AS  Vol.8 No.12 , December 2017
Hyperspectral Evaluation of Venturia inaequalis Management Using the Disease Predictive Model RIMpro in the Northeastern U.S.
Use of hyperspectral spectroradiometers allows for information on different light bands to be captured, allowing for identification of plant health status. Apple scab is the most important disease in the production of apples. RIMpro is a web-based decision support system (DDS) for orchardists that has the capacity to improve optimal fungicide application for the control of apple scab and has the potential to reduce the number of applications and thereby reduce input expenses. The objective of this study was to complete a hyperspectral assessment of apple leaves in order to evaluate the spectral characteristics of trees sprayed according to forecasted infection events from the DDS. No significant differences in visual assessments or vegetation indices were observed between conventionally treated leaves and leaves treated according to the DSS. In the first year of this study two fungicide treatments were eliminated, in the second one fungicide treatment was eliminated. This finding is important because it provides evidence that plant health status is similar between conventionally sprayed trees and trees on a DSS-guided reduced spray program. In addition, the use of spectroradiometers for assessing the efficacy of different fungicide programs was demonstrated. Finally, potassium bicarbonate tank-mixed with sulfur was confirmed to be an effective spray material for managing apple scab. By integrating the precise information provided by DSSs and the use of biorational pesticides, agricultural producers, service providers and educators are able to adapt climate change considerations and action-oriented decisions into pest management plans.
Cite this paper: Wallhead, M. , Zhu, H. and Broders, K. (2017) Hyperspectral Evaluation of Venturia inaequalis Management Using the Disease Predictive Model RIMpro in the Northeastern U.S.. Agricultural Sciences, 8, 1358-1371. doi: 10.4236/as.2017.812098.

[1]   Asner, G. (1998) Biophysical and Biochemical Sources of Variability in Canopy Reflectance. Remote Sensing of Environment, 64, 234-253.

[2]   Jackson, R.D. (1984) Remote Sensing of Vegetation Characteristics for Farm Management. SPIE Proceedings, 475, 81-96.

[3]   Bauer, M. (1985) Spectral Inputs to Crop Identification and Condition Assessment. Proceedings of the IEEE, 73, 1071-1085.

[4]   Hatfield, P. and Pinter, P. (1993) Remote Sensing for Crop Protection. Crop Protection, 12, 403-413.

[5]   Garcia-Ruiz, F., Sankaran, S., Mari Maja, J., Lee, W., Rasmussen, J. and Ehsani, R. (2013) Comparison of Two Aerial Imaging Platforms for Identification of Huanglongbing-Infected Citrus Trees. Computers and Electronics in Agriculture, 91, 106-115.

[6]   Rock, B., Vogelmann, J., Williams, D., Vogelmann, A. and Hoshizaki, T. (1986) Remote Detection of Forest Damage. BioScience, 36, 439-445.

[7]   Hunt, E. and Rock, B. (1989) Detection of Changes in Leaf Water Content Using Near- and Middle-Infrared Reflectances. Remote Sensing of Environment, 30, 43-54.

[8]   MacHardy, W. (1996) Apple Scab: Biology, Epidemiology, and Management. APS Press, St. Paul, MN.

[9]   KÖhl, J., Scheer, C., Holb, I., Masny, S. and Molhoek, W. (2015) Toward an Integrated Use of Biological Control by Cladosporium cladosporioides H39 in Apple Scab (Venturia inaequalis) Management. Plant Disease, 99, 535-543.

[10]   Carisse, O., Philion, V., Rolland, D. and Bernier, J. (2000) Effect of Fall Application of Fungal Antagonists on Spring Ascospore Production of the Apple Scab Pathogen, Venturia inaequalis. Phytopathology, 90, 31-37.

[11]   Holb, I., Heijne, B., Withagen, J., Gáll, J. and Jeger, M. (2005) Analysis of Summer Epidemic Progress of Apple Scab at Different Apple Production Systems in the Netherlands and Hungary. Phytopathology, 95, 1001-1020.

[12]   Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., Dubash, N.K., et al. (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, 1-151.

[13]   MacHardy, W.E. and Gadoury, D.M. (1989) A Revision of Mills Criteria for Predicting Apple Scab Infection Periods. Phytopathology, 79, 304-310.

[14]   Trapman, M. and Polfliet, M. (1997) Management of Primary Infections of Apple-Scab with the Simulation Program RIMpro: Review of Four Years Field Trials. IOBC wprs Bulletin, 20, 241-250.

[15]   Chapman, P. and Catlin, G. (1976) Growth Stages in Fruit Trees-From Dormant to Fruit Set. New York’s Food and Life Sciences Bulletin, 58, 1-11.

[16]   Beresford, R., Wright, P., Wood, P., Park, N., Larsen, N. and Fisher, B. (2013) Resistance of Venturia inaequalis to Demethylation Inhibitor and Dodine Fungicides in Four New Zealand Apple-Growing Regions. New Zealand Plant Protection, 66, 274-283.

[17]   Fiaccadori, R., Cicognani, E., Alberoni, G., Collina, M. and Brunelli, A. (2011) Sensitivity to Strobilurin Fungicides of Italian Venturia inaequalis Populations with Different Origin and Scab Control. Pest Management Science, 67, 535-540.

[18]   Trapman, M., Triloff, P., Philion, V. and Scholffer, K. (2012) The European Apple Scab Sandwich. The European Fruit Magazine, 2012, 4.

[19]   Rouse Jr, J., Haas, R., Schell, J. and Deering, D. (1974) Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.

[20]   Rock, B., Hoshizaki, T. and Miller, J. (1988) Comparison of in Situ and Airborne Spectral Measurements of the Blue Shift Associated with Forest Decline. Remote Sensing of Environment, 24, 109-127.

[21]   Vogelmann, J. and Rock, B. (1986) Assessing Forest Decline in Coniferous Forests of Vermont using NS-001 Thematic Mapper Simulator Data. International Journal of Remote Sensing, 7, 1303-1321.

[22]   Delalieux, S., Somers, B., Verstraeten, W., van Aardt, J., Keulemans, W. and Coppin, P. (2009) Hyperspectral Indices to Diagnose Leaf Biotic Stress of Apple Plants, Considering Leaf Phenology. International Journal of Remote Sensing, 8, 1887-1912.

[23]   Thenkabail, P., Smith, R. and De Pauw, E. (2000) Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics. Remote Sensing of Environment, 71, 158-182.

[24]   Merton, R. (1999) Multi-Temporal Analysis of Community Scale Vegetation Stress with Imaging Spectroscopy. PhD Thesis, Department of Geography, University of Auckland, Auckland.

[25]   Zarco-Tejada, P., Miller, J., Noland, T., Mohammed, G. and Sampson, P. (2001) Scaling-Up and Model Inversion Methods with Narrowband Optical Indices for Chlorophyll Content Estimation in Closed Forest Canopies with Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 39, 1491-1507.

[26]   Carroll, J., Weigle, T. and Petzoldt, C. (2009) The Network for Environment & Weather Applications (NEWA). New York Fruit Quarterly, 19, 5-9.

[27]   Koehler, G. (2007) Introduction to Ag-Radar. University of Maine Cooperative Extension.

[28]   Russo, J.M. (1997) Site Specific Weather Forecasts for IPM Decision. Proceedings of the 3rd National IPM Symposium/Workshop: Broadening Support for 21st Century IPM, Washington DC, 27 February-1 March 1996, 275-276.

[29]   Gates, D., Keegan, H., Schleter, J. and Weidner, V. (1965) Spectral Properties of Plants. Applied Optics, 4, 11-20.

[30]   Stephens, C. and Rasmussen, P. (2010) On Target Near-Infrared Tutorial. Utah State University Extension, Logan.

[31]   Holb, I. and Kunz, S. (2016) Integrated Control of Apple Scab and Powdery Mildew in an Organic Apple Orchard by Combining Potassium Carbonates with Wettable Sulfur, Pruning and Cultivar Susceptibility. Plant Disease, 100, 1894-1905.