基于可见-近红外光谱的柑橘叶片功能性氮含量无损监测模型研究

doi:10.3964/j.issn.1000-0593(2023)11-3396-08

Abstract
References
Related Citation (15)

Download: PDF (3643 KB)
Export: BibTeX | EndNote (RIS)

Abstract Citrus is the largest kind of fruit in China. Nitrogen is very important for the growth and development of citrus. Real-time and non-destructive monitoring of the nitrogen status of citrus is of great significance for accurate management of nitrogen nutrients. Nitrogen in plants can be divided into assimilable nitrogen, structural nitrogen and functional nitrogen. The content of each component of different forms of nitrogen in citrus leaves has a certain indicative effect on the physiological and biochemical reactions of leaves. Among them, the content of functional nitrogen is an important indicator of nitrogen nutrition status in citrus. “Chunjian” orange was used as the experimental material in this study. The reflectance spectra of citrus leaves under different nitrogen treatments were measured by the visible-near infrared spectrometer at the fruit swelling period and fruit coloring period, and the functional nitrogen content in leaves was determined by chemical analysis. The correlation between the original spectrum, first-order differential spectrum and the functional nitrogen content of leaves at the fruit swelling and fruit coloring periods of citrus was analyzed, and the sensitive bands were selected. The non-destructive monitoring model of the functional nitrogen content of leaves at the fruit swelling period and fruit coloring period of citrus was constructed by using the full-band and sensitive bands, combined with the spectral vegetation index method, spectral chemical measurement method and machine learning method, and the effects of various spectral variants and spectral preprocessing methods on the accuracy of the model were compared and analyzed. The results showed that the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by standard normal variate transformation pretreatment of the full-band original spectrum combined with the backpropagation neural network had high accuracy during thefruit swelling period. The calibration set determination coefficient R²_c and validation set determination coefficient R²_v were all 0.78, and the RMSEC and RMSEV of the modeling set were all 0.82 g·kg^-1. The model accuracy based on the original spectrum of the sensitive band combined with the random forest was also high, with R²_c and RMSEC were 0.84 and 0.67 g·kg^-1, R²_v and RMSEV were 0.74 and 0.83 g·kg^-1, respectively. In the fruit coloring period of citrus, the full-band original spectrum was preprocessed by standard normal variate transformation. The accuracy of the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by BPNN was high, with R²_c and RMSEC was 0.77 and 1.04 g·kg^-1, R²_v and RMSEV were 0.76 and 1.13 g·kg^-1, respectively. The study has shown that visible-near infrared spectroscopy can achieve non-destructive monitoring of functional nitrogen content in citrus leaves.

Key words：Citrus; Functional nitrogen; Visible-near infrared spectroscopy; Back propagation neural network; Random forest

Received: 2022-04-28 Accepted: 2022-12-10

ZTFLH:

O657.3

Corresponding Authors: WANG Jie E-mail: mutouyu@swu.edu.cn

Cite this article:

YANG Qun,LING Qi-han,WEI Yong, et al. Non-Destructive Monitoring Model of Functional Nitrogen Content in Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)11-3396-08 OR https://www.gpxygpfx.com/EN/Y2023/V43/I11/3396

[1] ZHANG Yun-hong, SHEN Qi-kun, DU Jun, et al（张运红，申其昆，杜君，等）. Journal of Triticeae Crops（麦类作物学报）, 2017, 37（11）: 1503.
[2] Liu Tao, Ren Tao, White Philip J, et al. Journal of Experimental Botany, 2018, 69（12）: 2995.
[3] Yang Xi, Tang Jianwu, Mustard John F, et al. Remote Sensing of Environment, 2016, 179: 1.
[4] Wang Jie, Zhao Huabing, Shen Changwei, et al. Agronomy Journal, 2014, 106（5）: 1867.
[5] WANG Jiao-jiao, SONG Xiao-yu, MEI Xin, et al（王娇娇，宋晓宇，梅新，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2021, 41（6）: 1722.
[6] HUANG Shuang-ping, HONG Tian-sheng, YUE Xue-jun, et al（黄双萍，洪添胜，岳学军，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2013, 29（5）: 132.
[7] LI Xiao-li, ZHANG Dong-yi, DONG Yu-lun, et al（李晓丽，张东毅，董雨伦，等）. Journal of China Agricultural University（中国农业大学学报）, 2021, 26（11）: 113.
[8] WANG Jun-jie, CHEN Ling, WANG Hai-gang, et al（王君杰，陈凌，王海岗，等）. Scientia Agricultura Sinica（中国农业科学）, 2019, 52（15）: 2593.
[9] ZHENG Chao, XIA Hai-ming, ZHOU Jia-cheng, et al（郑超，夏海鸣，周佳骋，等）. Chinese Journal of Analysis Laboratory（分析试验室）, 2015, 34（3）: 359.
[10] LIN Min, LÜ Jin（林敏，吕进）. Infrared Technology（红外技术）, 2004，（3）: 78.
[11] FENG Zhi-li, XIAO Feng, LU Xiao-ping, et al（冯志立，肖锋，卢小平，等）. Bulletin of Surveying and Mapping（测绘通报）, 2022，（3）: 70.
[12] TIAN Xiao-yu, HUANG Xing-yi, BAI Jun-wen, et al（田潇瑜，黄星奕，白竣文，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2019, 50（2）: 350.
[13] XING Zhi-na, WANG Ju-xiang, LIU Jie, et al（邢志娜，王菊香，刘洁，等）. Physical Testing and Chemical Analysis(Part B: Chemical Analysis)（理化检验: 化学分册）, 2018, 54（4）: 419.