Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado

Remote Sensing of Environment

Volumn 171, Issue , 2015, Pages 33-44

  De Castro, A I ^a   Ehsani, R ^a   Ploetz, R ^b   Crane, J H ^b   Abdulridha, J ^a  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Aerial image; Avocado; Early disease detection; Laurel wilt; Precision agriculture; Spectral analysis; Vegetation indices

Indexed keywords

AGRICULTURE; CAMERAS; CITRUS FRUITS; CROPS; DISEASE CONTROL; FORESTRY; FRUITS; IMAGE ANALYSIS; INFRARED DEVICES; MULTILAYER NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SPECTRUM ANALYSIS;

AERIAL IMAGES; AVOCADO; EARLY DISEASE DETECTION; LAUREL WILT; PRECISION AGRICULTURE; VEGETATION INDEX;

PLANTS (BOTANY);

FRUIT; IMAGE ANALYSIS; REMOTE SENSING; SPECTRAL ANALYSIS; SYMPTOM; VIDEOGRAPHY; WAVELENGTH; WILT;

FLORIDA [UNITED STATES]; UNITED STATES;

CITRUS; PERSEA AMERICANA; PHYTOPHTHORA;

EID: 84944454638     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.09.011     Document Type: Article

References (72)

1. Ashourloo D., Mobasheri M.R., Huete A. Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sensing 2014, 6:5107-5123.
2. Broge N.H., Leblanc E. 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 2000, 76:156-172.
3. Burks T.F., Shearer S.A., Heath J.R., Donohue K.D. Evaluation of neural-network classifiers for weed species discrimination. Biosystems Engineering 2005, 91(3):293-304.
4. Calderón R., Navas-Cortés J.A., Lucena C., Zarco-Tejada P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment 2013, 139:231-245.
5. Carillo D., Duncan R.E., Ploetz J.N., Campbell A.F., Ploetz R.C., Peña J.E. Lateral transfer of a phytopathogentic symbiont among native and exotic ambrosia beetles. Plant Pathology 2014, 63:54-62.
6. Chemically Speaking Newsletter Pesticide Potpourri 2009, February, 6. University of Florida, IFAS Ext. Pesticide Information Office, Gainesville, (http://pested.ifas.ufl.edu/newsletter.html. Accessed 9 January 2015).
7. Chen J. Evaluation of vegetation indices and modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 1996, 22:229-242.
8. Dammer K.H., Möller B., Rodemann B., Heppner D. Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Protection 2011, 30:420-428.
9. Daughtry C.S.T., Walthall C.L., Kim M.S., Brown de Colstoun E., McMurtrey J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 2000, 74:229-239.
10. De Castro A.I., Ehsani R., Ploetz R.C., Crane J.H., Buchanon S. Detection of laurel wilt disease in avocado using low altitude aerial imaging. PLoS ONE 2015, 10(4):e0124642. 10.1371/journal.pone.0124642.
11. De Castro I.A., Jurado-Expósito M., Peña-Barragán J.M., López-Granados F. Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops. The Scientific World Journal 2012, (section Agronomy).
12. Del Brío M.B., Molina S.A. Redes neuronales y sistemas borrosos 2006, RA-MA, Madrid, Spain. (3rd Ed.).
13. Du Q., Chang N.B., Yang C., Srilakshmid K.R. Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management. Journal of Environmental Management 2008, 86:14-26.
14. Estep L., Terrie G., Davis B. Technical note: Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks. International Journal of Remote Sensing 2004, 25:4999-5004. 10.1080/01431160412331291242.
15. Evans E.A., Crane J.H. Estimates of the replacement costs of commercial and backyard avocado trees in South Florida. Electronic data information source (EDIS) FE82500 2012, Food and Resource Economics Department, University of Florida, Gainesville, FL, (http://edis.ifas.ufl.edu/pdffiles/FE/FE82500.pdf. Accessed 9 January 2015).
16. Evans E.A., Bernal Lozano I. Sample avocado production costs and profitability analysis for Florida. Electronic data information source (EDIS) FE837 2015, Food and Resource Economics Department, University of Florida, Gainesville, FL, (http://edis.ifas.ufl.edu/pdffiles/FE/FE83700.pdf. Accessed 19 March 2015).
17. Evans E.A., Crane J.H., Hodges A., Osborne J.L. Potential economic impact of laurel wilt disease on the Florida avocado industry. HortTechnology 2010, 20:234-238.
18. FAO FAOSTAT online database at:, (Accessed 9 January 2015). http://www.fao.org/default.htm.
19. Flinn, A. (2014) Personal communication. Manager of Avocado Administrative Committee. Homestead, FL (October 30).
20. Fraedrich S.W., Harrington T.C., Rabaglia R.J., Mayfield A.E., Hanula J.L., Eickwort J.M., Miller D.R. A fungal symbiont of the redbay ambrosia beetle causes a lethal wilt in redbay and other Lauraceae in the Southeastern United States. Plant Disease 2008, 92:215-224.
21. García-Ruiz F., Sankaran S., Maja J.M., Lee W.S., Rasmussen J., Ehsani R. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 2013, 91:106-115.
22. Gitelson A.A., Merzlyak M.N. Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 1996, 148:494-500.
23. Gitelson A.A., Kaufman Y.J., Stark R., Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 2002, 80:76-87.
24. Gómez-Candón D., De Castro A.I., López-Granados F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture 2014, 15:44-56.
25. Gray C.J., Shaw D.R., Gerard P.D., Bruce L.M. Utility of multispectral imagery for soybean and weed species differentiation. Weed Technology 2008, 22(4):713-718.
26. Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., Strachan I. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 2004, 90:337-352.
27. Haboudane D., Miller J.R., Tremblay N., Zarco-Tejada P.J., Dextraze L. Integration of hyperspectral vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 2002, 81:416-426.
28. Han J., Kamner M., Pei J. Data mining. Concepts and techniques 2012, Morgan Kaufmann Publishers, Elsevier Inc., Waltham, MA, USA. 3rd ed.
29. Harrington T.C., Fraedrich S.W., Aghayeva D. Raffaelea lauricola, a new ambrosia beetle symbiont and pathogen on the Lauraceae. Mycotaxon 2008, 102:399-404.
30. Heremans S., Bossyns B., Eerens H., Van Orshoven J. Efficient collection of training data for sub-pixel land cover classification using neural networks. International Journal of Applied Earth Observation and Geoinformation 2011, 13(4):657-667. Elsevier B.V.
31. Inch S.A., Ploetz R.C. Impact of laurel wilt, caused by Raffaelea lauricola, on xylem function in avocado, Persea americana. Forest Pathology 2012, 42:239-245.
32. Jordan C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969, 50:663-666.
33. Kaufman Y.J., Remer L.A. Detection of forests using mid-IR reflectance: An application for aerosol studies. IEEE Transactions on Geoscience and Remote Sensing 1994, 32:672-683.
34. Këranen M., Aro E.M., Tyystjärvi E., Nevalainen O. Automatic plant identification with chlorophyll fluorescence fingerprinting. Precision Agriculture 2003, 4:53-67.
35. Laliberte A.S., Goforth M.A., Steele C.M., Rango A. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Remote Sensing 2011, 3(11):2529-2551.
36. Lebourgeois V., Bégué A., Labbé S., Mallavan B., Prévot L., Roux B. Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors 2008, 8(11):7300-7322.
37. López-Granados F. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research 2011, 51:1-11.
38. Merton R., Huntington J. Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. Summaries of the Eighth Annual JPL Airborne Earth Science Workshop 1999, 299-307. Jet Propulsion Laboratory, National Aeronautics and Space Administration, Pasandena, CA, USA.
39. Meyer G.E., Hindman T.W., Lakshmi K. Machine vision detection parameters for plant species identification. The SPIE Proceedings 1998, Vol. 3543. (Bellingham, WA).
40. Naidu R.A., Perry E.M., Pierce F.J., Mekuria T. The potential of spectral reflectance technique for the detection of grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture 2009, 66:38-45.
41. Peña-Barragán J.M., Ngugi M.K., Plant R.E., Six J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment 2011, 115:1301-1316.
42. Ploetz R.C., Carrillo D. Laurel wilt of avocado: Epidemiology and management of a recalcitrant disease of an important crop. Journal of Phytopathology 2015, (in press).
43. Ploetz R.C., Harrington T., Hulcr J., Fraedrich S., Smith J.A., Inch S., Kendra P., Mayfield A.E., et al. Recovery plan for laurel wilt of avocado (caused by Raffaelea lauricola). National Plant Disease Recovery System. Homeland Security Presidential Directive Number 9 (HSPD-9) 2011, (Available: http://www.ars.usda.gov/research/docs.htm?docid=14271).
44. Ploetz R.C., Hulcr J., Wingfield M., de Beer Z.W. Destructive tree diseases associated with ambrosia and bark beetles: Black swan events in tree pathology?. Plant Disease 2013, 95:856-872.
45. Ploetz R.C., Pérez-Martínez J.M., Smith J.A., Hughes M., Dreaden T.J., Inch S.A., Fu Y. Responses of avocado to laurel wilt, caused by Raffaelea lauricola. Plant Pathology 2012, 61:801-808.
46. Ploetz R.C., Peña J.E., Smith J.A., Dreaden T.L., Crane J.H., Schubert T., Dixon W. Laurel wilt is confirmed in Miami-Dade County, center of Florida's commercial avocado production. Plant Disease 2011, 95:1599.
47. Pozdnyakova L., Oudemans P.V., Hughes M.G., Giménez D. Estimation of spatial and spectral properties of phytophthora root rot and its effects on cranberry yield. Computers and Electronics in Agriculture 2012, 37:57-70.
48. Pybas, D. (2009). Personal communication, Miami, FL.
49. Qi J., Chehbouni A., Huete A.R., Keer Y.H., Sorooshian S. A modified soil vegetation adjusted index. Remote Sensing of Environment 1994, 48:119-126.
50. Rabaglia R.J., Dole S.A., Cognato A.I. Review of American Xyleborina (Coleoptera: Curculionidae: Scolytinae) occurring north of Mexico, with an illustrated key. Annals of the Entomological Society of America 2006, 99:1034-1056.
51. Raikes C., Burpee L.L. Use of multispectral radiometry for assessment of rhizoctonia blight in creeping bentgrass. Phytopathology 1998, 88:446-449.
52. Ray S.S., Jain N., Miglani A., Singh J.P., Singh A.K., Panigrahy S., Parihar J.S. Defining optimum spectral narrow bands and bandwidths for agricultural applications. Current Science 2010, 98:1365-1369.
53. Rogan J., Franklin J., Stow D., Miller J., Woodcock C., Roberts D. Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment 2008, 112:2272-2283. 10.1016/j.rse.2007.10.004.
54. Rondeaux G., Steven M., Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 1996, 55:95-107.
55. Rougean J.L., Breon F.M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 1995, 51:375-384.
56. Rouse J.W., Haas R.H., Schell J.A., Deering D.W. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Earth Resources Technology Satellite Symposium NASA SP-351, Washington, DC, USA 1973, Vol. 1:309-317.
57. Rzempoluck E.J. Neural network data analysis using Simulnet 1997, 1-3. Simon Fraser University, Burnaby. B.C. Canada, (13-75).
58. Sankaran S., Ehsani R., Inch S.A., Ploetz R.C. Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt. Plant Disease 2012, 96:1683-1689.
59. Sankaran S., Khot L.R., Maja J.M., Ehsani R. Comparison of two multiband cameras for use on small UAVs in agriculture. 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 25-28 June, Gainesville, FL 2013.
60. Sankaran S., Mishra A., Ehsani R., Davis C. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 2010, 72:1-13.
61. Smith A.M.S., Drake N.A., Wooster M.J., Hudak A.T., Holden Z.A., Gibbsons C.J. Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS. International Journal of Remote Sensing 2007, 28:2753-2775.
62. Smith G.M., Milton E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing 1999, 20:2653-2662.
63. SPSS v.22 IBM SPSS Statistics 22 documentation 2014, Redmond, WA, Microsoft Corp., Reference book.
64. Suárez L., Zarco-Tejada P.J., González-Dugo V., Berni J.A.J., Sagardoy R., Morales F., Fereres E. Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery. Remote Sensing of Environment 2010, 114:286-298.
65. Thenkabail P.S., Lyion J.G., Huete A. Hyperspectral remote sensing of vegetation 2012, Taylor and Francis Group, Boca Raton, FL, (Chapter 14). CRC Press (Ed.).
66. Torres-Sánchez J., López-Granados F., De Castro A.I., Peña-Barragán J.M. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE 2013, 8:e58210.
67. USDA Census of agriculture, Florida: State and county data. Graphic area series, Part 9 2009, April, Vol. 1. United States Department of Agriculture, National Agricultural Statistics Service, Washington, D.C., (http://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_1_State_Level/Florida/flv1.pdf. Accessed 22 May 2015).
68. USDA Noncitrus fruits and nuts: 2013 Summary, 1948-2698. July 2014 2013, National Agricultural Statistic Service, United States Department of Agriculture, Washington, D.C., (http://usda.mannlib.cornell.edu/usda/current/NoncFruiNu/NoncFruiNu-07-17-2014_revision.pdf. Accessed 8 March 2015).
69. USDA.Forest Service Forest Health Protection. Laurel Wilt Information 2014, (http://www.fs.fed.us/r8/foresthealth/laurelwilt/dist_map.shtml. Accessed 9 January 2015).
70. Usha K., Singh B. Potential applications of remote sensing in horticulture - A review. Scientia Horticulturae 2013, 153:71-83.
71. Wu N., Liu J., Zhou G., Yan R., Zhang L. Prediction of chlorophyll content of leaves of oil camelliae after being infected with anthracnose based on Vis/NIR spectroscopy. Spectroscopy and Spectral Analysis 2012, 32:1221-1224.
72. Zhang M., Qin Z., Liu X. Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture 2005, 6:489-508.