Dichotomous classification tree; Fusarium Head Blight; Near Infrared Hyperspectral Imaging; PLS-DA; Winter wheat; Analytical Chemistry; Spectroscopy
Abstract :
[en] The scope of this article is to present a laboratory-based method for the assessment of Fusarium Head Blight (FHB) on ears of winter wheat using Near Infrared Hyperspectral Imaging (NIR-HSI) in the spectral range 900–1700 nm. Inoculated and non-inoculated winter wheat ears were collected from a trial field in Belgium and analysed in the laboratory. Using a dichotomous classification tree framework, Partial Least Squares Discriminant Analysis (PLS-DA) models were developed and applied at pixel level to discriminate the different parts of the ears, namely the awns, the stem and the healthy and infected parts of the ears. This study proposes the use of spectral information at pixel level instead of averaging the spectral signature of the ear. This approach allows using the spatial information of the data in order to provide a quantitative assessment of the severity of the infection on the ear. The results presented in this work confirm that wavelengths related to water and nitrogen are important for the detection of FHB. The validation of the models indicates a good performance for the detection of FHB infection at pixel level with a sensitivity of 96 % and a specificity of 100 %. Furthermore, the validation of the method at ear level shows good performance for the detection of FHB-infected ears with a sensitivity of 99.4 % and a specificity of 91.9 %. The literature reports similar performances, but using equipment with a broader spectral range (400–2500 nm). The present study thus indicates that good discrimination performance can be achieved using a much narrower spectral range. In addition, the obtained results suggest that the method can discriminate three classes of FHB severity, which enables a semi-quantitative assessment of the FHB infection.
Disciplines :
Agriculture & agronomy
Author, co-author :
Vincke, Damien ; Université de Liège - ULiège > TERRA Research Centre
Eylenbosch, Damien; Walloon Agricultural Research Centre (CRA-W), Productions in Agriculture Department, Crop Production Unit, Gembloux, Belgium
Jacquemin, Guillaume; Walloon Agricultural Research Centre (CRA-W), Life Sciences Department, Biodiversity and Plant and Forest Improvement Unit, Gembloux, Belgium
Chandelier, Anne; Walloon Agricultural Research Centre (CRA-W), Life Sciences Department, Crops and Forest Health Unit, Gembloux, Belgium
Fernández Pierna, Juan Antonio; Walloon Agricultural Research Centre (CRA-W), Knowledge and Valorization of Agricultural Products Department, Quality and Authentication of Agricultural Products Unit, Gembloux, Belgium
Stevens, François; Walloon Agricultural Research Centre (CRA-W), Knowledge and Valorization of Agricultural Products Department, Quality and Authentication of Agricultural Products Unit, Gembloux, Belgium
Baeten, Vincent; Walloon Agricultural Research Centre (CRA-W), Knowledge and Valorization of Agricultural Products Department, Quality and Authentication of Agricultural Products Unit, Gembloux, Belgium
Mercatoris, Benoît ; Université de Liège - ULiège > TERRA Research Centre > Biosystems Dynamics and Exchanges (BIODYNE)
Vermeulen, Philippe; Walloon Agricultural Research Centre (CRA-W), Knowledge and Valorization of Agricultural Products Department, Quality and Authentication of Agricultural Products Unit, Gembloux, Belgium
Language :
English
Title :
Near infrared hyperspectral imaging method to assess Fusarium Head Blight infection on winter wheat ears
SPW - Service Public de Wallonie EU - European Union SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement EC - European Commission
Funding text :
This work was performed by the PhenWheat project funded by the Walloon Region, Service Public de Wallonie (SPW), Direction Générale Agriculture Ressources Naturelles Environnement (DGO3 - DGARNE), Direction Recherche et Développement, project number D31-1385/S1 . This study has also contributed as preliminary work for the INVITE project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 817970 . The authors are grateful to the team from the Crop Production Unit of the CRA-W for managing the field trials. They are also grateful to Lisa Plasman, Benoît Scaut, Nicaise Kayoka Mukendi and the technical staff of all CRA-W research teams involved.This work was performed by the PhenWheat project funded by the Walloon Region, Service Public de Wallonie (SPW), Direction Générale Agriculture Ressources Naturelles Environnement (DGO3 - DGARNE), Direction Recherche et Développement, project number D31-1385/S1. This study has also contributed as preliminary work for the INVITE project funded by the European Union's Horizon 2020 research and innovation programme under grant agreement N° 817970. The authors are grateful to the team from the Crop Production Unit of the CRA-W for managing the field trials. They are also grateful to Lisa Plasman, Benoît Scaut, Nicaise Kayoka Mukendi and the technical staff of all CRA-W research teams involved.
Gaikpa, D.S., Lieberherr, B., Maurer, H.P., Longin, C.F.H., Miedaner, T., Buerstmayr, H., Comparison of rye, triticale, durum wheat and bread wheat genotypes for Fusarium head blight resistance and deoxynivalenol contamination. Plant Breed. 139:2 (2020), 251–262, 10.1111/pbr.12779.
Chandelier, A., Nimal, C., André, F., Planchon, V., Oger, R., Fusarium species and DON contamination associated with head blight in winter wheat over a 7-year period (2003–2009) in Belgium. Eur. J. Plant Pathol. 130 (2011), 403–414, 10.1007/s10658-011-9762-x.
Osborne, L.E., Stein, J.M., Epidemiology of Fusarium head blight on small-grain cereals. Int. J. Food Microbiol. 119 (2007), 103–108, 10.1016/j.ijfoodmicro.2007.07.032.
Kang, Z., Buchenauer, H., Cytology and ultrastructure of the infection of wheat spikes by Fusarium culmorum. Mycol. Res. 104 (2000), 1083–1093, 10.1017/S0953756200002495.
Pirgozliev, S.R., Edwards, S.G., Hare, M.C., Jenkinson, P., Strategies for the control of Fusarium head blight in cereals. Eur. J. Plant Pathol. 109 (2003), 731–742, 10.1023/A:1026034509247.
Nightingale, M.J., Marchylo, B.A., Clear, R.M., Dexter, J.E., Preston, K.R., Fusarium head blight: effect of fungal proteases on wheat storage proteins. Cereal Chem. 76 (1999), 150–158, 10.1094/CCHEM.1999.76.1.150.
Boyacioǧlu, D., Hettiarachchy, N.S., Changes in some biochemical components of wheat grain that was infected with Fusarium graminearum. J. Cereal Sci. 21 (1995), 57–62, 10.1016/S0733-5210(95)80008-5.
Bauriegel, E., Herppich, W., Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat. Agriculture 4 (2014), 32–57, 10.3390/agriculture4010032.
Mahlein, A.-K., Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100 (2016), 1–11, 10.1007/s13398-014-0173-7.2.
Sankaran, S., Mishra, A., Ehsani, R., Davis, C., A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72 (2010), 1–13, 10.1016/j.compag.2010.02.007.
Bebronne, R., Carlier, A., Meurs, R., Leemans, V., Vermeulen, P., Dumont, B., Mercatoris, B., In-field proximal sensing of septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery. Biosyst. Eng. 197 (2020), 257–269, 10.1016/j.biosystemseng.2020.06.011.
Zhou, B., Elazab, A., Bort, J., Vergara, O., Serret, M.D., Araus, J.L., Low-cost assessment of wheat resistance to yellow rust through conventional RGB images. Comput. Electron. Agric. 116 (2015), 20–29, 10.1016/j.compag.2015.05.017.
Qiu, R., Yang, C.e., Moghimi, A., Zhang, M., Steffenson, B.J., Hirsch, C.D., Detection of Fusarium Head Blight in wheat using a deep neural network and color imaging. Remote Sens., 11(22), 2019, 2658, 10.3390/rs11222658.
E.-C. Oerke, G. Menz, R. Gerhards, R.A. Sikora, eds., Precision Crop Protection - the Challenge and Use of Heterogeneity, Springer Dordrecht Heidelberg London New York, 2010. doi: 10.1007/978-90-481-9277-9.
Al Masri, A., Hau, B., Dehne, H.W., Mahlein, A.K., Oerke, E.C., Impact of primary infection site of Fusarium species on head blight development in wheat ears evaluated by IR-thermography. Eur. J. Plant Pathol. 147 (2017), 855–868, 10.1007/s10658-016-1051-2.
Maxwell, K., Johnson, G.N., Chlorophyll-fluorescence - a practical guide. J. Exp. Bot. 51 (2000), 659–668, 10.1093/jexbot/51.345.659.
Mahlein, A., Alisaac, E., Al Masri, A., Behmann, J., Dehne, H., Oerke, E., Monitoring fusarium head blight of wheat on spikelet scale. Sensors (Switzerland) 19 (2019), 1–18.
Dale, L.M., Thewis, A., Boudry, C., Rotar, I., Dardenne, P., Baeten, V., Pierna, J.A.F., Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Appl. Spectrosc. Rev. 48 (2013), 142–159, 10.1080/05704928.2012.705800.
Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., Mahlein, A.K., Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J. Plant Dis. Prot. 125 (2018), 5–20, 10.1007/s41348-017-0124-6.
Whetton, R.L., Waine, T.W., Mouazen, A.M., Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: part 2: on-line field measurement. Biosyst. Eng. 167 (2018), 144–158, 10.1016/j.biosystemseng.2018.01.004.
Whetton, R.L., Hassall, K.L., Waine, T.W., Mouazen, A.M., Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: part 1: laboratory study. Biosyst. Eng. 166 (2018), 101–115, 10.1016/j.biosystemseng.2017.11.008.
Bohnenkamp, D., Kuska, M.T., Mahlein, A.K., Behmann, J., Hyperspectral signal decomposition and symptom detection of wheat rust disease at the leaf scale using pure fungal spore spectra as reference. Plant Pathol. 68 (2019), 1188–1195, 10.1111/ppa.13020.
Kuska, M.T., Brugger, A., Thomas, S., Wahabzada, M., Kersting, K., Oerke, E.C., Steiner, U., Mahlein, A.K., Spectral patterns reveal early resistance reactions of barley against Blumeria graminis f. sp. hordei. Phytopathology 107 (2017), 1388–1398, 10.1094/PHYTO-04-17-0128-R.
Vermeulen, P.h., Pierna, J.A.F., Egmond, H.P.V., Dardenne, P., Baeten, V., Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging. Food Addit. Contam. Part A Chem. Anal. Control. Expo. & Risk Assess. 29:2 (2012), 232–240.
J.A. Fernández Pierna, P. Vermeulen, D. Eylenbosch, J. Burger, B. Bodson, P. Dardenne, V. Baeten, Chemometrics in NIR Hyperspectral Imaging: Theory and Applications in the Agricultural Crops and Products Sector, in: S. Brown, R. Tauler, B. Walczak (Eds.), Chemom. Chem. Biochem. Data Anal., Elsevier B.V., 2020: pp. 361–379.
Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A.A., Bonifazi, G., Fanelli, C., Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int. J. Food Microbiol. 144 (2010), 64–71, 10.1016/j.ijfoodmicro.2010.08.001.
Williams, P., Manley, M., Fox, G., Geladi, P., Indirect detection of Fusarium verticillioides in maize [Zea mays L kernels by near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 18 (2010), 49–58, 10.1255/jnirs.858.
Sendin, K., Manley, M., Baeten, V., Fernández Pierna, J.A., Williams, P.J., Near infrared hyperspectral imaging for white maize classification according to grading regulations. Food Anal. Methods 12 (2019), 1612–1624, 10.1007/s12161-019-01464-0.
Delwiche, S.R., Kim, M.S., Dong, Y., Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sens. Instrum. Food Qual. Saf. 5 (2011), 63–71, 10.1007/s11694-011-9112-x.
Barbedo, J.G.A., Tibola, C.S., Fernandes, J.M.C., Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst. Eng. 131 (2015), 65–76, 10.1016/j.biosystemseng.2015.01.003.
Shahin, M.A., Symons, S.J., Detection of fusarium damage in Canadian wheat using visible/near-infrared hyperspectral imaging. Sens. Instrum. Food Qual. Saf. 6 (2012), 3–11, 10.1007/s11694-012-9126-z.
Vincke, D., Mercatoris, B., Eylenbosch, D., Baeten, V., Vermeulen, P., Assessment of kernel presence in winter wheat ears at spikelet scale using near-infrared hyperspectral imaging. J. Cereal Sci., 106, 2022, 103497, 10.1016/j.jcs.2022.103497.
Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., Herppich, W.B., Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 75 (2011), 304–312, 10.1016/j.compag.2010.12.006.
Menesatti, P., Antonucci, F., Pallottino, F., Giorgi, S., Matere, A., Nocente, F., Pasquini, M., D'Egidio, M.G., Costa, C., Laboratory vs. in-field spectral proximal sensing for early detection of Fusarium head blight infection in durum wheat. Biosyst. Eng. 114 (2013), 289–293, 10.1016/j.biosystemseng.2013.01.004.
Jin, X., Jie, L., Wang, S., Qi, H., Li, S., Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens., 10(3), 2018, 395.
Alisaac, E., Behmann, J., Kuska, M.T., Dehne, H.-W., Mahlein, A.-K., Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species. Eur. J. Plant Pathol. 152 (2018), 869–884, 10.1007/s10658-018-1505-9.
R.W. Stack, M.P. Mcmullen, A Visual Scale to Estimate Severity of Fusarium Head Blight in Wheat, North Dakota Univ. Ext. Serv. PP-1095 (2011).
Eylenbosch, D., Bodson, B., Baeten, V., Fernández Pierna, J.A., NIR hyperspectral imaging spectroscopy and chemometrics for the discrimination of roots and crop residues extracted from soil samples. J. Chemom. 32 (2018), 1–11, 10.1002/cem.2982.
Fernández Pierna, J.A., Vermeulen, P., Amand, O., Tossens, A., Dardenne, P., Baeten, V., NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemom. Intell. Lab. Syst. 117 (2012), 233–239, 10.1016/j.chemolab.2012.02.004.
B.M. Wise, N.B. Gallagher, R. Bro, J.M. Shaver, W. Windig, R.S. Koch, Chemometrics Tutorial for PLS_Toolbox and Solo, Eigenvector Research, Inc., 3905 West Eaglerock Drive, Wenatchee, WA 98801 USA, 2006.
J. Workman, L. Weyer, Practical Guide to Interpretive Near-Infrared Spectroscopy, CRC Press, Taylor & Francis Group, Boca Raton, FL 33487-2742, USA, 2008. doi: 10.1080/0034408080030105.
B.G. Osborne, T. Fearn, Near Infrared Spectroscopy in Food Analysis, Longman Scientific & Technical, Longman Group UK Limited, Essex CM20 2JE, England, 1986.
Salgó, A., Gergely, S., Analysis of wheat grain development using NIR spectroscopy. J. Cereal Sci. 56 (2012), 31–38, 10.1016/j.jcs.2012.04.011.
Siuda, R., Grabowski, A., Lenc, L., Ralcewicz, M., Spychaj-Fabisiak, E., Influence of the degree of fusariosis on technological traits of wheat grain. Int. J. Food Sci. Technol. 45 (2010), 2596–2604, 10.1111/j.1365-2621.2010.02438.x.
Chong, I.G., Jun, C.H., Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78 (2005), 103–112, 10.1016/j.chemolab.2004.12.011.
