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Phenology affects differentiation of crop and weed species using hyperspectral remote sensing

Published online by Cambridge University Press:  18 August 2020

Nicholas T. Basinger Open the ORCID record for Nicholas T. Basinger [Opens in a new window] ,
Katherine M. Jennings Open the ORCID record for Katherine M. Jennings [Opens in a new window] ,
Erin L. Hestir Open the ORCID record for Erin L. Hestir [Opens in a new window] ,
David W. Monks ,
David L. Jordan  and
Wesley J. Everman Open the ORCID record for Wesley J. Everman [Opens in a new window]
Nicholas T. Basinger*
Affiliation:
Graduate Research Assistant, North Carolina State University, Department of Horticultural Science, Raleigh, NC, USA
Katherine M. Jennings
Affiliation:
Associate Professor, North Carolina State University, Department of Horticulture Science, Raleigh, NC, USA
Erin L. Hestir
Affiliation:
Assistant Professor, University of California, Department of Civil and Environmental Engineering, Merced, CA, USA
David W. Monks
Affiliation:
Professor, North Carolina State University, Department of Horticultural Science, Raleigh, NC, USA
David L. Jordan
Affiliation:
Professor, North Carolina State University, Department of Crop and Soil Science, Raleigh, NC, USA
Wesley J. Everman
Affiliation:
Associate Professor, North Carolina State University, Department of Crop and Soil Science, Raleigh, NC, USA
*
Author for correspondence: Nicholas T. Basinger, Department of Crop and Soil Sciences, University of Georgia, 3111 Miller Plant Sciences, 120 Carlton St., Athens, GA 30602. (Email: nicholas.basinger@uga.edu)
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Abstract

The effect of plant phenology and canopy structure of four crops and four weed species on reflectance spectra were evaluated in 2016 and 2017 using in situ spectroscopy. Leaf-level and canopy-level reflectance were collected at multiple phenologic time points in each growing season. Reflectance values at 2 wk after planting (WAP) in both years indicated strong spectral differences between species across the visible (VIS; 350–700 nm), near-infrared (NIR; 701–1,300 nm), shortwave-infrared I (SWIR1; 1,301–1,900 nm), and shortwave-infrared II (SWIR2; 1,901–2,500 nm) regions. Results from this study indicate that plant spectral reflectance changes with plant phenology and is influenced by plant biophysical characteristics. Canopy-level differences were detected in both years across all dates except for 1 WAP in 2017. Species with similar canopy types (e.g., broadleaf prostrate, broadleaf erect, or grass/sedge) were more readily discriminated from species with different canopy types. Asynchronous phenology between species also resulted in spectral differences between species. SWIR1 and SWIR2 wavelengths are often not included in multispectral sensors but should be considered for species differentiation. Results from this research indicate that wavelengths in SWIR1 and SWIR2 in conjunction with VIS and NIR reflectance can provide differentiation across plant phenologies and, therefore should be considered for use in future sensor technologies for species differentiation.

Keywords

Hyperspectral remote sensingplant morphologyspecies differentiationspectral discriminationspectral variabilityspectroscopy

Information

Type
Research Article
Information
Weed Technology , Volume 34 , Issue 6 , December 2020 , pp. 897 - 908
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Weed Science Society of America

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Footnotes

Associate Editor: Prashant Jha, Iowa State University

References

Ahmadiani, N, Robbins, RJ, Collins, TM, Giusti, MM (2016) Molar absorptivity (ϵ) and spectral characteristics of cyanidin-based anthocyanins from red cabbage. Food Chem 197:900–906 CrossRefGoogle ScholarPubMed
Andrew, ME, Ustin, SL (2009) Habitat suitability modeling of an invasive plant with advanced remote sensing data. Divers Distrib 15:627–640 CrossRefGoogle Scholar
Andrew, ME, Ustin, SL (2008) The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens Environ 112:4301–4317 CrossRefGoogle Scholar
Asen, S, Stewart, R N, Norris, K H (1972). Co-pigmentation of anthocyanins in plant tissues and its effect on colour. Phytochemistry, 11, 1139–1144 CrossRefGoogle Scholar
Asner, GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ 64:234–253 CrossRefGoogle Scholar
Bandyopadhyay, KK, Pradhan, S, Sahoo, RN, Singh, R, Gupta, VK, Joshi, DK, Sutradhar, AK (2014) Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices. Agric Water Manag 146:115–123 CrossRefGoogle Scholar
Basinger, NT (2018) Interference and spectral changes of Palmer amaranth (Amaranthus palmeri S. Wats.) and large crabgrass [Digitaria sanguinalis (L.) Scop.] density in sweetpotato and soybean and use of spectroscopy for discrimination of crop and weed species. Ph.D dissertation. Raleigh, NC: North Carolina State University. 989 pGoogle Scholar
Blackburn, GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:855–867 CrossRefGoogle ScholarPubMed
Bolch, EA, Santos, MJ, Ade, C, et al. (2020) Remote detection of invasive alien species. Pages 267–307 in: Cavender-Bars, J, Gamon, JA, Towsend, PA (eds.). Remote Sensing of Plant Diversity. Berlin, Germany: SpringerOpen CrossRefGoogle Scholar
Burks, TF, Shearer, SA, Green, JD, Heath, JR (2002) Influence of weed maturity levels on species classification using machine vision. Weed Sci 50:802–811 CrossRefGoogle Scholar
Burks, TF, Shearer, SA, Heath, JR, Donohue, KD (2005). Evaluation of neural-network classifiers for weed species discrimination. Biosyst Eng 91(3):293–304 CrossRefGoogle Scholar
Casanova, D, Epema, GF, Goudriaan, J (1998) Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crop Res 55:83–92 CrossRefGoogle Scholar
Cho, MA, Debba, P, Mathieu, R, Naidoo, L, Van Aardt, J, Asner, GP (2010) Improving discrimination of savannah tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis. IEEE Trans Geosci Remote Sens 48:4133–4142 Google Scholar
Cohen, Y, Alchanatis, V, Zusman, Y, Dar, Z, Bonfil, DJ, Karnieli, A, Zilberman, A, Moulin, A, Ostrovsky, V, Levi, A, Brikman, R, Shenker, M (2010) Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite. Precis Agric 11:520–537 CrossRefGoogle Scholar
Corder, GW, Foreman, DI (2009) Nonparametric Statistics for Non-Statisticians. Hoboken, NJ: John Wiley and Sons Google Scholar
Curran, PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278 CrossRefGoogle Scholar
Dayan, FE, Green, HM, Weete, JD, Hancock, HG (1996) Postemergence activity of sulfentrazone: effects of surfactants and leaf surfaces. Weed Sci 44:797–803 CrossRefGoogle Scholar
Del Fiore, A, Reverberi, M, Ricelli, A, Pinzari, F, Serranti, S, Fabbri, AA, Bonifazi, G, Fanelli, C (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int J Food Microbiol 144:64–71 CrossRefGoogle ScholarPubMed
Dian Bah, M, Hafiane, A, Canals, R (2018) Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sens 10:1–23 Google Scholar
Diker, K, Bausch, WC (2003) Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosyst Eng 85:437–447 CrossRefGoogle Scholar
Ehleringer, J (1983) Ecophysiology of Amaranthus palmeri, a Sonoran Desert summer annual. Oecologia 57:107–112 CrossRefGoogle ScholarPubMed
Everman, WJ, Medlin, CR, Dirks, RD, Bauman, TT, Biehl, L (2008) The effect of postemergence herbicides on the spectral reflectance of corn. Weed Technol 22:514–522 CrossRefGoogle Scholar
Farooq, A, Jia, X, Hu, J, Zhou, J (2019) Multi-resolution weed classification via convolutional neural network and superpixel based local binary pattern using remote sensing images. Remote Sens 11:1692 CrossRefGoogle Scholar
Gamon, JA, Serrano, L, Surfus, JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:492–501 CrossRefGoogle ScholarPubMed
Glenn, NF, Mundt, JT, Weber, KT, Prather, TS, Lass, LW, Pettingill, J (2005) Hyperspectral data processing for repeat detection of small infestations of leafy spurge. Remote Sens Environ 95:399–412 CrossRefGoogle Scholar
Goel, PK, Prasher, SO, Landry, JA, Patel, RM, Bonnell, RB, Viau, AA, Miller, JR (2003) Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Comput Electron Agric 38:99–124 CrossRefGoogle Scholar
Gray, CJ, Shaw, DR, Bruce, LM (2009) Utility of hyperspectral reflectance for differentiating soybean (Glycine max) and six weed species. Weed Technol 23:108–119 CrossRefGoogle Scholar
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 Sens Environ 86:542–553 CrossRefGoogle Scholar
Hemming, J, Rath, T (2001) Computer-vision-based weed identification under field conditions using controlled lighting. J Agric Eng Res 78:233–243 CrossRefGoogle Scholar
Henry, WB, Shaw, DR, Reddy, KR, Bruce, LM, Tamhankar, HD (2004a) Remote sensing to distinguish soybean from weeds after herbicide application. Weed Technol 18:594–604 CrossRefGoogle Scholar
Henry, WB, Shaw, DR, Reddy, KR, Bruce, LM, Tamhankar, HD (2004b) Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture. Weed Sci 52:788–796 CrossRefGoogle Scholar
Horak, MJ, Loughin, TM (2006) Growth analysis of four Amaranthus species. Weed Sci 48:347–355 CrossRefGoogle Scholar
Huang, Y, Lee, MA, Thomson, SJ, Reddy, KN (2016) Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric Biol Eng 9:98–109 Google Scholar
Hung, C, Xu, Z, Sukkarieh, S (2014) Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sens 6:12037–12054 CrossRefGoogle Scholar
Hunt, ER, Daughtry, CST, Mirsky, SB, Hively, WD (2014) Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications. IEEE J Sel Top Appl Earth Obs Remote Sens 7:4566–4571 CrossRefGoogle Scholar
Islam, MS, Jalaluddin, M, Garner, JO, Yoshimoto, M, Yamakawa, O (2005) Artificial shading and temperature influence on anthocyanin compositions in sweetpotato leaves. HortScience 40:176–180 CrossRefGoogle Scholar
Islam, MS, Yoshimoto, M, Terahara, N, Yamakawa, O (2002) Anthocyanin compositions in sweetpotato (ipomoea batatas L.) leaves. Biosci Biotechnol Biochem 66:2483–2486 CrossRefGoogle ScholarPubMed
Jain, N, Ray, SS, Singh, JP, Panigrahy, S (2007) Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precis Agric 8:225–239 CrossRefGoogle Scholar
Jensen, JR (2007) Remote Sensing of the Environment: An Earth Resource Persepective. 2nd edn. Upper Saddle River, NJ: Prentice-Hall, Inc. 592 pGoogle Scholar
Jurado-Expósito, M, López-Granados, F, Atenciano, S, García-Torres, L, González-Andújar, JL (2003) Discrimination of weed seedlings, wheat (Triticum aestivum) stubble and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop Prot 22:1177–1180 CrossRefGoogle Scholar
Koger, CH, Shaw, DR, Reddy, KN, Bruce, LM (2004) Detection of pitted morningglory (Ipomoea lacunosa) by hyperspectral remote sensing. I. Effects of tillage and cover crop residue. Weed Sci 52:222–229 CrossRefGoogle Scholar
Kokaly, RF, Asner, GP, Ollinger, SV, Martin, ME, Wessman, CA (2009) Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens Environment 113:S78–S91 CrossRefGoogle Scholar
Lamb, DW, Brown, RB (2001) Remote-sensing and mapping of weeds in crops. J Agric Engng Res 78:117–125 CrossRefGoogle Scholar
Lass, LW, Callihan, RH (1997) The effect of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol 11:248–256 CrossRefGoogle Scholar
Lausch, A, Salbach, C, Schmidt, A, Doktor, D, Merbach, I, Pause, M (2015) Deriving phenology of barley with imaging hyperspectral remote sensing. Ecol Model 295:123–135 CrossRefGoogle Scholar
López-Granados, F (2011) Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res 51:1–11 CrossRefGoogle Scholar
López-Granados, F, Peña-Barragán, JM, Jurado-Expósito, M, Francisco-Fernández, M, Cao, R, Alonso-Betanzos, A, Fontenla-Romero, O (2008) Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res 48:28–37 CrossRefGoogle Scholar
Lu, S (2013) Effects of leaf surface wax on leaf spectrum and hyperspectral vegetation indices. Int Geosci Remote Sens Symp 453–456 CrossRefGoogle Scholar
Mahlein, AK, Rumpf, T, Welke, P, Dehne, HW, Plümer, L, Steiner, U, Oerke, EC (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 128:21–30 CrossRefGoogle Scholar
Medlin, CR, Shaw, DR, Gerard, PD, LaMastus, FE (2000) Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393–398 CrossRefGoogle Scholar
Meier, U (2001) Growth Stages of Mono- and Dicotyledonous Plants. 2nd edn. Bonn, Germany: Federal Biological Research Centre for Agriculture and Forestry Google Scholar
Menges, RM, Nixon, PR, Richardson, AJ (1985) Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci 33:569–581 CrossRefGoogle Scholar
Ouyang, ZT, Gao, Y, Xie, X, Guo, HQ, Zhang, TT, Zhao, B (2013) Spectral discrimination of the invasive plant Spartina alterniflora at multiple phenological stages in a saltmarsh wetland. PLoS One 8:1–12 CrossRefGoogle Scholar
Peña-Barragán, JM, López-Granados, F, Jurado-Expósito, M, García-Torres, L (2006) Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage. Weed Res 46:10–21 CrossRefGoogle Scholar
Penuelas, J, Filella, I, Biel, C, Serrano, L, Save, R (1993) The reflectance at the 950-970 nm region as an indicator of plant water status. Int J Remote Sens 14:1887–1905 CrossRefGoogle Scholar
Ray, SS, Das, G, Singh, JP, Panigrahy, S (2006) Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. Int J Remote Sens 27:5373–5387 CrossRefGoogle Scholar
Rustioni, L, Bedgood, DR, Failla, O, Prenzler, PD, Robards, K (2012) Copigmentation and anti-copigmentation in grape extracts studied by spectrophotometry and post-column-reaction HPLC. Food Chem 132:2194–2201 CrossRefGoogle Scholar
Santos, MJ, Hestir, EL, Khanna, S, Ustin, SL (2012) Image spectroscopy and stable isotopes elucidate functional dissimilarity between native and nonnative plant species in the aquatic environment. New Phytol 193:683–695 CrossRefGoogle ScholarPubMed
Schmidt, KS, Skidmore, AK (2003) Spectral discrimination of vegetation types in a coastal wetland. Remote Sens Environ 85:92–108 CrossRefGoogle Scholar
Serbin, SP, Singh, A, McNeil, BE, Kingdon, CC, Townsend, PA (2014) Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol Appl 24:1651–1669 CrossRefGoogle Scholar
Thenkabail, PS, Enclona, EA, Ashton, MS, Van Der Meer, B, (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 80:213–224 Google Scholar
Thorp, KR, Tian, LF (2004) A review on remote sensing of weeds in agriculture. Precis Agri 5:447–508 Google Scholar
Ustin, SL, Roberts, DA, Gamon, JA, Asner, GP, Green, RO (2004). Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54: 523–534 CrossRefGoogle Scholar
Vaiphasa, C, Skidmore, AK, de Boer, WF, Vaiphasa, T (2007) A hyperspectral band selector for plant species discrimination. ISPRS J Photogramm 62:225–235 CrossRefGoogle Scholar
Webster, TM (2014) Weed survey-southern states: broadleaf crops sub-section. Proceedings, Southern Weed Sci Soc 66:1–5 Google Scholar
Webster, TM (2010) Weed survey-southern states: vegetable, fruit and nut crops sub-section. Proceedings, Southern Weed Sci Soc 63:246–257 Google Scholar
Wills, GD (1987) Herbicide action on purple and yellow nutsedge (Cyperus rotundus and C. esculentus). Weed Technol 1:2–9 CrossRefGoogle Scholar
Wills, GD, Hoagland, RE, Paul, RN (1980) Anatomy of yellow nutsedge (Cyperus esculentus). Weed Sci 28:432–437 CrossRefGoogle Scholar
Wu, J, Chavana-Bryant, C, Prohaska, N, Serbin, SP, Guan, K, Albert, LP, Yang, X, van Leeuwen, WJD, Garnello, AJ, Martins, G, Malhi, Y, Gerard, F, Oliviera, RC, Saleska, SR (2017) Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests. New Phytol 214:1033–1048 CrossRefGoogle ScholarPubMed
Xiao, Y, Zhao, W, Zhou, D, Gong, H (2014) Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales. IEEE Trans Geosci Remote Sens 52:4014–4024 CrossRefGoogle Scholar
Xue, L, Yang, L (2009) Deriving leaf chlorophyll content of green leafy vegetables from hyperspectral reflectance. ISPRS J Photogramm 64:97–106 CrossRefGoogle Scholar
Yeats, TH, Rose, JK (2013) The formation and function of plant cuticles. Plant Physiol 163:5–20.CrossRefGoogle ScholarPubMed
Youngentob, KN, Renzullo, LJ, Held, AA, Jia, X, Lindenmayer, DB, Foley, WJ (2012) Using imaging spectroscopy to estimate integrated measures of foliage nutritional quality. Methods Ecol Evol 3:416–426.CrossRefGoogle Scholar
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