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Applications of Sensing for Disease Detection

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Sensing Approaches for Precision Agriculture

Part of the book series: Progress in Precision Agriculture ((PRPRA))

Abstract

The potential loss of world crop production from the effect of pests, including weeds, animal pests, pathogens and viruses has been quantified as around 40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows some case studies on the use of remote sensing technology in some of the world’s major crops; namely cotton, avocado and grapevines. In these case studies, RS has been applied to detect disease caused by fungi using different acquisition platforms at different scales, such as leaf-level hyperspectral data and canopy-level remote imagery taken from satellites, manned airplanes or helicopter, and UAVs. The results proved that remote sensing is useful, efficient and effective for identifying cotton root rot zones in cotton fields, laurel wilt-infested avocado trees and esca-affected vines, which would allow farmers to optimize inputs and field operations, resulting in reduced yield losses and increased profits.

Ana Isabel de Castro Megías: Introduction and Case Study 13.2

J. Alex Thomasson, Tianyi Wang, Curtis Cribben, Thomas Isakeit, Xiwei Wang and Robert L. Nichols: Case Study 13.1

Reza Ehsani and José Manuel Peña: Case Study 13.2

Claudia Pérez-Roncal, Ainara López-Maestresalas, Chenghai Yang, Carmen Jarén, Diana Marin, Jorge Urrestarazu, Carlos Lopez-Molina, Gonzaga Santesteban and Silvia Arazuri: Case Study 13.3

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References

  • Abdulridha J, Ampatzidis Y, Ehsani R, de Castro A (2018) Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Comput Electron Agric 155:203–211

    Article  Google Scholar 

  • Barker M, Rayens W (2003) Partial least squares for discrimination. J Chemometr 17(3):166–173

    Article  CAS  Google Scholar 

  • Bertsch C, Larignon P, Farine S et al (2009) The spread of grapevine trunk disease. Science 324(5928):721

    Article  CAS  PubMed  Google Scholar 

  • Büning-Pfaue H (2003) Analysis of water in food by near infrared spectroscopy. Food Chem 82(1):107–115

    Article  Google Scholar 

  • Campbell JB (2002) Introduction to remote sensing, 3rd edn. Guilford Press, New York

    Google Scholar 

  • Chappelle EW, Kim MS, McMurtrey JE (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 Sens Environ 39(3):239–247

    Article  Google Scholar 

  • De Castro AI, Jurado-Expósito M, Gómez-Casero MT et al (2012) Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops. Sci World J 630390

    Google Scholar 

  • De Castro AI, Ehsani R, Ploetz R et al (2015a) Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sens Environ 171:33–44

    Article  Google Scholar 

  • De Castro AI, Ehsani R, Ploetz RC et al (2015b) Detection of laurel wilt disease in avocado using low altitude aerial imaging. PLoS One 10(4):e0124642

    Article  PubMed  PubMed Central  Google Scholar 

  • Di Gennaro SF, Battiston E, Di Marco S et al (2016) Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathol Mediterr 55(2):262–275

    Google Scholar 

  • Di Marco S, Osti F, Calzarano F et al (2011) Effects of grapevine applications of fosetyl-aluminium formulations for downy mildew control on “esca” and associated fungi. Phytopathol Mediterr 50(4):S285–S299

    Google Scholar 

  • Drake DR, Minzenmayer RR, Multer WL et al (2013) Evaluation of farmer applications of Topguard (flutriafol) for cotton root rot control in the first Section 18 exemption year. In: Proccedings of the Beltwide Cotton Conf. National Cotton Council of America, Cordova

    Google Scholar 

  • ElMasry G, Sun DW (2010) Principles of hyperspectral imaging technology. In: Sun DW (ed) Hyperspectral imaging for food quality analysis and control. Academic Press, San Diego, pp 3–43

    Chapter  Google Scholar 

  • Evans EA, Bernal Lozano I (2015) Sample avocado production costs and profitability analysis for Florida. Electronic data information source (EDIS) FE837. Gainesville, FL: Food and Resource Economics Department, University of Florida. https://edis.ifas.ufl.edu/dosearch.html. Accessed 23 March 2018

  • Fischer M (2002) A new wood-decaying basidiomycete species associated with esca of grapevine: Fomitiporia mediterranea (Hymenochaetales). Mycol Prog 1(3):315–324

    Article  Google Scholar 

  • García-Jiménez J, Raposo R, Armengol J (2010) Enfermedades fúngicas de la madera de la vid. In: Jiménez-Díaz RM, Montesinos Seguí E (eds) Enfermedades de las plantas causadas por hongos y oomicetos: naturaleza y control integrado. SEF-Phytoma España, pp 161–189

    Google Scholar 

  • Geladi P, Burger J, Lestander T (2004) Hyperspectral imaging: calibration problems and solutions. Chemom Intell Lab Syst 72(2):209–217

    Article  CAS  Google Scholar 

  • Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148(3–4):494–500

    Article  CAS  Google Scholar 

  • Gitelson AA, Kaufman YJ, Stark R et al (2002) Novel algorithms for remote estimation of vegetation fraction. Remote Sens EnvironRemote Sens Environ 80(1):76–87

    Article  Google Scholar 

  • Graniti A, Surico G, Mugnai L (2000) Esca of grapevine: a disease complex or a complex of diseases? Phytopathol Mediterr 39(1):16–20

    Google Scholar 

  • Han J, Kamber M, Pei J (2012) Data mining. Concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, Waltham

    Google Scholar 

  • Hanula JL, Mayfield AE III, Fraedrich SW et al (2008) Biology and host associations of redbay ambrosia beetle (Coleoptera: Curculionidae: Scolytinae), exotic vector of laurel wilt killing redbay trees in the southeastern United States. J Econ EntomolJ Econ Entomol 101(4):1276–1286

    Article  Google Scholar 

  • Hofstetter V, Buyck B, Croll D et al (2012) What if esca disease of grapevine were not a fungal disease? Fungal Divers 54:51–67

    Article  Google Scholar 

  • Howden SM, Soussana JF, Tubiello FN et al (2007) Adapting agriculture to climate change. Procceding of the Natl Acad Sci USA 104(50):19691–19696

    Article  CAS  Google Scholar 

  • Isakeit T, Minzenmayer R, Sansone C (2009) Flutriafol control of cotton root rot caused by Phymatotrichopsis omnivora. In Procceding of the Beltwide Cotton Conf. 130–133. Cordova, Tenn.: National Cotton Council of America

    Google Scholar 

  • Kaufman YJ, Remer LA (1994) Detection of forests using mid-IR reflectance: an application for aerosol studies. IEEE Trans Geosci Remote Sens 32(3):672–683

    Article  Google Scholar 

  • Laveau C, Letouze A, Louvet G et al (2009) Differential aggressiveness of fungi implicated in esca and associated diseases of grapevine in France. Phytopathol Mediterr 48(1):32–46

    Google Scholar 

  • Levasseur-Garcia C, Malaurie H, Mailhac N (2016) An infrared diagnostic system to detect causal agents of grapevine trunk diseases. J Microbiol Methods 131:1–6

    Article  CAS  PubMed  Google Scholar 

  • Lopez-Molina C, Ayala-Martinez D, Lopez-Maestresalas A et al (2017) Baddeley’s Delta metric for local contrast computation in hyperspectral imagery. Prog Artif Intell 6:121–132

    Article  Google Scholar 

  • Lu JZ, Ehsani R, Shi YY et al (2017) Field detection of anthracnose crown rot in strawberry using spectroscopy technology. Comput Electron Agric 135:289–299

    Article  Google Scholar 

  • Lu JZ, Ehsani R, Shi YY et al (2018) Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Sci Rep 8:2793

    Article  PubMed  PubMed Central  Google Scholar 

  • Lyda SD (1978) Ecology of Phymatotrichum omnivorum. Annu Rev Phytopathol 16:193–209

    Article  Google Scholar 

  • Mahlein AK (2016) Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100(2):241–251

    Article  PubMed  Google Scholar 

  • Mahlein AK, Steiner U, Hillnhütter C et al (2012) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8:3

    Article  PubMed  PubMed Central  Google Scholar 

  • Mendel J, Burns C, Kallifatidis B et al (2018) Agri-dogs: using canines for earlier detection of laurel wilt disease affecting avocado trees in South Florida. HortTechnology 28(2):109–116

    Article  CAS  Google Scholar 

  • Mobaraki N, Amigo JM (2018) HYPER-Tools. A graphical user-friendly interface for hyperspectral image analysis. Chemom Intel Lab Syst 172:174–187

    Article  CAS  Google Scholar 

  • Mugnai L, Graniti A, Surico G (1999) Esca (black measles) and brown wood-streaking: two old and elusive diseases of grapevines. Plant Dis 83(5):404–418

    Article  CAS  PubMed  Google Scholar 

  • Mutka AM, Bart RS (2015) Image-based phenotyping of plant disease symptoms. Front Plant Sci 5:734

    Article  PubMed  PubMed Central  Google Scholar 

  • NCC (2013) Disease Database (2011). National Cotton Council of America, Cordova. Available at: http://www.cotton.org/tech/pest/index.cfm. Accessed 20 February 2013

    Google Scholar 

  • Oerke EC, Dehne HW (2004) Safeguarding production - losses in major crops and the role of crop protection. Crop Prot 23:275–285

    Article  Google Scholar 

  • Osborne BG, Fearn T, Hindle PH (1993) Practical NIR spectroscopy with applications in food and beverage analysis. Longman Scientific and Technical, Harlow

    Google Scholar 

  • Pammel LH (1888) Root rot of cotton, or “cotton blight”. Texas Agric Exp Station Ann Report 1:50–65

    Google Scholar 

  • Ploetz RC, Harrington T, Hulcr J et al (2011) Recovery plan for laurel wilt of avocado (caused by Raffaelea lauricola). National Plant Disease Recovery System. Homeland Security Presidential Directive Number 9 (HSPD-9). http://www.ars.usda.gov/research/docs.htm?docid=14271 accessed 20 April 2013

  • Ploetz RC, Konkol JL, Narvaez T et al (2017a) Presence and prevalence of Raffaelea lauricola, cause of laurel wilt, in different species of ambrosia beetle in Florida USA. J Econ Entomol 110(2):347–354

    PubMed  Google Scholar 

  • Ploetz RC, Kendra PE, Choudhury RA et al (2017b) Laurel wilt in natural and agricultural ecosystems: understanding the drivers and scales of complex pathosystems. Forest 8(2):48

    Google Scholar 

  • Rançon F, Bombrun L, Keresztes B et al (2019) Comparison of SIFT encoded and deep learning features for the classification and detection of esca disease in Bordeaux vineyards. Remote Sens (Basel) 11(1):1–26

    Google Scholar 

  • Rodríguez-Pérez JR, Riaño D, Carlisle E et al (2007) Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. Am J Enol Vitic 58(3):302–317

    Article  Google Scholar 

  • Smith HE, Elliot FC, Bird LS (1962) Root rot losses of cotton can be reduced. Pub. No. MP361. Texas A&M Agricultural Extension Service, College Station

    Google Scholar 

  • Statista (2018) Import value of avocados worldwide in 2017, by leading country (in million U.S. dollars). Source: UN Comtrade; 2017. https://www.statista.com/statistics/938571/major-importers-avocado-import-value/ Accessed 06 November 2018

  • Surico G, Mugnai L, Marchi G (2008) The esca disease complex. In: Ciancio A, Mukerji KG (eds) Integrated management of diseases caused by fungi, phytoplasma and bacteria. Integrated management of plant pests and diseases, vol 3. Springer, Dordrecht, pp 119–136

    Google Scholar 

  • Thomasson JA, Wang T, Wang X et al (2018) Disease detection and mitigation in a cotton crop with UAV remote sensing. In Proccedings of the autonomous air and ground sensing Systems for Agricultural Optimization and Phenotyping. Bellingham, Wash.: SPIE

    Google Scholar 

  • Valtaud C, Larignon P, Roblin G et al (2009) Developmental and ultrastructural features of Phaeomoniella chlamydospora and Phaeoacremonium aleophilum in relation to xylem degradation in esca disease of the grapevine. J Plant Pathol 91(1):37–51

    CAS  Google Scholar 

  • Wang T, Thomasson JA (2019) Plant-by-plant level classifications of cotton root rot by UAV remote sensing. In Proccedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. Bellingham, Wash.: SPIE

    Google Scholar 

  • Yang C (2012) A high-resolution airborne four-camera imaging system for agricultural remote sensing. Comput Electron Agric 88(1):13–24

    Article  Google Scholar 

  • Yang C, Odvody GN, Fernandez CJ et al (2014) Monitoring cotton root rot progression within a growing season using airborne multispectral imagery. J Cotton Sci 18(1):85–93

    Google Scholar 

  • Yang C, Odvody GN, Thomasson JA et al (2016) Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery. Comput Electron Agric 123(1):154–162

    Article  Google Scholar 

  • Yang C, Odvody GN, Thomasson JA et al (2018) Site-specific management of cotton root rot using airborne and high-resolution satellite imagery and variable-rate technology. Trans ASABE 61(3):849–858

    Article  Google Scholar 

  • Zhang J, Huang Y, Pu R et al (2019) Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agric 165:104943

    Article  Google Scholar 

Download references

Acknowledgments

The research presented here was partly financed by the USDA Specialty Block Grant No. 019730 (Florida Department of Agriculture and Consumer Services, USA), AGL2017-83325-C4-1R and AGL2017-83325-C4-4R Projects (Spanish Ministry of Science, Innovation and Universities and AEI/EU-FEDER funds), Public University of Navarre postgraduate scholarships (FPI-UPNA-2017, Res.654/2017), Project DECIVID (Res.104E/2017, Department of Economic Development of the Navarre Government-Spain), and the Spanish MINECO project TIN2016-77356-P (AEI, Feder/UE). The authors thank Don Pyba and Sherrie Buchanon for their helpful assistance, as well as the Viticulture and Enology Station of Navarra-Spain (EVENA) for providing the samples and for their valuable support.

Author information

Authors and Affiliations

  1. National Institute for Agricultural and Food Research and Technology (INIA-CSIC), Madrid, Spain

    Ana Isabel de Castro Megías

  2. School of Agricultural Engineering and Biosciences, Public University of Navarre (UPNA), Pamplona, Spain

    Claudia Pérez-Roncal, Ainara López-Maestresalas, Carmen Jarén, Diana Marin, Jorge Urrestarazu, Carlos Lopez-Molina, Gonzaga Santesteban & Silvia Arazuri

  3. Mississippi State University, Mississippi State, MS, USA

    J. Alex Thomasson

  4. University of California, Merced, CA, USA

    Reza Ehsani

  5. USDA Agricultural Research Service, College Station, TX, USA

    Chenghai Yang

  6. Texas A&M AgriLife Research, Dallas, TX, USA

    Tianyi Wang

  7. Bridgestone Americas, Inc., Burlington, NC, USA

    Curtis Cribben

  8. Texas A&M University, College Station, TX, USA

    Thomas Isakeit

  9. Nanjing Forestry University, Xuanwu, China

    Xiwei Wang

  10. Cotton Incorporated, Cary, NC, USA

    Robert L. Nichols

  11. Institute of Agricultural Sciences (ICA-CSIC), Madrid, Spain

    José Manuel Peña

Authors
  1. Ana Isabel de Castro Megías
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  2. Claudia Pérez-Roncal
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  3. J. Alex Thomasson
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  4. Reza Ehsani
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  5. Ainara López-Maestresalas
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  6. Chenghai Yang
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  7. Carmen Jarén
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  8. Tianyi Wang
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  9. Curtis Cribben
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  10. Diana Marin
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  11. Thomas Isakeit
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  12. Jorge Urrestarazu
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  13. Carlos Lopez-Molina
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  14. Xiwei Wang
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  15. Robert L. Nichols
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  16. Gonzaga Santesteban
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  17. Silvia Arazuri
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  18. José Manuel Peña
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Corresponding author

Correspondence to Ana Isabel de Castro Megías .

Editor information

Editors and Affiliations

  1. Department of Geography, Brigham Young University, Provo, UT, USA

    Ruth Kerry

  2. Department of Agricultural and Forest Engineering Research Group on AgroICT & Precision Agriculture – GRAP, Universitat de Lleida/Agrotecnio-CERCA Center, Lleida, Catalunya, Spain

    Alexandre Escolà

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de Castro Megías, A.I. et al. (2021). Applications of Sensing for Disease Detection. In: Kerry, R., Escolà, A. (eds) Sensing Approaches for Precision Agriculture. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-030-78431-7_13

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