A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves

Authors

  • Yiren DING Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Shizhe QIN Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (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)
  • Qiushuang YAO Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Mi YANG 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)
  • Xin LV 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/nbha50312775

Keywords:

cotton, chlorophyll fluorescence parameters, leaf position, machine learning, yield

Abstract

The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management.

References

Araus JL, Amaro T, Voltas J, Nakkoul H, Nachit MM (1998). Chlorophyll fluorescence as a selection criterion for grain yield in durum wheat under Mediterranean conditions. Field Crops Research 55(3):209-223. https://doi.org/10.1016/S0378-4290(97)00079-8

Ashapure A, Jung J, Chang A, Oh S, Yeom J, Maeda M, … Smith W (2020). Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data. SPRS Journal of Photogrammetry and Remote Sensing 169:180-194. https://doi.org/10.1016/j.isprsjprs.2020.09.015

Baha N (2021). Impact of bio-fertilizers on germination and early seedling growth of Alfalfa (Medicago sativa L.) under salt stress. Acta Physiologiae Plantarum 43(5):1-11. https://doi.org/10.1007/s11738-021-03248-8

Bussotti F, Pollastrini, M, Cascio C, Desotgiu R, Gerosa G, Marzuoli R, … Strasser RJ (2011). Conclusive remarks. Reliability and comparability of chlorophyll fluorescence data from several field teams. Environmental and Experimental Botany 73:116-119. https://doi.org/10.1016/j.envexpbot.2010.10.023

Calatayud Á, San Bautista A, Pascual B, Maroto JV, López-Galarza S (2013). Use of chlorophyll fluorescence imaging as diagnostic technique to predict compatibility in melon graft. Scientia Horticulturae 149:13-18. https://doi.org/10.1016/j.scienta.2012.04.019

Chaerle L, Hagenbeek D, De Bruyne E, Valcke R, Van Der Straeten D (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant and Cell Physiology 45(7):887-896. https://doi.org/10.1093/pcp/pch097

Dehkordi PA, Nehbandan A, Hassanpour-bourkheili S, Kamkar B (2020). Yield gap analysis using remote sensing and modelling approaches: Wheat in the northwest of Iran. International Journal of Plant Production 14(3):443-452. https://doi.org/10.1007/s42106-020-00095-4

Dong Z, Men Y, Liu Z, Li J, Ji J (2020). Application of chlorophyll fluorescence imaging technique in analysis and detection of chilling injury of tomato seedlings. Computers and Electronics in Agriculture 168:105109. https://doi.org/10.1016/j.compag.2019.105109

Fang H, Liang S, Hoogenboom G (2011). Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation. International Journal of Remote Sensing 32(4):1039-1065. https://doi.org/10.1080/01431160903505310

Faraloni C, Cutino I, Petruccelli R, Leva AR, Lazzeri S, Torzillo G (2011). Chlorophyll fluorescence technique as a rapid tool for in vitro screening of olive cultivars (Olea europaea L.) tolerant to drought stress. Environmental and Experimental Botany 73:49-56. https://doi.org/10.1016/j.envexpbot.2010.10.011

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

Gabriel JL, Zarco-Tejada PJ, López-Herrera PJ, Pérez-Martín E, Alonso-Ayuso M, Quemada M (2017). Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering 160:124-133. https://doi.org/10.1016/j.biosystemseng.2017.06.003

Guo BB, Qi SL, Heng YR, Duan JZ, Zhang HY, Wu YP, … Zhu YJ (2017). Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. European Journal of Agronomy 82:113-124. https://doi.org/10.1016/j.eja.2016.10.009

Hutmacher RB, Travis RL, Nichols RL, Rains DE, Roberts BA, Weir BL, … Perkins S (2001). Response of Acala cotton to nitrogen rates in the San Joaquin Valley of California. The Scientific World Journal 1:691-698. https://doi.org/10.1100/tsw.2001.334

Janka E, Körner O, Rosenqvist E, Ottosen CO (2013). High temperature stress monitoring and detection using chlorophyll a fluorescence and infrared thermography in chrysanthemum (Dendranthema grandiflora). Plant Physiology and Biochemistry 67:87-94. https://doi.org/10.1016/j.plaphy.2013.02.025

Kalaji HM, Schansker G, Brestic M, Bussotti F, Calatayud A, Ferroni L, … Bąba W (2017). Erratum to: Frequently asked questions about chlorophyll fluorescence, the sequel. Photosynthesis Research 132(1):67. https://doi.org/10.1007/s11120-017-0356-0

Kalaji HM, Schansker G, Ladle RJ, Goltsev V, Bosa K, Allakhverdiev SI, … Zivcak M (2014). Frequently asked questions about in vivo chlorophyll fluorescence: practical issues. Photosynthesis Research 122(2):121-158. https://doi.org/10.1007/s11120-016-0318-y

Li Y, Zhou Q, Zhou J, Zhang G, Chen C, Wang J (2014). Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions. Ecological Modelling 291:15-27. https://doi.org/10.1016/j.ecolmodel.2014.07.013

Li Z, Wang J, Xu X, Zhao C, Jin X, Yang G, Feng H (2015). Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation. Remote Sensing 7(9):12400-12418. https://doi.org/10.3390/rs70912400

Lin ZH, Zhong QS, Chen CS, Ruan QC, Chen ZH, You XM (2016). Carbon dioxide assimilation and photosynthetic electron transport of tea leaves under nitrogen deficiency. Botanical Studies 57(1):1-12. https://doi.org/10.1186/s40529-016-0152-8

Murchie EH, Lawson T (2013). Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. Journal of Experimental Botany 64(13):3983-3998. https://doi.org/10.1093/jxb/ert208

Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture 121:57-65. https://doi.org/10.1016/j.compag.2015.11.018

Peng S, Garcia FV, Laza RC, Sanico AL, Visperas RM, Cassman KG (1996). Increased N-use efficiency using a chlorophyll meter on high-yielding irrigated rice. Field Crops Research 47(2-3):243-252. https://doi.org/10.1016/0378-4290(96)00018-4

Ptushenko VV, Ptushenko OS, Tikhonov AN (2014). Chlorophyll fluorescence induction, chlorophyll content, and chromaticity characteristics of leaves as indicators of photosynthetic apparatus senescence in arboreous plants. Biochemistry (Moscow) 79(3):260-272. https://doi.org/10.1134/S0006297914030122

Qu S, Li Z, Qiu C, Yang G, Song X, Chen Z, Liu C (2017). Remote sensing prediction of winter wheat grain protein content based on nitrogen nutrition index at anthesis stage. Transactions of the Chinese Society of Agricultural Engineering 33(12):186-193. https://doi.org/10.11975/j.issn.1002-6819.2017.12.024

Schächtl J, Huber G, Maidl FX, Sticksel E, Schulz J, Haschberger P (2005). Laser-induced chlorophyll fluorescence measurements for detecting the nitrogen status of wheat (Triticum aestivum L.) canopies. Precision Agriculture 6(2):143-156. https://doi.org/10.1007/s11119-004-1031-y

Souza de R, Peña-Fleitas MT, Thompson RB, Gallardo M, Grasso R, Padilla FM (2022). Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops. Precision Agriculture 23(1):278-299. https://doi.org/10.1007/s11119-021-09837-4

Sun D, Xu H, Weng H, Zhou W, Liang Y, Dong X, … Cen H (2020). Optimal temporal–spatial fluorescence techniques for phenotyping nitrogen status in oilseed rape. Journal of Experimental Botany 71(20):6429-6443. https://doi.org/10.1093/jxb/eraa372

Tian Y, Zhu Y, Cao W (2005). Monitoring leaf photosynthesis with canopy spectral reflectance in rice. Photosynthetica 43(4):481-489. https://doi.org/10.1007/s11099-005-0078-y

Wang L, Poque S, Valkonen J (2019). Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant Methods 15(1):1-14. https://doi.org/10.1186/s13007-019-0501-1

Wang W, Wang C, Pan D, Zhang Y, Luo B, Ji J (2018). Effects of drought stress on photosynthesis and chlorophyll fluorescence images of soybean (Glycine max) seedlings. International Journal of Agricultural and Biological Engineering 11(2):196-201. https://doi.org/10.25165/j.ijabe.20181102.3390

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

Yang C, Everitt JH, Bradford JM (2006). Evaluating high-resolution QuickBird satellite imagery for estimating cotton yield. Transactions of the ASABE 49(5):1599-1606. https://doi.org/10.13031/2013.22034

Zhao B, Duan A, Ata-Ul-Karim ST, Liu Z, Chen Z, Gong Z, … Ning D (2018). Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy 93:113-125. https://doi.org/10.1016/j.eja.2017.12.006

Zhou C, Le J, Hua D, He T, Mao J (2019). Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments. Measurement 136:478-486. https://doi.org/10.1016/j.measurement.2018.12.088

Živčák M, Olšovská K, Slamka P, Galambošová J, Rataj V, Shao HB, Brestič M (2015). 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

2022-09-22

How to Cite

DING, Y., QIN, S., MA, L., CHEN, X., YAO, Q., YANG, M., MA, Y., LV, X., & ZHANG, Z. (2022). A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 50(3), 12775. https://doi.org/10.15835/nbha50312775

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Section

Research Articles
CITATION
DOI: 10.15835/nbha50312775

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