Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado

Computers and Electronics in Agriculture

Volumn 155, Issue , 2018, Pages 203-211

  Abdulridha, Jaafar ^a   Ampatzidis, Yiannis ^a   Ehsani, Reza ^b   de Castro, Ana I ^c  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

Author keywords

Decision tree; Disease detection; Multi layer perceptron; Neural networks; Spectral reflectance analysis; Vegetation indices

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FRUITS; INFRARED DEVICES; IRON; MULTILAYER NEURAL NETWORKS; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; NITROGEN; REMOTE SENSING; VEGETATION;

CLASSIFICATION METHODS; DISEASE DETECTION; MULTI LAYER PERCEPTRON; MULTI VARIATE ANALYSIS; MULTILAYER PERCEPTRON NEURAL NETWORKS; NITROGEN DEFICIENCY; SPECTRAL REFLECTANCES; VEGETATION INDEX;

TREES (MATHEMATICS);

ARTIFICIAL NEURAL NETWORK; BEETLE; DECISION ANALYSIS; DETECTION METHOD; ENVIRONMENTAL FACTOR; FRUIT; FUNGAL DISEASE; INFECTIOUS DISEASE; MULTIVARIATE ANALYSIS; PERFORMANCE ASSESSMENT; REMOTE SENSING; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE; SYMPTOM;

FLORIDA [UNITED STATES]; UNITED STATES;

LAURACEAE; PERSEA AMERICANA; PERSEA BORBONIA; PLATYPODINI;

EID: 85055114146     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.10.016     Document Type: Article

References (81)

1. Abdulridha, J., Ehsani, R., de Castro, A.I., Detection and differentiation between laurel wilt disease phytophthora disease, and salinity damage using a hyperspectral sensing technique. Agric.-Basel, 6, 2016, 13.
2. Ampatzidis, Y., De Bellis, L., Luvisi, A., iPathology: robotic applications and management of plants and plant diseases. Sustainability, 2017, 9.
3. Apan, A., Held, A., Phinn, S., Markley, J., Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery. Int. J. Rem. Sens. 25 (2004), 489–498.
4. Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., Royo, C., Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92 (2000), 83–91.
5. Atkinson, P.M., Tatnall, A.R.L., Neural networks in remote sensing - Introduction. Int. J. Rem. Sens. 18 (1997), 699–709.
6. Babar, M.A., Reynolds, M.P., Van Ginkel, M., Klatt, A.R., Raun, W.R., Stone, M.L., Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46 (2006), 1046–1057.
7. Bajwa, S.G., Rupe, J.C., Mason, J., Soybean disease monitoring with leaf reflectance. Rem. Sens., 2017, 9.
8. Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., Herppich, W.B., Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 75 (2011), 304–312.
9. Blackburn, G.A., Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int. J. Rem. Sens. 19 (1998), 657–675.
10. Boroujerdnia, M., Ansari, N.A., Dehcordie, F.S., Effect of cultivars, harvesting time and level of nitrogen fertilizer on nitrate and nitrite content, yield in romaine lettuce. Asian J. Plant Sci. 6 (2007), 550–553.
11. 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. Rem. Sens. Environ. 76 (2001), 156–172.
12. Carrillo, D., Crane, J.H., Pena, J.E., Potential of contact insecticides to control Xyleborus glabratus (Coleoptera: Curculionidae), a vector of laurel wilt disease in avocados. J. Econ. Entomol. 106 (2013), 2286–2295.
13. Chan, J.C.W., Paelinckx, D., Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Rem. Sens. Environ. 112 (2008), 2999–3011.
14. Cruz, A.C., Luvisi, A., De Bellis, L., Ampatzidis, Y., X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion. Front. Plant Sci., 2017, 10.3389/fpls.2017.01741 10 October 2017.
15. de Castro, A.I., Jurado-Exposito, M., Gomez-Casero, M.T., Lopez-Granados, F., Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops. Sci. World J., 2012, 2012, 630390.
16. de Castro, A.I., Ehsani, R., Ploetz, R.C., Crane, J.H., Abdulridha, J., Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Rem. Sens. Environ. 171 (2015), 33–44.
17. 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, 10(4), 2015, e0124642.
18. De Castro, A.I., Torres-Sánchez, J., Peña, J.M., Jiménez-Brenes, F.M., Csillik, O., López-Granados, F., An automatic random forest-OBIA algorithm for earlyweed mapping between and within crop rows using UAV imagery. Rem. Sens., 2018(10), 2018, 285.
19. de Castro, A.I., Jiménez-Brenes, F.M., Torres-Sánchez, J., Peña, J.M., Borra-Serrano, I., López-Granados, F., 3-D characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture applications. Rem. Sens., 10, 2018, 584.
20. Evans, E.A., Crane, J., Hodges, A., Osborne, J.L., Potential economic impact of laurel wilt disease on the florida avocado industry. Horttechnology 20 (2010), 234–238.
21. Fraedrich, S.W., Harrington, T.C., Rabaglia, R.J., Ulyshen, M.D., Mayfield, A.E. III, 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 Dis. 92 (2008), 215–224.
22. Frels, K., Guttieri, M., Joyce, B., Leavitt, B., Baenziger, P.S., Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crops Res. 217 (2018), 82–92.
23. Friedl, M.A., Brodley, C.E., Decision tree classification of land cover from remotely sensed data. Rem. Sens. Environ. 61 (1997), 399–409.
24. Gamon, J.A., Penuelas, J., Field, C.B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Rem. Sens. Environ. 41 (1992), 35–44.
25. Gao, B.C., NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Rem. Sens. Environ. 58 (1996), 257–266.
26. Ghaffari, R., Zhang, F., Liescu, D., Hines, E., Leeson, M., Napier, R., Clarkson, J., Early Detection of Diseases in Tomato Crops: An Electronic Nose and Intelligent Systems Approach. 2010, IEEE, Barcelona, Spain.
27. Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., Novel algorithms for remote estimation of vegetation fraction. Rem. Sens. Environ. 80 (2002), 76–87.
28. Gitelson, A.A., Merzlyak, M.N., Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J. Plant Physiol. 148 (1996), 494–500.
29. Goel, P.K., Prasher, S.O., Landry, J.A., Patel, R.M., Bonnell, R.B., Viau, A.A., Miller, J.A., Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Comput. Electron. Agric. 39 (2003), 63–93.
30. Gramling, J.M., Potential effects of laurel wilt on the flora of North America. Southeast. Nat. 9 (2010), 827–836.
31. Gunkel, G., Kosmol, J., Sobral, M., Rohn, H., Montenegro, S., Aureliano, J., Sugar cane industry as a source of water pollution - case study on the situation in Ipojuca river, Pernambuco, Brazil. Water Air Soil Pollut. 180 (2007), 261–269.
32. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Rem. Sens. Environ. 81 (2002), 416–426.
33. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Rem. Sens. Environ. 90 (2004), 337–352.
34. Hallik, L., Kazantsev, T., Kuusk, A., Galmes, J., Tomas, M., Niinemets, U., Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain). Reg. Environ. Change 17 (2017), 2097–2109.
35. Han, J., Kamner, M., Pei, J., Data Mining. Concepts and Techniques. 3rd ed., 2012, Morgan Kaufmann Publishers, Elsevier Inc., Waltham, MA, USA.
36. Hanula, J.L., Mayfield, A.E. III, Fraedrich, S.W., Rabaglia, R.J., 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. Entomol. 101 (2008), 1276–1286.
37. Heim, R.H.J., Wright, I.J., Chang, H.C., Carnegie, A.J., Pegg, G.S., Lancaster, E.K., Falster, D.S., Oldeland, J., Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning. Plant. Pathol. 67 (2018), 1114–1121.
38. Hunt, E.R. Jr., Rock, B.N., Detection of changes in leaf water content using near- and middle-infrared reflectances. Rem. Sens. Environ. 30 (1989), 43–54.
39. Jordan, C.F., Derivation of leaf area index from quality of light. Ecology, 1969.
40. Keranen, M., Aro, E.-M., Tyystjarvi, E., Nevalainen, O., Automatic plant identification with chlorophyll fluorescence fingerprinting. Precis. Agric. 4 (2003), 53–67.
41. Lopez-Granados, F., Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res. 51 (2011), 1–11.
42. López-Granados, F., Torres-Sánchez, J., de Castro, A.I., Serrano-Pérez, A., Mesas-Carrascosa, J.M., Peña, J., Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agron. Sustain. Dev. 36 (2016), 36–67.
43. Lopez-Lopez, M., Calderon Gonzalez-Dugo, R., Zarco-Tejada, P.J., Fereres, E., Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Rem. Sens., 2016, 8.
44. Lu, J.Z., Ehsani, R., Shi, Y.Y., Abdulridha, J., de Castro, A.I., Xu, Y.J., Field detection of anthracnose crown rot in strawberry using spectroscopy technology. Comput. Electron. Agric. 135 (2017), 289–299.
45. Lu, J.Z., Ehsani, R., Shi, Y.Y., de Castro, A.I., Wang, S., Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Sci. Rep., 2018, 8.
46. Luvisi, A., Ampatzidis, Y.G., De Bellis, L., Plant pathology and information technology: opportunity for management of disease outbreak and applications in regulation frameworks. Sustainability, 2016, 8.
47. 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. 11 (2010), 413–431.
48. 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. 133 (2012), 197–209.
49. Mahlein, A.-K., Steiner, U., Hillnhuetter, C., Dehne, H.-W., Oerke, E.-C., Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8, 2012.
50. Merton, R., 1998. Monitoring Community Hysteresis Using Spectral Shift Analysis and the Red-Edge Vegetation Stress Index, JPL Airborne Earth Science Workshop. NASA, Jet Propulsion Laboratory, Pasadena, California, USA.
51. Moshou, D., Bravo, C., West, J., Wahlen, T., McCartney, A., Ramon, H., Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 44 (2004), 173–188.
52. Neuwirthova, E., Lhotakova, Z., Albrechtova, J., The effect of leaf stacking on leaf reflectance and vegetation indices measured by contact probe during the season. Sensors, 17, 2017.
53. Nie, P.C., Dong, T., He, Y., Xiao, S.P., Research on the effects of drying temperature on nitrogen detection of different soil types by Near Infrared Sensors. Sensors, 2018, 18.
54. Oerke, E.C., Dehne, H.W., Safeguarding production - losses in major crops and the role of crop protection. Crop Prot. 23 (2004), 275–285.
55. Osborne, S.L., Schepers, J.S., Francis, D.D., Schlemmer, M.R., Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agron. J. 94 (2002), 1215–1221.
56. Pal, M., Mather, P.M., An assessment of the effectiveness of decision tree methods for land cover classification. Rem. Sens. Environ. 86 (2003), 554–565.
57. Peña-Barragan, J.M., López-Granados, F., Jurado-Expósito, M., García-Torres, L., Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage. Weed Res. 46:1 (2006), 10–21.
58. Peña-Barragan, J.M., Ngugi, M.K., Plant, R.E., Six, J., Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Rem. Sens. Environ. 115 (2011), 1301–1316.
59. Peñuelas, J., Baret, F., Filella, I., Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica 31 (1995), 221–230.
60. Peñuelas, J., Llusia, J., Pinol, J., Filella, I., Photochemical reflectance index and leaf photosynthetic radiation-use-efficiency assessment in Mediterranean trees. Int. J. Rem. Sens. 18 (1997), 2863–2868.
61. Peñuelas, J., Pinol, J., Ogaya, R., Filella, I., Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int. J. Rem. Sens. 18 (1997), 2869–2875.
62. Ploetz, R.C., Perez-Martinez, 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. Pathol. 61 (2012), 801–808.
63. Ploetz, R., Schaffer, B., Vargas, A., Konkol, J., Salvatierra, J., Inch, S., Campbell, A., Wideman, R., Physiological impacts of laurel wilt on avocado. Phytopathology, 103, 2013, 114.
64. Ploetz, R.C., Kendra, P.E., Choudhury, R.A., Rollins, J.A., Campbell, A., Garrett, K., Hughes, M., Dreaden, T., Laurel wilt in natural and agricultural ecosystems: understanding the drivers and scales of complex pathosystems. Forests, 8, 2017.
65. Prabhakar, M., Prasad, Y.G., Thirupathi, M., Sreedevi, G., Dharajothi, B., Venkateswarlu, B., Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Comput. Electron. Agric. 79 (2011), 189–198.
66. Ranson, K.J., Daughtry, C.S.T., Biehl, L.L., Sun angle, view angle, and background effects on spectral response of simulated balsam fir canopies. Photogramm. Eng. Rem. Sens. 52 (1986), 649–658.
67. 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. Curr. Sci. 98 (2010), 1365–1369.
68. Roujean, J.L., Breon, F.M., Estimating par absorbed by vegetation from bidirectional reflectance measurements. Rem. Sens. Environ. 51 (1995), 375–384.
69. Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W., 1973. Monitering vegetation system in the great plain with ERTS. In: Third symposium, NASA.
70. Salgadoe, A.S.A., Robson, A.J., Lamb, D.W., Dann, E.K., Searle, C., Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis. Rem. Sens., 10, 2018.
71. 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 Dis. 96 (2012), 1683–1689.
72. Sanyal, P., Patel, S.C., Pattern recognition method to detect two diseases in rice plants. Imaging Sci. J. 56 (2008), 319–325.
73. Sims, D.A., Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Rem. Sens. Environ. 81 (2002), 337–354.
74. Strachan, I.B., Pattey, E., Boisvert, J.B., Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Rem. Sens. Environ. 80 (2002), 213–224.
75. Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Van Der Meer, B., Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Rem. Sens. Environ. 91:3–4 (2004), 354–376.
76. Tilling, A.K., O'Leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., Belford, R., Remote sensing of nitrogen and water stress in wheat. Field Crops Res. 104 (2007), 77–85.
77. Wang, F.-M., Huang, J.-F., Xu, J.-F., Wang, X.-Z., Wavebands selection tor rice information extraction based on spectral bands inter-correlation. Spectroscopy Spectral Anal. 28 (2008), 1098–1101.
78. Yang, P., Mlynczak, M.G., Wei, H., Kratz, D.P., Baum, B.A., Hu, Y.X., Wiscombe, W.J., Heidinger, A., Mishchenko, M.I., Spectral signature of ice clouds in the far-infrared region: single-scattering calculations and radiative sensitivity study. J. Geophys. Res.-Atmos., 108, 2003.
79. Zhang, Y., Cong, Q., Xie, Y., Zhao, B., Zhang, Y., Cong, Q., Xie, Y.F., Zhao, B., Progress in application of near infrared spectroscopy technology in agriculture. Trans. Chin. Soc. Agric. Eng. 23 (2007), 285–290.
80. Zhao, D.L., Reddy, K.R., Kakani, G., Reddy, V.R., Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. Eur. J. Agron. 22 (2005), 391–403.
81. Zhao, Y.R., Li, X.L., Yu, K.Q., Cheng, F., He, Y., Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease. Sci. Rep., 6, 2016.