean squared error (RMSE) and relative variance deviation (RPD) were used to evaluate the model. The PLSR model based on pre-processing spectra such as the original spectrum, Multiplicative Scatter Correction (MSC) algorithm and Savitsky-Golay smoothing algorithm is compared. The best spectral pre-processing method is selected for subsequent data processing. Finally, the successive projections algorithm (SPA) is used to analyze and compare the prediction accuracy based on the optimal feature wavelength and the full wavelength, and to determine the influence of the hyperspectral image scanning speed on the canopy water content prediction of Arabidopsis thaliana. The results show that when the scanning speed was increased from 20 to 30 mm·s-1, the full-band PLSR model based on MSC pretreatment predicted that the coefficient of canopy moisture content in Arabidopsis was reduced by 0.88%, less than 1%. When the scanning speed was increased from 20 to 40 mm·s-1, the coefficient of determination of canopy water content in Arabidopsis was reduced by 2.3%. It shows that while the scanning speed is properly increased, the high water content prediction accuracy of the plant canopy can be ensured. Changing the hyperspectral scanning speed can more effectively utilize the spatial information of the hyperspectral image space. After the scanning speed is appropriately increased, the spatial dimension information of the hyperspectral image changes, improving the image collection efficiency of the actual production application and reducing the data processing time.
吕梦琪，宋宇杰，翁海勇，孙大伟, 董晓娅，方 慧，岑海燕. 近红外高光谱成像扫描速度对拟南芥冠层含水率预测的影响[J]. 光谱学与光谱分析, 2020, 40(11): 3508-3514.
LÜ Meng-qi, SONG Yu-jie, WENG Hai-yong, SUN Da-wei, DONG Xiao-ya, FANG Hui, CEN Hai-yan. Effect of Near Infrared Hyperspectral Imaging Scanning Speed on Prediction of Water Content in Arabidopsis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3508-3514.
[1] WENG Hai-yong, CEN Hai-yan, HE Yong（翁海勇, 岑海燕, 何 勇）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2018, 38(1): 235.
[2] Rymaszewski W, Dauzat M, Bediee A, et al. Bio-Protocol, 2018, 8(4): e2739.
[3] ZHAO Jing, HUANG Cao-jun, LI Bo-shi（赵 晶, 黄操军, 李博识）. Agricultural Technology and Information（农业科技与信息）, 2018, (16)：46.
[4] SONG Zhen, JI Chang-ying, ZHANG Bo（宋 镇, 姬长英, 张 波）. Jiangsu Journal of Agricultural Sciences（江苏农业学报）, 2019，35(2)：436.
[5] ZHANG Ya-wei, WANG Shu-mao, CHEN Du, et al（张亚伟, 王书茂, 陈 度, 等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2017, 48(OS1): 118.
[6] Ariana D P, Lu R. Journal of Food Engineering, 2010, 96: 583.
[7] ZHANG Hai-liang, GAO Jun-feng, HE Yong（章海亮, 高俊峰, 何 勇）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2013, 44(9): 177.
[8] ZHOU Zhu, LI Xiao-yu, TAO Hai-long, et al(周 竹，李小昱，陶海龙，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(21): 221.
[9] Röemer C, Wahabzada M, Ballvora A, et al. Functional Plant Biology, 2012, 39: 878.
[10] Ge Y, Bai G, Stoerger V, et al. Computers and Electronics in Agriculture, 2016, 127: 625.
[11] Pandey P, Ge Y, Stoerger V, et al. Frontiers in Plant Science, 2017, 8：1348.
[12] Kim D M, Zhang H, Zhou H, et al. Scientific Reports, 2015, 5：15919.
[13] Yuan F, Yang H M, Xue Y, et al. Nature, 2014, 514(7522): 367.
[14] Leone A P, Viscarra-Rossel R A, Amenta P, et al. Current Analytical Chemistry, 2012, 8: 283.
[15] Bilgili A V, van Es H M, Akbas F, et al. Journal of Arid Environments, 2010, 74: 229.
[16] Helland I S, Sbø S, Almøy T, et al. Journal of Chemometrics, 2018, 32(9)：e3044.
[17] Sutton M, Thiébaut R, Liquet B. Statistics in Medicine，2018, 37: 3338.
[18] ZHANG Chu, LIU Fei, KONG Wen-wen, et al(张 初，刘 飞，孔汶汶，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2013, 29(20): 270.
[19] ZHOU Zhu, YIN Jian-xin, ZHOU Su-yin, et al（周 竹, 尹建新, 周素茵, 等）. Laser & Optoelectronics（激光与光电子学进展）, 2017, 54(2): 311.
