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Part of the book series: Sustainability in Plant and Crop Protection ((SUPP,volume 13))

Abstract

Plant diseases contribute 10–16% losses in global harvests each year, costing an estimated US$ 220 billion. Abundant use of chemicals such as bactericides, fungicides, and nematicides to control plant diseases are causing adverse effects to many agroecosystems. Precision plant protection offers a non-destructive means of managing plant diseases based on the concept of spatio-temporal variability. Global Navigation Satellite System (GNSS) and Geographic Information System (GIS) allow for assessment of field heterogeneity due to disease problems and can enable site-specific intervention. Similarly, hyperspectral remote sensing is a cutting-edge spectral approach for plant diseases detection. The main aim of precision plant protection is to significantly reduce the injudicious use of chemical inputs and hence the adverse impact of chemicals to the environment. This chapter provides some insights into the deployment of site- and time-specific approaches to manage plant disease problems in a balanced and optimized manner.

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References

  • Ahmed, S. S. S. J., Santosh, W., Kumar, S., & Thanka Christlet, T. H. (2010). Neural network algorithm for the early detection of Parkinson’s disease from blood plasma by FTIR micro-spectroscopy. Vibrational Spectroscopy, 53, 181–188. https://doi.org/10.1016/j.vibspec.2010.01.019.

    Article  CAS  Google Scholar 

  • Ashourloo, D., Mobasheri, M., & Huete, A. (2014). Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing, 6, 4723–4740. https://doi.org/10.3390/rs6064723.

    Article  Google Scholar 

  • Ashourloo, D., Matkan, A. A., Huete, A., Aghighi, H., & Mobasheri, M. R. (2016). Developing an index for detection and identification of disease stages. IEEE Geosc Remote Sens Lett, 13, 851–855. https://doi.org/10.1109/LGRS.2016.2550529.

    Article  Google Scholar 

  • Ayala-Silva, T., & Beyl, C. A. (2005). Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Advances in Space Research, 35, 305–317.

    Article  CAS  Google Scholar 

  • Balabin, R. M., & Smirnov, S. V. (2011). Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Analytica Chimica Acta, 692, 63–72. https://doi.org/10.1016/j.aca.2011.03.006.

    Article  CAS  PubMed  Google Scholar 

  • Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S., et al. (2018). Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors, 18, 441. https://doi.org/10.3390/s18020441.

    Article  CAS  Google Scholar 

  • Beltrán-Peña, H., Soria-Ruiz, J., Téliz-Ortiz, D., Ochoa-Martínez, D. L., Nava-Díaz, C., & Ochoa-Ascencio, S. (2014). Molecular and satellite spectral imaging detection of Avocado Sunblotch Viroid (ASBVd). Revista Fitotecnia Mexicana, 37, 21–29.

    Google Scholar 

  • Benetoli da Silva, T. R., Reis de Sousa, A. C., & de Goés Maciel, C. D. (2012). Relationship between chlorophyll meter readings and total N in crambe leaves as affected by nitrogen topdressing. Industrial Crops and Products, 39, 135–138. https://doi.org/10.1016/j.indcrop.2012.02.008.

    Article  CAS  Google Scholar 

  • Blackburn, G. A. (2007). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58, 855–867. https://doi.org/10.1093/jxb/erl123.

    Article  CAS  PubMed  Google Scholar 

  • Blackmer, T. M., Schepers, J. S., & Meyer, G. E. (1995). Remote sensing to detect nitrogen deficiency in corn. In: Site-specific management for agricultural systems (pp. 505–512). https://doi.org/10.2134/1995.site-specificmanagement.c35.

    Google Scholar 

  • Bolton, D. K., & Friedl, M. A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173, 74–84. https://doi.org/10.1016/j.agrformet.2013.01.007.

    Article  Google Scholar 

  • Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156–172. https://doi.org/10.1016/S0034-4257(00)00197-8.

    Article  Google Scholar 

  • Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88, 677. https://doi.org/10.2307/2657068.

    Article  CAS  PubMed  Google Scholar 

  • Carter, G. A., & Miller, R. L. (1994). Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment, 50, 295–302. https://doi.org/10.1016/0034-4257(94)90079-5.

    Article  Google Scholar 

  • Castro, K. L., & Sanchez-Azofeifa, G. A. (2008). Changes in spectral properties, chlorophyll content and internal mesophyll structure of senescing Populus balsamifera and Populus tremuloides leaves. Sensors, 8, 51–69. https://doi.org/10.3390/s8010051.

    Article  CAS  PubMed  Google Scholar 

  • Chaerle, L. (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant & Cell Physiology, 45, 887–896. https://doi.org/10.1093/pcp/pch097.

    Article  CAS  Google Scholar 

  • Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment, 39, 239–247. https://doi.org/10.1016/0034-4257(92)90089-3.

    Article  Google Scholar 

  • Chen, X., Han, W., & Li, M. (2012). Spectroscopic determination of leaf water content using linear regression and an artificial neural network. African Journal of Biotechnology, 11, 2518–2527. https://doi.org/10.5897/AJB11.2733.

    Article  Google Scholar 

  • Clevers, J. G. P. W., Kooistra, L., & Salas, E. A. L. (2004). Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data. International Journal of Remote Sensing, 25, 3883–3895. https://doi.org/10.1080/01431160310001654473.

    Article  Google Scholar 

  • Damm, A., Guanter, L., Verhoef, W., Schläpfer, D., Garbari, S., & Schaepman, M. E. (2015). Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data. Remote Sensing of Environment, 156, 202–215. https://doi.org/10.1016/j.rse.2014.09.031.

    Article  Google Scholar 

  • Diener, T. O. (1999). Viroids and the nature of viroid diseases. In 100 Years of Virology (pp. 203–220). Vienna: Springer. https://doi.org/10.1007/978-3-7091-6425-9_15.

    Chapter  Google Scholar 

  • Gillespie, T. J., & Sentelhas, P. C. (2008). Agrometeorology and plant disease management: a happy marriage. Science in Agriculture. https://doi.org/10.1590/S0103-90162008000700012.

    Article  Google Scholar 

  • Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2017a). Use of reflectance spectroscopy as a tool for screening viroid-inoculated oil palm seedlings. Open Access Journal of Agricultural Research, 2, 1–5. https://doi.org/10.23880/OAJAR-16000145.

    Article  Google Scholar 

  • Golhani, K., Balasundram, S.K., Vadamalai, G., & Pradhan, B. (2017b). Red-edge indices to diagnose orange spotting disease of oil palm in Malaysia. In: 38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017.

    Google Scholar 

  • Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2018). A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5, 354–371. https://doi.org/10.1016/j.inpa.2018.05.002.

    Article  Google Scholar 

  • Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2019a). Selection of a spectral index for detection of orange spotting disease in oil palm (Elaeis guineensis Jacq.) using red edge and neural network techniques. Journal of the Indian Society of Remote Sensing, 47, 639–646. https://doi.org/10.1007/s12524-018-0926-4.

    Article  Google Scholar 

  • Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2019b). Estimating chlorophyll content at leaf scale in viroid- inoculated oil palm seedlings (Elaeis guineensis Jacq.) using reflectance spectra (400 nm–1050 nm). International Journal of Remote Sensing, 40, 647–7662. https://doi.org/10.1080/01431161.2019.1584930.

    Article  Google Scholar 

  • Granados-Ramírez, R., Reyna-Trujillo, T., Gómez-Rodríguez, G., & Soria-Ruiz, J. (2004). Analysis of NOAA-AVHRR-NDVI images for crops monitoring. International Journal of Remote Sensing, 25, 1615–1627. https://doi.org/10.1080/0143116031000156855.

    Article  Google Scholar 

  • Grinn-Gofroń, A., Nowosad, J., Bosiacka, B., Camacho, I., Pashley, C., Belmonte, J., et al. (2019). Airborne Alternaria and Cladosporium fungal spores in Europe: forecasting possibilities and relationships with meteorological parameters. Science of the Total Environment, 653, 938–946.

    Article  Google Scholar 

  • Grisham, M. P., Johnson, R. M., & Zimba, P. V. (2010). Detecting sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes. Journal of Virological Methods, 167, 140–145. https://doi.org/10.1016/j.jviromet.2010.03.024.

    Article  CAS  PubMed  Google Scholar 

  • Helland, I. S. (2001). Some theoretical aspects of partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 58, 97–107. https://doi.org/10.1016/S0169-7439(01)00154-X.

    Article  CAS  Google Scholar 

  • Huete, A. R. (1989). Soil influences in remotely sensed vegetation-canopy spectra. In G. Asrar (Ed.), Theory and applications of optical remote sensing (pp. 107–141). New York: Wiley-Interscience.

    Google Scholar 

  • Indahl, U. G. (2014). The geometry of PLS1 explained properly: 10 key notes on mathematical properties of and some alternative algorithmic approaches to PLS1 modelling. Journal of Chemometrics, 28, 168–180. https://doi.org/10.1002/cem.2589.

    Article  CAS  Google Scholar 

  • Iounousse, J., Er-Raki, S., El Motassadeq, A., & Chehouani, H. (2015). Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images. Applied Soft Computing, 30, 1–13. https://doi.org/10.1016/j.asoc.2015.01.037.

    Article  Google Scholar 

  • Jimenez, L. O., & Landgrebe, D. A. (1999). Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Transactions on Geoscience and Remote Sensing, 37, 2653–2667.

    Article  Google Scholar 

  • Jones, C. D., Jones, J. B., & Lee, W. S. (2010). Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture, 74, 329–335. https://doi.org/10.1016/j.compag.2010.09.008.

    Article  Google Scholar 

  • Knipling, E. B. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1, 155–159. https://doi.org/10.1016/S0034-4257(70)80021-9.

    Article  Google Scholar 

  • Köksal, E. S. (2011). Hyperspectral reflectance data processing through cluster and PCA for estimating irrigation and yield related indicators. Agricultural Water Management, 98, 1317–1328. https://doi.org/10.1016/j.agwat.2011.03.014.

    Article  Google Scholar 

  • Krafft, C., Steiner, G., Beleites, C., & Salzer, R. (2009). Disease recognition by infrared and raman spectroscopy. Journal of Biophotonics, 2, 13–28. https://doi.org/10.1002/jbio.200810024.

    Article  CAS  PubMed  Google Scholar 

  • Krezhova, D., Stoev, A., & Maneva, S. (2015). Detection of biotic stress caused by apple stem grooving virus in apple trees using hyperspectral reflectance analysis. Comptes Rendus de l’Académie Bulgare des Sciences, 68, 175–182.

    CAS  Google Scholar 

  • Kuska, M. T., & Mahlein, A. K. (2018). Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. The European Journal of Plant Pathology, 1–6.

    Google Scholar 

  • Lacis, A. A., & Hansen, J. (1974). A parameterization for the absorption of solar radiation in the earth’s atmosphere. Journal of the Atmospheric Sciences, 31, 118–133.

    Article  Google Scholar 

  • Lee, K. M., Herrman, T. J., Lingenfelser, J., & Jackson, D. S. (2005). Classification and prediction of maize hardness-associated properties using multivariate statistical analyses. Journal of Cereal Science, 41, 85–93. https://doi.org/10.1016/j.jcs.2004.09.006.

    Article  CAS  Google Scholar 

  • Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74, 2–33. https://doi.org/10.1016/j.compag.2010.08.005.

    Article  Google Scholar 

  • Lemon, S. M., Hamburg, M. A., Sparling, P. F., Choffnes, E. R., & Mack, A. (2007). Global infectious disease surveillance and detection : assessing the challenges — finding solutions, Workshops summary (p. 284). Washington, DC: National Academies Press. doi:978-0-309-11114-0.

    Google Scholar 

  • Li, L., Ustin, S. L., & Lay, M. (2005). Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico. Remote Sensing of Environment, 94, 1–16. https://doi.org/10.1016/j.rse.2004.08.010.

    Article  CAS  Google Scholar 

  • Li, L., Ren, T., Ma, Y., Wei, Q., Wang, S., Li, X., et al. (2016). Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters. Computers and Electronics in Agriculture, 126, 21–31. https://doi.org/10.1016/j.compag.2016.05.008.

    Article  Google Scholar 

  • Lingjaerde, O. C., & Christophersen, N. (2000). Shrinkage structure of partial least squares. Scandinavian Journal of Statistics, 27, 459–473. https://doi.org/10.1111/1467-9469.00201.

    Article  Google Scholar 

  • Mahlei, A.-K., Rumpf, T., Welke, P., Dehne, H.-W., Plümer, L., Steiner, U., & Oerke, E.-C. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30. https://doi.org/10.1016/j.rse.2012.09.019.

    Article  Google Scholar 

  • Mahlein, A. (2010). Detection, identification and quantification of fungal diseases of sugar beet leaves using imaging and non-imaging hyperspectral techniques. Bonn: University of Bonn.

    Google Scholar 

  • Mahlein, A. K. (2016). Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease, 100, 241–251. https://doi.org/10.1094/PDIS-03-15-0340-FE.

    Article  PubMed  Google Scholar 

  • Martens, H. (2001). Reliable and relevant modelling of real world data: a personal account of the development of PLS regression. Chemometrics and Intelligent Laboratory Systems, 58, 85–95.

    Article  CAS  Google Scholar 

  • Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., et al. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35, 1–25. https://doi.org/10.1007/s13593-014-0246-1.

    Article  Google Scholar 

  • Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., Solovchenko, A. E., & Pogosyan, S. I. (2003). Application of reflectance spectroscopy for analysis of higher plant pigments. Russian Journal of Plant Physiology, 50, 704–710.

    Article  CAS  Google Scholar 

  • Oerke, E. C., Gerhards, R., Menz, G., & Sikora, R. A. (2010). Precision crop protection-the challenge and use of heterogeneity (p. 441). Dordrecht: Springer.

    Book  Google Scholar 

  • Paydipati, R. (2004). Evaluation of clasifiers for automatic disease detection in citrus leaves machine vision. Master Thesis, Univ. FL. In USA.

    Google Scholar 

  • Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., & Upchurch, D. R. (2003). Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69, 647–664. https://doi.org/10.14358/PERS.69.6.647.

    Article  Google Scholar 

  • Riedell, W.E., Osborne, S.L., Hesler L.S., & Blackmer, T.M. (2000). Remote sensing of insect damage in wheat. In: Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, Minnesota, USA (16–19 July, 2000), 1–11.

    Google Scholar 

  • Rouse, J. W., Haas, R. H., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Greenbelt: NASA/GSFC type III final report.

    Google Scholar 

  • Rumpf, T., Mahlein, A., Dörschlag, D., & Plümer, L. (2009). Identification of combined vegetation indices for the early detection of plant diseases. In: C.M.U. Neale, and A. Maltese (Eds.). SPIE Europe Remote Sensing, 747217. doi:https://doi.org/10.1117/12.830525.

  • Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74, 91–99. https://doi.org/10.1016/j.compag.2010.06.009.

    Article  Google Scholar 

  • Schellber, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2008). Precision agriculture on grassland: applications, perspectives and constraints. European Journal of Agronomy, 29, 59–71.

    Article  Google Scholar 

  • Selvaraja, S., Balasundram, S. K., Vadamalai, G., & Husni, M. H. A. (2013). Site-specific disease management: a preliminary case with orange spotting in oil palm. In J. V. Stafford (Ed.), Precision Agriculture 13 (pp. 577–584). Dordrecht: Precision Agriculture ’13. Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-778-3_71.

    Chapter  Google Scholar 

  • Slonecker, E. (2011). Analysis of the effects of heavy metals on vegetation hyperspectral reflectance properties. In Hyperspectral remote sensing of vegetation (pp. 561–578). Boca Raton: CRC Press. https://doi.org/10.1201/b11222-33.

    Chapter  Google Scholar 

  • Steele, M., Gitelson, A. A., & Rundquist, D. (2008). Nondestructive estimation of leaf chlorophyll content in grapes. American Journal of Enology and Viticulture, 2, 299–305. https://doi.org/10.2307/2445170.

    Article  Google Scholar 

  • Sundberg, R. (1999). Multivariate calibration — direct and indirect regression methodology. Scandinavian Journal of Statistics, 26, 161–207.

    Article  Google Scholar 

  • Thanarajoo, S. S. (2014). Rapid detection, accumulation and translocation of Coconut cadang-cadang viroid variants in oil palm. Serdang: Universiti Putra Malaysia.

    Google Scholar 

  • Thomas, S., Wahabzada, M., Kuska, M. T., Rascher, U., & Mahlein, A. K. (2017). Observation of plant--pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Functional Plant Biology, 44, 23–34.

    Article  CAS  Google Scholar 

  • Thomas, S., Kuska, M. T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., et al. (2018). Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. The Journal of Plant Diseases and Protection, 125, 5–20.

    Article  Google Scholar 

  • Vadamalai, G., Hanold, D., Rezaian, M. A., & Randles, J. W. (2006). Variants of Coconut cadang-cadang viroid isolated from an African oil palm (Elaeis guineensis Jacq.) in Malaysia. Archives of Virology, 151, 1447–1456. https://doi.org/10.1007/s00705-005-0710-y.

    Article  CAS  PubMed  Google Scholar 

  • Van Maanen, A., & Xu, X. M. (2003). Modelling plant disease epidemics. In Epidemiology of Mycotoxin Producing Fungi (pp. 669–682). Dordrecht: Springer. https://doi.org/10.1007/978-94-017-1452-5_2.

    Chapter  Google Scholar 

  • Wasukar, A. R. (2014). Artificial neural network – an important asset for future computing. International Journal of Emerging Trends in Science, 1, 28–34.

    Google Scholar 

  • West, J. S., Bravo, C., Oberti, R., Moshou, D., Ramon, H., & McCartney, H. A. (2010). Detection of fungal diseases optically and pathogen inoculum by air sampling. In Precision crop protection - the challenge and use of heterogeneity (pp. 135–149). Dordrecht: Springer. https://doi.org/10.1007/978-90-481-9277-9_9.

    Chapter  Google Scholar 

  • Wold, H. (1975). Soft modeling by latent variables: the nonlinear iterative partial least squares (NIPALS) approach. Journal of Applied Probability, 12(S1), 117–142.

    Article  Google Scholar 

  • Wu, Y. H., Cheong, L. C., Meon, S., Lau, W. H., Kong, L. L., Joseph, H., & Vadamalai, G. (2013). Characterization of Coconut cadang-cadang viroid variants from oil palm affected by orange spotting disease in Malaysia. Archives of Virology, 158, 1407–1410. https://doi.org/10.1007/s00705-013-1624-8.

    Article  CAS  PubMed  Google Scholar 

  • Yang, F., Li, J., Gan, X., Qian, Y., Wu, X., & Yang, Q. (2010). Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Computers and Electronics in Agriculture, 70, 52–59. https://doi.org/10.1016/j.compag.2009.08.010.

    Article  Google Scholar 

  • Zhao, X. (2012). Advances and technology in infrared spectroscopy. Journal of Anqing Teachers College, 18, 94–97.

    Google Scholar 

  • Zhu, H., Cen, H., Zhang, C., & He, Y. (2016). Early detection and classification of tobacco leaves inoculated with tobacco mosaic virus based on hyperspectral imaging technique. ASABE Annual International Meeting, 1. https://doi.org/10.13031/aim.20162460422.

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Balasundram, S.K., Golhani, K., Shamshiri, R.R., Vadamalai, G. (2020). Precision Agriculture Technologies for Management of Plant Diseases. In: Ul Haq, I., Ijaz, S. (eds) Plant Disease Management Strategies for Sustainable Agriculture through Traditional and Modern Approaches. Sustainability in Plant and Crop Protection, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-35955-3_13

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