Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis

Remote Sensing

Volumn 10, Issue 2, 2018, Pages

  Salgadoe, Arachchige Surantha Ashan ^a   Robson, Andrew James ^a   Lamb, David William ^a   Dann, Elizabeth Kathryn ^b   Searle, Christopher ^c  

^a UNIVERSITY OF NEW ENGLAND   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c Stahmann Farms Australia   (Australia)

Author keywords

Avocado; Canopy porosity; Multispectral satellite imagery; Phytophthora root rot disease (PRR); RGB image gap analysis; Vegetation indices worldview 3

Indexed keywords

EDGE DETECTION; FARMS; FORESTRY; FRUITS; ORCHARDS; PHOTOGRAPHY; POROSITY; SATELLITE IMAGERY; VEGETATION;

AVOCADO; GAP ANALYSIS; MULTISPECTRAL SATELLITE IMAGERY; ROOT ROT; VEGETATION INDEX;

IMAGE ANALYSIS;

EID: 85042527380     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10020226     Document Type: Article

References (47)

1. Erwin, D.C.; Ribeiro, O.K. Phytophthora cinnamomi Rands (1922) var. cinnamomi. In Phytophthora Diseases Worldwide; APS Press: St. Paul, MN, USA, 1996; pp. 269-279. ISBN 978-0-89054-212-5
2. Marais, L.J.; Menge, J.A.; Bender, G.S.; Faber, B. Phytophthora Root Rot. AvoResearch Calif. Avocado Comm. 2002, 2, 3-6
3. Pegg, K.; Whiley, A.; Langdon, P.; Saranah, J. Comparison of phosetyl-Al, phosphorous acid and metalaxyl for the long-term control of Phytophthora root rot of avocado. Aust. J. Exp. Agric. 1987, 27, 471-474
4. Dann, E.K.; Ploetz, R.C.; Coates, L.M.; Pegg, K.G. Foliar, Fruit and Soilborne Diseases. In The Avocado: Botany, Production and Uses; Schaffer, B., Whiley, A.W., Wolstenholme, B.N., Eds.; CABI: Cambridge, MA, USA, 2012; pp. 397-398. ISBN 978-1-84593-701-0
5. Nutter, F.W.; Esker, P.D.; Netto, R.A.C. Disease Assessment Concepts and the Advancements Made in Improving the Accuracy and Precision of Plant Disease Data. Eur. J. Plant Pathol. 2006, 115, 95-103
6. Darvas, J.M.; Toerien, J.C.; Mine, D.L. Control of Avocado Root Rot by Trunk Injection with Phosethyl-Al. Plant Dis. 1984, 68, 691-693
7. Horner, I.J.; Jensen, E.H. Evaluation of Phytoptora Control in Avocados; New Zealand Avocado Growers' Association Annual Research Report; Avocado Grower's Association: Tauranga, New Zealand, 2004; pp. 1-7
8. Mahlein, A.-K. Plant Disease Detection by Imaging Sensors-Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Dis. 2016, 100, 241-251
9. Bock, C.H.; Parker, P.E.; Cook, A.Z.; Gottwald, T.R. Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves. Plant Dis. 2008, 92, 530-541
10. Newton, A.C.; Hackett, C.A. Subjective components of mildew assessment on spring barley. Eur. J. Plant Pathol. 1994, 100, 395-412
11. Steddom, K.; Bredehoeft, M.W.; Khan, M.; Rush, C.M. Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet. Plant Dis. 2005, 89, 153-158
12. Nixon, M.S.; Aguado, A.S. Human and computer vision. In Feature Extraction and Image Processing; Elsevier: Oxford, UK, 2008; pp. 1-29. ISBN 978-0-08-050625-8
13. Goodenough, A.E.; Goodenough, A.S. Development of a Rapid and Precise Method of Digital Image Analysis to Quantify Canopy Density and Structural Complexity. Int. Sch. Res. Not. 2012, 2012, 1-11
14. Macfarlane, C.; Hoffman, M.; Eamus, D.; Kerp, N.; Higginson, S.; McMurtrie, R.; Adams, M. Estimation of leaf area index in eucalypt forest using digital photography. Agric. For. Meteorol. 2007, 143, 176-188
15. Mora, M.; Avila, F.; Carrasco-Benavides, M.; Maldonado, G.; Olguín-Cáceres, J.; Fuentes, S. Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Comput. Electron. Agric. 2016, 123, 195-202
16. Poblete-Echeverría, C.; Fuentes, S.; Ortega-Farias, S.; Gonzalez-Talice, J.; Yuri, J. Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient. Sensors 2015, 15, 2860-2872
17. Castillo-Ruiz, F.; Castro-Garcia, S.; Blanco-Roldan, G.; Sola-Guirado, R.; Gil-Ribes, J. Olive Crown Porosity Measurement Based on Radiation Transmittance: An Assessment of Pruning Effect. Sensors 2016, 16, 723
18. De Bei, R.; Fuentes, S.; Gilliham, M.; Tyerman, S.; Edwards, E.; Bianchini, N.; Smith, J.; Collins, C. VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine. Sensors 2016, 16, 585
19. Pekin, B.; Macfarlane, C. Measurement of crown cover and leaf area index using digital cover photography and its application to remote sensing. Remote Sens. 2009, 1, 1298-1320
20. Apan, A.; Held, A.; Phinn, S.; Markley, J. Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery. Int. J. Remote Sens. 2004, 25, 489-498
21. 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 Sens. Environ. 2013, 139, 231-245
22. Calderón, R.; Navas-Cortés, J.; Zarco-Tejada, P. Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sens. 2015, 7, 5584-5610
23. Robson, A. Remote Sensing Application for the Determination of Yeild, Maturity and Aflatoxin Contamination in Peanut. Ph.D. Thesis, University of Queensland, Brisbane, Australia, 2007
24. Robson, A.; Rahman, M.M.; Muir, J.; Saint, A.; Simpson, C.; Searle, C. Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops. Adv. Anim. Biosci. 2017, 8, 498-504
25. Jackson, R.D.; Huete, A.R. Interpreting vegetation indexes. Prev. Vet. Med. 1991, 11, 185-200
26. Pinter, P.J., Jr.; Hatfield, J.L.; Schepers, J.S.; Barnes, E.M.; Moran, M.S.; Daughtry, C.S.T.; Upchurch, D.R. Remote Sensing for Crop Management. Photogramm. Eng. Remote Sens. 2003, 69, 647-664
27. Hatfield, J.L.; Gitelson, A.A.; Schepers, J.S.; Walthall, C.L. Application of Spectral Remote Sensing for Agronomic Decisions. Agron. J. 2008, 100, 117-131
28. Barton, C.V.M. Advances in remote sensing of plant stress. Plant Soil 2012, 354, 41-44
29. Weiss, M. Can_Eye V6.4.91 User Manual; French National Institute for Agriculture Research (INRA): Toulouse, France, 2017
30. Macfarlane, C.; Arndt, S.K.; Livesley, S.J.; Edgar, A.C.; White, D.A.; Adams, M.A.; Eamus, D. Estimation of leaf area index in eucalypt forest with vertical foliage, using cover and fullframe fisheye photography. For. Ecol. Manag. 2007, 242, 756-763
31. Fuentes, S.; Palmer, A.R.; Taylor, D.; Zeppel, M.; Whitley, R.; Eamus, D. An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI. Funct. Plant Biol. 2008, 35, 1070-1079
32. Fuentes, S.; Poblete-Echeverría, C.; Ortega-Farias, S.; Tyerman, S.; De Bei, R. Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods: New automated canopy vigour monitoring tool. Aust. J. Grape Wine Res. 2014, 20, 465-473
33. Liu, H.; Jezek, K.C. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. Int. J. Remote Sens. 2004, 25, 937-958
34. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62-66
35. Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.;Weiss, M.; Baret, F. Review of methods for in situ leaf area index determination. Agric. For. Meteorol. 2004, 121, 19-35
36. López-López, M.; Calderón, R.; González-Dugo, V.; Zarco-Tejada, P.; Fereres, E. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sens. 2016, 8, 276
37. Mahlein, A.-K.; Oerke, E.-C.; Steiner, U.; Dehne, H.-W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133, 197-209
38. Mahlein, A.-K.; Steiner, U.; Dehne, H.-W.; Oerke, E.-C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric. 2010, 11, 413-431
39. Yi, Q.; Jiapaer, G.; Chen, J.; Bao, A.; Wang, F. Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression. ISPRS J. Photogramm. Remote Sens. 2014, 91, 72-84
40. Nelson, M.R.; Orum, T.V.; Jaime-Garcia, R.; Nadeem, A. Applications of Geographic Information Systems and Geostatistics in Plant Disease Epidemiology and Management. Plant Dis. 1999, 83, 308-319
41. Erwin, D.C.; Ribeiro, O.K. Phytophthora Diseases Worldwide. In Phytophthora Diseases Worldwide; APS Press: St. Paul, MN, USA, 1996; pp. 8-38. ISBN 978-0-89054-212-5
42. Apan, A.; Held, A.; Phinn, S.; Markley, J. Formulation and assessment of narrow-band vegetation indices from EO-1 Hyperion imagery for discriminating sugarcane disease. In Proceedings of the Spatial Sciences Institute Biennial Conference, Canberra, Australia, 22-26 September 2003
43. Suarez, L.A.; Apan, A.; Werth, J. Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data. Int. J. Remote Sens. 2017, 38, 6528-6553
44. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 2008, 63, 409-426
45. Fu, Y.-Y.;Wang, J.-H.; Yang, G.-J.; Song, X.-Y.; Xu, X.-G.; Feng, H.-K. Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements. Guang Pu Xue Yu Guang Pu Fen Xi Guang Pu 2013, 33, 1315-1319
46. Sadgrove, E.J.; Falzon, G.; Miron, D.; Lamb, D. Fast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machine. Comput. Electron. Agric. 2017, 139, 204-212
47. Meroni, M.; Colombo, R.; Panigada, C. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sens. Environ. 2004, 92, 195-206