A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion

Authors

  • Xiu LIN Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Faduman HA Xinjiang Agricultural Vocational and Technical College, Changji, 831100 (CN)
  • Lulu MA Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Xiangyu CHEN Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Yiru MA Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Aiqun CHEN Kekedala Agricultural Technology Popularization Station of the Fourth Division of Xinjiang Production and Construction Corps, Kekedala, 835213 (CN)
  • Zhenan HOU Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Ze ZHANG Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)

DOI:

https://doi.org/10.15835/nbha51113059

Keywords:

characteristic parameter, chlorophyll fluorescence parameters, cotton leaf nitrogen, hyperspectral imaging, monitoring model

Abstract

In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring.

References

Bai ZP (2020). Remote sensing monitoring of wheat stripe rust based on reflectance spectroscopy and SIF. Xi'an: Xi'an University of Science and Technology, (in Chinese)

Barraclough PB, Howarth JR, Jones J, Lopez-Bellido R, Parmar S, Shepherd CE, Hawkesford MJ (2010). Nitrogen efficiency of wheat: Genotypic and environmental variation and prospects for improvement. European Journal of Agronomy 33(1):1-11. https://doi.org/10.1016/j.eja.2010.01.005

Blackburn GA (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing 19(4):657-675. https://doi.org/10.1080/014311698215919

Chen B, Zheng H, Luo G, Chen C, Bao A, Liu T, Chen X (2020). Estimating nitrogen content in cotton canopy using hyperspectral imaging. Journal of Irrigation and Drainage 39(03):35-41. (in Chinese)

Chen YS (2007). Quantitative study on the effects of nitrogen on post-flowering leaf photosynthesis and chlorophyll fluorescence parameters of cucumber in greenhouse. Nanjing: Nanjing Agricultural University. (in Chinese)

Ding YR, Li DM, Ma LL (2020). Study on inversion model of cotton chlorophyll fluorescence parameters and cotton growth index under drip irrigation. Agricultural Research in the Arid Areas 38(06):234-242. https://doi.org/10.7606/j.issn.1000-7601.2020.06.31

Dong HZ, Li WJ, Tang W, Li ZH (2000). Photosynthetic characters of field grown cotton leaves. Shandong Agricultural Sciences 6(7-9):15. (in Chinese) https://doi.org/10.3969/j.issn.1001-4942.2000.06.002

Feng W, He L, Zhang HY, Guo BB, Zhu YJ, Wang CY, Guo TC (2015). Assessment of plant nitrogen status using chlorophyll fluorescence parameters of the upper leaves in winter wheat. European Journal of Agronomy 64:78-87. https://doi.org/10.1016/j.eja.2014.12.013

Halihashi Y, Li QJ, Zhang Y (2019). Effects of organic manure nitrogen replacing chemical fertilizer nitrogen on cotton nutrient uptake, nitrogen utilization efficiency and yield. Soil and Fertilizer Sciences in China 3:137-142. (in Chinese). https://doi.org/10.11838/sfsc.1673-6257.18267

Hansen PM, Schjoerring JK(2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86(4):542-553. https://doi.org/10.1016/S0034-4257(03)00131-7

He T (2016). Hyperspectral remote sensing model for maize nitrogen monitoring. Shenyang: Shenyang Agricultural University. (in Chinese)

Hongyun YANG, Jianjun LUO, Aizhen S, Ying WAN, Wenlong YI (2020). Study on estimation model of total nitrogen content in rice leaves based on image characteristics. Acta Agriculturae Zhejiangensis 32:2232-2243. (in Chinese) https://doi.org/10.3969/j.issn.1004-1524.2020.12.15

Jay S, Maupas F, Bendoula R, Gorretta N (2017). Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research 210:33-46. https://doi.org/10.1016/j.fcr.2017.05.005

Jing X, Bai Z, Gao Y, Liu LY (2019). Wheat stripe rust monitoring by random forest algorithm combined with SIF and reflectance spectrum. Transactions of the Chinese Society of Agricultural Engineering 35(13):154-161. (in Chinese) https://doi.org/10.11975/j.issn.1002-6819.2019.13.017

Khamis S, Lamaze T, Lemoine Y, Foyer C (1990). Adaptation of the photosynthetic apparatus in maize leaves as a result of nitrogen limitation. Plant Physiology 94(3):1436-1443.1436-1443.

Li DM, Lv X, Luo HH (2020). A monitoring model of nitrogen nutrition of drip irrigation cotton based on chlorophyll fluorescence parameters. Cotton Science 32(01):63-76. (in Chinese) https://doi.org/10.11963/1002-7807.ldmzz.20191231

Li F, Li D, Elsayed S, Hu Y, Schmidhalter U (2021). Using optimized three-band spectral indices to assess canopy N uptake in corn and wheat. Science Direct. European Journal of Agronomy 127:126-286. https://doi.org/10.1016/j.eja.2021.126286

Li JH, Wang F, Li JW, Zou RB, Liao GP (2016). Multifractal methods for rapeseed nitrogen nutrition qualitative diagnosis modeling. Journal of Biological Mathematics 9:285-297. https://doi.org/10.1142/S1793524516500649

Li MF, Peng WY, Li HT (2016). Effects of the combined application of nitrogen and boron on the yield, petiole annulus and nutrient uptake of cotton. Soil and Fertilizer Sciences in China 4:97-102. (in Chinese) https://doi.org/10.11838/sfsc.20160416

Liu L, Chen YH, Lei ZX (2021). A study on high-quality development of the cotton industry of Xinjiang [J]. Macroeconomic Management 10:77-83. (in Chinese)

Lyu T, Shen J, Ma J, Ma P, Yang Z, Dai Z, ... Li M (2021). Effects of slow-release nitrogen fertilizer combined with urease inhibitor on photosynthetic characteristics of cotton. China Cotton 48(10):1-7. (in Chinese) https://doi.org/10.11963/cc20210128

Ma JF (2006). Nitrogen content monitoring in wheat and rice based on leaf chlorophyll fluorescence parameters. Nanjing: Nanjing Agricultural University. (in Chinese).

Ma L, Chen X, Zhang Q, Lin J, Yin C, Ma Y, ... Lv X (2022). Estimation of nitrogen content based on the hyperspectral vegetation indexes of interannual and multi-temporal in cotton. Agronomy 12(6):1319. https://doi.org/10.3390/agronomy12061319

Niu Z, Chen YH, Sui HZ, Zhang QY, Zhao CJ (2000). Mechanism analysis of leaf biochemical concentration by high spectral remote sensing. Journal of Remote Sensing 4(2):125-129. (in Chinese) https://doi.org/10.3321/j.issn:1007-4619.2000.02.008

Serrano L, Filella I, Penuelas J (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 40:723-731. https://doi.org/10.2135/cropsci2000.403723x

Shi XH, Cai HJ (2018). Estimation of nitrogen nutrition index of greenhouse tomato under different water and nitrogen fertilizer treatments based on leaf SPAD. Transactions of the Chinese Society of Agricultural Engineering 34:116-126. (in Chinese) https://doi.org/10.11975/j.issn.1002-6819.2018.17.016

Suo JY (2016). Evaluation of Nitrogen Application Efficiency and Nutritional Status of Cotton in Xinjiang. Urumqi: Xinjiang Agricultural University. (in Chinese)

Wang H, Guo Z, Shi Y, Zhang Y, Yu Z (2015). Impact of tillage practices on nitrogen accumulation and translocation in wheat and soil nitrate-nitrogen leaching in drylands. Soil and Tillage Research 153:20-27. https://doi.org/10.1016/j.still.2015.03.006

Wen PF (2016). A spectral estimation model for cotton nitrogen and a diagnosis system. Xinjiang: Shihezi University. (in Chinese)

Xu HC, Yao B, Wang Q, Chen TT, Zhu TZ, He H, ... Wu LQ (2021). Determination of suitable band width for estimating rice nitrogen nutrition index based on leaf reflectance spectra. Scientia Agricultura Sinica 54:4525-4539. (in Chinese) https://doi.org/10.3864/j.issn.0578-1752.2021.21.004

Xu W, Chen P, Zhan Y, Chen S, Zhang L, Lan Y (2021). Cotton yield estimation model based on machine learning using time series UAV remote sensing data. International Journal of Applied Earth Observation and Geoinformation 104:102511. https://doi.org/10.1016/j.jag.2021.102511

Zdoan G, Lin X, Sun DW (2021). Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends in Food Science & Technology 111:151-165. https://doi.org/10.1016/j.tifs.2021.02.044

Zhang L, Shangguan Z, Mao M, Yu G (2003). Effects of long-term application of nitrogen fertilizer on leaf chlorophyll fluorescence of upland winter wheat. Chinese Journal of Applied Ecology (05):695-698. (in Chinese)

Zhang WF, Gou L, Wang ZL (2003). Effect of nitrogen on chlorophyll fluorescence of leaves of high-yielding cotton in Xinjiang. Scientia Agricultura Sinica (08): 893-898. (in Chinese)

Zhong KG, Li N, Hua K (2021). Analysis and forecast of influencing factors of cotton yield in Xinjiang based on grey theory. Agriculture and Technology 41(21):1-3. (in Chinese) https://doi.org/10.19754/j.nyyjs.20211115001

Zhu Y, Tian Y, Yao X, Liu X, Cao W (2007). Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Production Science 10(4):400-411. https://doi.org/10.1626/pps.10.400

Živčák M, Olšovská K, Slamka P, Galambošová J, Rataj V, Shao HB, Brestič M (2014). Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency. Plant Soil and Environment 60(5):210-215. https://doi.org/10.2135/cropsci2013.08.0551

Published

2023-03-29

How to Cite

LIN, X., HA, F., MA, L., CHEN, X., MA, Y., CHEN, A., HOU, Z., & ZHANG, Z. (2023). A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 51(1), 13059. https://doi.org/10.15835/nbha51113059

Issue

Section

Research Articles
CITATION
DOI: 10.15835/nbha51113059

Most read articles by the same author(s)