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Forrest W. Nutter, Jr.: a career in phytopathometry

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Abstract

Forrest W. Nutter, Jr., had a distinguished career in plant disease epidemiology and crop loss assessment, starting as a graduate student at the University of New Hampshire and North Dakota State University, and then as a faculty member at the University of Georgia and Iowa State University. This article reviews his pioneering contributions in phytopathometry over a nearly 40-year period, in which he developed and evaluated the tenets of disease assessment, and explored ways of improving assessment using visual, photographic, and electronic means. He initiated research on the measurement of disease and other crop traits using multispectral radiometers in 1983, and continued this work for decades, providing early evidence of the wavelengths often associated with disease severity, and demonstrating multiple applications for radiometric measurements. This work led to research on the reliability and accuracy of electronic and visual methods for assessing disease intensity and the relationships between different types of assessments; evaluations of the underlying principles of visual disease estimation; and the development of computerized training programs for disease assessment (e.g., Disease.Pro and Severity.Pro), which have been used in more than 25 countries and 100 universities. Among other things, his research provided substantial evidence to refute the Horsfall-Barratt scale for accurate or reliable visual disease assessment. His contributions have given a strong foundation for all researchers who wish to improve measurements of disease, or use measurements to evaluate controls or understand disease progress and crop losses.

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References

  • Adcock TE, Nutter FW Jr, Banks PA (1990) Measuring herbicide injury to soybeans using a radiometer. Weed Science 38:625–627

    CAS  Google Scholar 

  • Ang KL-M, Seng JKP (2021) Big data and machine learning with hyperspectral information in agriculture. IEEE9, pp 36699–36718

  • Bock CH, Nutter FW Jr (2011) Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis. CAB Reviews 6:1–15

    Google Scholar 

  • Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci 29:59–107

    Google Scholar 

  • Bock CH, Hotchkiss MW, Wood BW (2016) Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set. Plant Pathol 65:261–272

    Google Scholar 

  • Bock CH, Barbedo JGA, Del Ponte EM, Bohnenkamp D, Mahlein A-K (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathology Research 2:9

    Article  Google Scholar 

  • Bock CH, Chiang K-S, Del Ponte EM (2021) Plant disease severity estimated visually: a century of research, best practices and opportunities for improving methods and practices to maximize accuracy. Tropical Plant Pathology.  https://doi.org/10.1007/s40858-021-00439-z

  • Bohnenkamp D, Behmann J, Paulus S, Steiner U, Mahlein A-K (2021) A hyperspectral library of foliar diseases of wheat. Phytopathology.  https://doi.org/10.1094/PHYTO-09-19-0335-R

  • Camino C, Calderón R, Parnell S, Dierkes H, Chemin Y, Román-Écija M, Montes-Borrego M, Landa BB, Navas-Cortes JA, Zarco-Tejada PJ, Beck PSA (2021) Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant trials. Remote Sensing and Environment 260:112420

    CAS  PubMed Central  PubMed  Google Scholar 

  • Campbell CL, Madden LV (1990) Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York

    Google Scholar 

  • Chiang K-S, Bock CH, El Jarroudi M, Delfosse P, Lee IH, Liu HI (2016) Effects of rater bias and assessment method on disease severity estimation with regard to hypothesis testing. Plant Pathol 65:523–535

    Google Scholar 

  • Chiang K-S, Liu HI, Bock CH (2017) A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy. Ann Appl Biol 171:139–154

    Google Scholar 

  • Del Ponte EM, Pethybridge SJ, Bock CH, Michereff SJ, Machado FJ, Spolti P (2017) Standard area diagrams for aiding severity estimation: scientometrics, pathosystems, and methodological trends in the last 25 years. Phytopathology 107:1161–1174

    PubMed  Google Scholar 

  • Deng X, Huang Z, Zheng Z, Lan Y, Dai F (2019) Field detection and classification of citrus Huanglongbing based on hyperspectral reflectance. Comput Electron Agric 167:105006

    Google Scholar 

  • Esker PD, Gibb KS, Dixon PM, Nutter FW Jr (2006a) Use of survival analysis to determine the time-to-death of papaya due to phytoplasma diseases in Australia. Plant Dis 90:102–107

    PubMed  Google Scholar 

  • Esker PD, Harri J, Dixon PM, Nutter FW Jr (2006b) Comparison of models for forecasting of Stewart’s disease of corn in Iowa. Plant Dis 90:1353–1357

    CAS  PubMed  Google Scholar 

  • Esker PD, Gibb KS, Dixon PM, Nutter FW Jr (2007) An application of space-time analysis to improve the epidemiology understanding of the papaya-papaya yellow crinkle pathosystem. Plant Health Progress. https://doi.org/10.1094/PHP-2007-0726-02-RS

    Article  Google Scholar 

  • Fletcher J, Barnaby NG, Burans JP, Melcher U, Nutter FW Jr, Thomas C, Corona FMO (2010) Forensics plant pathology. In: B Budowle, SE Schutzer, RG Breeze, PS Keim, and SA Morse (eds) Microbial Forensics, 2nd edn. Elsevier Press, San Diego, pp 89–105

  • Gold KM, Townsend PA, Chlus A, Herrmann I, Couture JJ, Larson ER, Gevens AJ (2020) Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens 12:286

    Article  Google Scholar 

  • Goldstein EB (1989) Sensation and perception, 3rd edn. Wadsworth Publ, Belmont

    Google Scholar 

  • Guan J, Nutter FW Jr (2001) Factors affecting the quality and quantity of sunlight reflected from alfalfa canopies. Plant Dis 85:865–874

    CAS  PubMed  Google Scholar 

  • Guan J, Nutter FW Jr (2002a) Relationships between defoliation, leaf area index, canopy reflectance, and forage yield in the alfalfa-leaf spot pathosystem. Comput Electron Agric 37:97–112

    Google Scholar 

  • Guan J, Nutter FW Jr (2002b) Relationships between percentage defoliation, dry weight, percentage reflectance, leaf-to-stem ratio, and green leaf area index in the alfalfa leaf spot pathosystem. Crop Sci 42:1264–1273

    Google Scholar 

  • Guan J, Nutter FW Jr (2003) Quantifying the intra-rater repeatability and inter-rater reliability of visual disease and remote sensing assessment methods in the alfalfa foliar disease pathosystem. Can J Plant Pathol 25:143–149

    Google Scholar 

  • Guan J, Nutter FW Jr (2004) Comparison of single-point alfalfa yield models based on visual disease intensity and remote sensing assessments. Can J Plant Pathol 26:314–324

    Google Scholar 

  • Horsfall JG, Barratt RW (1945) An improved grading system for measuring plant disease. Phytopathology 35:655 (Abstract)

    Google Scholar 

  • Horsfall JG, Cowling EB (1978) Pathometry: The measurement of plant disease. In: Horsfall JG, Cowling EB (eds) Plant Disease: An Advanced Treatise, vol II. Academic Press, New York, pp 120–136

    Google Scholar 

  • James WC (1974) Assessment of plant diseases and losses. Annu Rev Phytopathol 12:27–48

    Google Scholar 

  • Krasil’fnikov NN (1993) Mathematical model of neural adaptation of human visual system to image luminance. Sensor Systems 7:100–109

    Google Scholar 

  • Large EC (1966) Measuring plant disease. Annu Rev Phytopathol 4:9–28

    Google Scholar 

  • Lin L, Hedayat AS, Sinha B, Yang M (2002) Statistical methods in assessing agreement: Models, issues, and tools. J Am Stat Assoc 97:257–270

    Google Scholar 

  • Lindow SE, Webb RR (1983) Quantification of foliar plant disease symptoms by microcomputer-digitized video image analysis. Phytopathol 73:520–524

    Google Scholar 

  • Madden LV, Nutter FW Jr (1995) Modeling crop losses at the field scale. Can J Plant Pathol 17:124–137

    Google Scholar 

  • Madden LV, Hughes G, van den Bosch F (2007) The Study of Plant Disease Epidemics. APS Press, American Phytopathological Society, St. Paul

    Google Scholar 

  • Mahlein A-K, Steiner U, Dehne HW, Oerke E-C (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis Agric 11:413–431

    Google Scholar 

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

    PubMed Central  PubMed  Google Scholar 

  • Mahlein A-K, Kuska MT, Behmann J, Polder G, Walter A (2018) Hyperspectral sensors and imaging technologies in phytopathology: State of the art. Annu Rev Phytopathol 56:535–558

    CAS  PubMed  Google Scholar 

  • McRoberts N, Thomas C, Brown JK, Nutter FW, Stack J, Martyn RD (2016) The evolution of a process for selecting and prioritizing plant diseases for recovery plans. Plant Dis 100:665–671

    CAS  PubMed  Google Scholar 

  • Mrisho LM, Mbilinyi NA, Ndalahwa M, Ramcharan AM, Kehs AK, McCloskey PC, Murithi H, Hughes DP, Legg JP (2020) Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava-CMD and CBSD. Frontiers in Plant Science 11:590889

    PubMed Central  PubMed  Google Scholar 

  • Nita M, Ellis MA, Madden LV (2003) Reliability and accuracy of visual estimation of Phomopsis leaf blight of strawberry. Phytopathology 93:995–1005

    CAS  PubMed  Google Scholar 

  • Nutter FW Jr (1980) BLIGHT FORECAST: A simplified program for grower determination of fungicide scheduling. Protection Ecology 2:219–222

    Google Scholar 

  • Nutter FW Jr (1989) Detection and measurement of plant disease gradients in peanut using a multispectral radiometer. Phytopathology 79:958–963

    Google Scholar 

  • Nutter FW Jr (1997b) Quantifying the temporal dynamics of plant viruses: a review. Crop Prot 16:603–618

    Google Scholar 

  • Nutter FW Jr (1999) Disease assessment theory and practice: “What we think we see is what we get.” Fitopatologia Brasileira 24:229–231

    Google Scholar 

  • Nutter FW Jr (2007) To honor Horsfall-Barratt and repeal their scale. Phytopathology 97:S137 (Abstr.)

    Google Scholar 

  • Nutter FW Jr (2010) Weber-Fechner Law. In: Salkind NJ (ed) Encyclopedia of research design, vol 3. Sage Publications, Thousand Oaks, pp 1612–1615

    Google Scholar 

  • Nutter FW Jr, Esker PD (2006) The role of psychophysics in phytopathology: the Weber-Fechner Law revisited. Eur J Plant Pathol 114:199–213

    Google Scholar 

  • Nutter FW Jr, Littrell RH (1996) Relationships among percent defoliation, canopy reflectance, and pod yield in the peanut-late leafspot system. Crop Prot 15:135–142

    Google Scholar 

  • Nutter FW Jr, Litwiller D (1998) A computer program to generate standard area diagrams to aid raters in assessing disease severity. Phytopathology 88:S117 (Abstr.)

    Google Scholar 

  • Nutter FW Jr, MacHardy WE (1980) A procedure for selecting components of a potato late blight forecasting and fungicidal control program. Plant Dis 64:1103–1105

    Google Scholar 

  • Nutter FW Jr, Madden LV (2005) Plant diseases as a possible consequence of biological attack. In: Bronze MS, Greenfield RA (eds) Biodefense: Principles and Pathogens. Horizon Bioscience, Norfold, pp 793–818

    Google Scholar 

  • Nutter FW Jr, Madden LV (2009) Plant pathogens as biological weapons against agriculture. In: Lutwick LI, Lutwick SM (eds) Beyond Anthrax: The Weaponization of Infectious Diseases. Humana Press, Springer-Verlag, New York, pp 335–363

    Google Scholar 

  • Nutter FW Jr, Pederson VD (1985) Receptivity, incubation period, and lesion size as criteria for screening barley genotypes for resistance to Pyrenophora teres. Phytopathology 75:603–606

    Google Scholar 

  • Nutter FW Jr, Schultz PM (1995) Improving the accuracy and precision of disease assessments: selection of methods and use of computer - aided training programs. Can J Plant Pathol 17:174–185

    Google Scholar 

  • Nutter FW Jr, Worawitlikit O (1989) Disease.Pro: a computerized program for evaluating and improving a person’s ability to assess disease proportion. Phytopathology 79:1135 (Abstr.)

    Google Scholar 

  • Nutter FW Jr, Cole H Jr, Schein RD (1983) Disease forecasting system for warm weather Pythium blight of turfgrass. Plant Dis 67:1126–1128

    Google Scholar 

  • Nutter FW Jr, Pederson VD, Timian RG (1984) Relationship between seed infection by barley stripe mosaic virus and yield loss. Phytopathology 74:363–366

    Google Scholar 

  • Nutter FW Jr, Pederson VD, Foster AE (1985) Effect of inoculations with Cochliobolus sativus at specific growth stages on grain yield and quality of malting barley. Crop Sci 25:933–938

    Google Scholar 

  • Nutter FW Jr, Littrell RH, Brenneman TB (1990) Evaluation of fungicide efficacy to control late leaf spot in peanut using a multispectral radiometer. Phytopathology 80:102–108

    Google Scholar 

  • Nutter FW Jr, Teng PS, Shokes FM (1991) Disease assessment terms and concepts. Plant Dis 75:1187–1188

    Google Scholar 

  • Nutter FW Jr, Gleason ML, Jenco JH, Christians NC (1993a) Accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems in the dollar spot-bentgrass pathosystem. Phytopathology 83:806–812

    Google Scholar 

  • Nutter FW Jr, Teng PS, Royer MH (1993b) Terms and concepts for yield, crop loss, and disease thresholds. Plant Dis 77:211–215

    Google Scholar 

  • Nutter FW Jr, Tylka GL, Guan J, Moreira AJD, Marett CC, Rosburg TR, Basart JP, Chong CS (2002) Use of remote sensing to detect plant stress caused by soybean cyst nematode. J Nematol 34:222–231

    CAS  PubMed Central  PubMed  Google Scholar 

  • Nutter FW Jr, Esker PD, Coelho Netto RA (2006) Disease assessment of concepts and the advancements made in improving the accuracy and precision of plant disease data. Eur J Plant Pathol 115:95–103

    Google Scholar 

  • Nutter FW Jr, Van Rij N, Eggenberger SK, Holah N (2010) Spatial and temporal dynamics of plant pathogens. In: Oerke E-C, Gerhards R, Menz G, Sikora RA (eds) Precision Crop Protection – The Challenge and Use of Heterogeneity. Springer, NY, pp 27–50

    Google Scholar 

  • Nutter FW Jr, Byamukama E, Coelho-Netto RA, Eggenberger SK, Gleason ML, Gougherty A, Robertson AE, Van Rij N (2011) Integrating GPS, GIS, and remote sensing technologies with disease management principles to improve plant health. In: Clay S (ed) GIS Applications in Agriculture-Invasive Species. Taylor & Francis Group LLC, Boca Raton, pp 59–90

    Google Scholar 

  • Nutter FW Jr, Eggenberger SK, Streit AJ (2014a) Disease severity assessment training using DISEASE.PRO. In: Stevenson K, Jeger M (eds) Exercises in Plant Disease Epidemiology, 2nd edn. APS Press, St. Paul, pp 189–198

    Google Scholar 

  • Nutter FW Jr, Eggenberger SK, Streit AJ (2014) Intra-rater and inter-rater agreement in disease assessment. In: Stevenson K, Jeger M (eds) Exercises in Plant Disease Epidemiology, 2nd edn. APS Press, St. Paul, pp 197–202

    Google Scholar 

  • Nutter FW Jr, Esker PD (2001) Disease assessment keys. In: OC Maloy, TD Murray (eds) Encyclopedia of plant pathology. John Wiley and Sons, Inc., NY, pp 323–326

  • Nutter FW Jr (1990) Remote sensing and image analysis for crop loss assessment. In: Crop Loss Assessment in Rice. International Rice Research Institute, Los Banos, pp 93–105

  • Nutter FW Jr. (1997a) Disease severity assessment training. In: L Francl, D Neher (eds) Exercises in Plant Disease Epidemiology. APS Press, St. Paul, pp 1–7. 233 p

  • Nutter FW Jr (2001) Disease assessment terms and concepts. In: OC Maloy, TD Murray (eds) Encyclopedia of plant pathology. John Wiley and Sons, Inc., NY, pp 312–323

  • Nutter FW Jr (2010b) The 8th I.E. Melhus graduate student symposium: Forty-Five years after Van der Plank, new visions for the future of plant disease epidemiology. Online. Plant Health Progress 11(1).  https://doi.org/10.1094/PHP-2010-0526-01-SY

  • Patrignani A, Ochsner TE (2015) Canopeo: A powerful new tool for measuring fractional green canopy cover. Agron J 107:2312–2320

    CAS  Google Scholar 

  • Pederson VD, Fiechtner G (1980) A Low-cost, Compact Data Acquisition System for Recording Visible and Infrared Reflection from Barley Crop Canopies. Crop Loss Assessment. Agr. Exp. Sta. Univ of Minn Misc Publ 7:71–75

    Google Scholar 

  • Pederson VD, Nutter FW Jr (1983) Low-cost portable multispectral radiometer for assessment of onset and severity of foliar disease of barley. Proc SPIE (Int Soc Optical Eng) 356:126–130

    Google Scholar 

  • Pethybridge SJ, Nelson SC (2015) Leaf Doctor – a new application for assessing disease severity. Plant Dis 99:1310–1316

    PubMed  Google Scholar 

  • Pethybridge SJ, Esker PD, Hay FS, Wilson CR, Nutter FW Jr (2005) Spatiotemporal description of epidemics caused by Phoma ligulicola in Tasmanian pyrethrum fields. Phytopathology 95:648–658

    PubMed  Google Scholar 

  • Pethybridge SJ, Hay FS, Esker PD, Wilson CR, Nutter FW Jr (2007a) Use of a multispectral radiometer for noninvasive assessments of foliar disease caused by ray blight in pyrethrum. Plant Dis 91:1397–1406

    PubMed  Google Scholar 

  • Pethybridge SJ, Esker PD, Dixon P, Hay FS, Groom T, Wilson CR, Nutter FW Jr (2007b) Quantifying loss caused by ray blight disease in Tasmanian pyrethrum fields. Plant Dis 91:1116–1121

    PubMed  Google Scholar 

  • Pethybridge SJ, Hay FS, Esker PD, Groom T, Wilson CR, Nutter FW Jr (2008a) Visual and radiometric assessments for yield losses caused by ray blight in pyrethrum. Crop Sci 48:343–352

    Google Scholar 

  • Pethybridge SJ, Hay FS, Esker PD, Gent DH, Wilson CR, Groom T, Nutter FW (2008b) Diseases of pyrethrum in Tasmania: Challenges and prospects for management. Plant Dis 92:1260–1272

    PubMed  Google Scholar 

  • Pethybridge SJ, Gent DH, Esker PD, Turechek WW, Hay FS, Nutter FW Jr (2009) Site-specific risk factors for ray blight in Tasmanian pyrethrum fields. Plant Dis 93:229–237

    PubMed  Google Scholar 

  • Pethybridge SJ, Vaghefi N, Kikkert JR (2017) Management of Cercospora leaf spot in conventional and organic table beet production. Plant Dis 101:1642–1651

    CAS  PubMed  Google Scholar 

  • Raza MM, Eggenberger S, Nutter FW Jr, Leandro LFS (2017) Can canopy reflectance be used for early detection of soybean sudden death syndrome? Phytopathology 107:S175-176 (Abstr.)

    Google Scholar 

  • Raza MM, Eggenberger S, Nutter FW Jr, Leandro LFS (2019) Early detection of soybean sudden death syndrome using high-resolution satellite imagery. Phytopathology 109:S189 (Abstr.)

    Google Scholar 

  • Raza MM, Harding C, Liebman M, Leandro LF (2020) Exploring the potential of high-resolution satellite imagery for the detection of soybean sudden death syndrome. Remote Sens 12:1213

    Google Scholar 

  • Savary S, Teng PS, Willocquet L, Nutter FW Jr (2006) Quantification and modeling of crop losses: A review of purposes. Annu Rev Phytopathol 44:89–112

    CAS  PubMed  Google Scholar 

  • Singh V, Sharma N, Singh S (2020) A review of imaging techniques for plant disease detection. Artificial Intelligence in Agriculture 4:229–242

    Google Scholar 

  • Spearschneider J (2020) Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale. New Phytol 228:35–41

    Google Scholar 

  • Tomerlin JR, Howell TA (1988) DISTRAIN: a computer program for training people to estimate disease severity on cereal leaves. Plant Dis 72:455–459

    Google Scholar 

  • Zhu H, Chu B, Zhang C, Liu F, Jiang L, He Y (2017) Hyperspectral imaging of presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Scientific Reports 7:4125

    PubMed Central  PubMed  Google Scholar 

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Madden, L.V., Esker, P.D. & Pethybridge, S.J. Forrest W. Nutter, Jr.: a career in phytopathometry. Trop. plant pathol. 47, 5–13 (2022). https://doi.org/10.1007/s40858-021-00469-7

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