Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

  • Jaafar Abdulridha
  • Yiannis AmpatzidisEmail author
  • Sri Charan Kakarla
  • Pamela Roberts


Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.


Disease detection Hyperspectral Remote sensing Classification methods UAV Spectral vegetation indices 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Abdulridha, J., Ampatzidis, Y., Ehsani, R., & de Castro, A. (2018). 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,155, 203–211. Scholar
  2. Abdulridha, J., Ehsani, R., Abd-Elrahma, A., & Ampatzidis, Y. (2019). A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Computers and Electronics in Agriculture,156, 549–557. Scholar
  3. Abdulridha, J., Ehsani, R., & de Castro, A. (2016). Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique. Agriculture-Basel,6(4), 13. Scholar
  4. Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilve, H., et al. (2017). Detection of flavescence doree grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 9(4), 308. Scholar
  5. Ampatzidis, Y., De Bellis, L., & Luvisi, A. (2017). iPathology: Robotic applications and management of plants and pant diseases. Sustainability,9(6), 1010. Scholar
  6. Ampatzidis, Y., Kiner, J., Abdolee, R., & Ferguson, L. (2018). Voice-controlled and wireless solid set canopy delivery (VCW-SSCD) system for mist-cooling. Sustainability,10(2), 421. Scholar
  7. Ampatzidis, Y., & Partel, V. (2019). UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence. Remote Sensing,11(4), 410. Scholar
  8. Ampatzidis, Y., Partel, V., Meyering, B., & Albrecht, U. (2019). Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence. Computers and Electronics in Agriculture,164, 104900. Scholar
  9. Ampatzidis, Y., Tan, L., Haley, R., & Whiting, M. D. (2016). Cloud-based harvest management information system for hand-harvested specialty crops. Computers and Electronics in Agriculture,122, 161–167. Scholar
  10. Ampatzidis, Y. G., & Vougioukas, S. G. (2009). Field experiments for evaluating the incorporation of RFID and barcode registration and digital weighing technologies in manual fruit harvesting. Computers and Electronics in Agriculture,66(2), 166–172. Scholar
  11. Ampatzidis, Y. G., Whiting, M. D., Scharf, P. A., & Zhang, Q. (2012). Development and evaluation of a novel system for monitoring harvest labor efficiency. Computers and Electronics in Agriculture,88, 85–94. Scholar
  12. Babar, M. A., Reynolds, M. P., Van Ginkel, M., Klatt, A. R., Raun, W. R., & Stone, M. L. (2006). Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science,46(3), 1046–1057. Scholar
  13. Barbedo, J. G. A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus,2, 660. Scholar
  14. Barnes, J. D., Balaguer, L., Manrique, E., Elvira, S., & Davison, A. W. (1992). A reappraisal of the use of DMSO for the extraction and determination of chlorophylls-A and chlorophylls-B in lichens and higher-plants. Environmental and Experimental Botany,32(2), 85–100. Scholar
  15. Bausch, W. C., & Duke, H. R. (1996). Remote sensing of plant nitrogen status in corn. Transactions of the ASAE,39(5), 1869–1875.CrossRefGoogle Scholar
  16. Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing,19(4), 657–675. Scholar
  17. Broge, N. H., & Leblanc, E. (2001). 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,76(2), 156–172. Scholar
  18. Burks, T. F., Shearer, S. A., & Payne, F. A. (2000). Classification of weed species using color texture features and discriminant analysis. Transactions of the ASAE,43(2), 441–448.CrossRefGoogle Scholar
  19. Calderon, R., Navas-Cortes, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). 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,139, 231–245. Scholar
  20. Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). Ration analysis of reflectance spectra (RARS)—An algorithm for the remote estimation concentration of chlorophyll-a, chlorophyll-b, and carotenoid soybean leaves. Remote Sensing of Environment,39(3), 239–247. Scholar
  21. Cruz, A., Ampatzidis, Y., Pierro, R., Materazzi, A., Panattoni, A., De Bellis, L., et al. (2019). Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Computers and Electronics in Agriculture,157, 63–76. Scholar
  22. Cruz, A. C., Luvisi, A., De Bellis, L., & Ampatzidis, Y. (2017). X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion. Frontiers in Plant Science,8, 1741. Scholar
  23. Das, R., & Sengur, A. (2010). Evaluation of ensemble methods for diagnosing of valvular heart disease. Expert Systems with Applications,37(7), 5110–5115. Scholar
  24. Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., & Dungey, H. S. (2017). Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing,131, 1–14. Scholar
  25. De Tomás, A., Nieto, H., Guzinski, R., Mendiguren, G., Sandholt, I., & Berline, P. (2012). In multi-scale approach of the surface temperature/vegetation index triangle method for estimating evapotranspiration over heterogeneous landscapes. EGU General Assembly,101, 131–138.Google Scholar
  26. Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture,10(6), 459–470. Scholar
  27. Di Gennaro, S. F., Battiston, E., Di Marco, S., Facini, O., Matese, A., Nocentini, M., et al. (2016). Unmanned aerial vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex. Phytopathologia Mediterranea,55(2), 262–275. Scholar
  28. Filella, I., Zhang, C., Seco, R., Potosnak, M., Guenther, A., Karl, T., et al. (2018). A MODIS photochemical reflectance index (PRI) as an estimator of isoprene emissions in a temperate deciduous forest. Remote Sensing, 10(4), 557. Scholar
  29. Foody, G. M. (2004). Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. International Journal of Remote Sensing,25(15), 3091–3104. Scholar
  30. Franke, J., Menz, G., Oerke, E. C., & Rascher, U. (2005). Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants. In G. D. U. Manfred Owe (Ed.), SPIE-volume 5976 remote sensing for agriculture, ecosystems, and hydrology VII (p. 59761D). Washington, USA: SPIE - The International Society for Optical Engineering.CrossRefGoogle Scholar
  31. Galvez, J. F., Mejuto, J. C., & Simal-Gandara, J. (2018). Future challenges on the use of blockchain for food traceability analysis. TrAC, Trends in Analytical Chemistry,107, 222–232. Scholar
  32. Gamon, J. A., Penuelas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment,41(1), 35–44. Scholar
  33. Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology,160(3), 271–282. Scholar
  34. Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment,80(1), 76–87. Scholar
  35. Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology,148(3–4), 494–500.CrossRefGoogle Scholar
  36. Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology,74(1), 38–45.;2.CrossRefPubMedGoogle Scholar
  37. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). 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,90(3), 337–352. Scholar
  38. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment,81(2–3), 416–426. Scholar
  39. Hariharan, J., Fuller, J., Ampatzidis, Y., Abdulridha, J., & Lerwill, A. (2019). Finite difference analysis and bivariate correlation of hyperspectral data for detecting laurel wilt disease and nutritional deficiency in avocado. Remote Sensing,11(15), 1748. Scholar
  40. Huang, H. S., Deng, J. Z., Lan, Y. B., Yang, A. Q., Zhang, L., Wen, S., et al. (2019). Detection of helminthosporium leaf blotch disease based on UAV imagery. Applied Sciences-Basel, 9(3), 558. Scholar
  41. Huberty, C. J. (1984). Issues in the use and interpretation of discriminant-analysis. Psychological Bulletin,95(1), 156–171. Scholar
  42. Hunt, E. R., Jr., & Rock, B. N. (1989). Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment,30(1), 43–54.CrossRefGoogle Scholar
  43. Jacquemoud, S., & Baret, F. (1990). Prospect—A model of leaf optical-properties spectra. Remote Sensing of Environment,34(2), 75–91. Scholar
  44. Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology,50, 663–666.CrossRefGoogle Scholar
  45. Liao, Y. Y., Strayer-Scherer, A. L., White, J., Mukherjee, A., De la Torre-Roche, R., Ritchie, L., et al. (2019). Nano-magnesium oxide: A novel bactericide against copper-tolerant Xanthomonas perforans causing tomato bacterial spot. Phytopathology,109(1), 52–62. Scholar
  46. Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods,13, 80. Scholar
  47. Lu, J. Z., Ehsani, R., Shi, Y. Y., de Castro, A. I., & Wang, S. (2018). Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. Scientific Reports, 8, 2793. Scholar
  48. Luvisi, A., Ampatzidis, Y. G., & De Bellis, L. (2016). Plant pathology and information technology: Opportunity for management of disease outbreak and applications in regulation frameworks. Sustainability, 8(8), 831. Scholar
  49. MacKenzie, K. J., Sumabat, L. G., Xavier, K. V., & Vallad, G. E. (2018). A review of corynespora cassiicola and its increasing relevance to tomato in Florida. Plant Health Progress,19, 303–309. Scholar
  50. Mahlein, A. K. (2016). Plant disease detection by imaging sensors—Parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease,100(2), 241–251. Scholar
  51. Mahlein, A. K., Steiner, U., Dehne, H. W., & Oerke, E. C. (2010). Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture,11(4), 413–431. Scholar
  52. 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, USAGoogle Scholar
  53. Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing,24(14), 2855–2877. Scholar
  54. Naidu, R. A., Perry, E. M., Pierce, F. J., & Mekuria, T. (2009). 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,66(1), 38–45. Scholar
  55. Onofre, R. B., Rebello, C. S., Mertely, J. C., & Peres, N. A. (2019). First report of target spot caused by Corynespora cassiicola on strawberry in North America. Plant Disease,103(6), 1412. Scholar
  56. Partel, V., Kakarla, C., & Ampatzidis, Y. (2019a). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture,157, 339–350. Scholar
  57. Partel, V., Nunes, L., Stansly, P., & Ampatzidis, Y. (2019b). Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence. Computer and Electronics in Agriculture,162, 328–336.CrossRefGoogle Scholar
  58. Penuelas, J., Baret, F., & Filella, I. (1995). Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica,31(2), 221–230.Google Scholar
  59. Penuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing,14(10), 1887–1905.CrossRefGoogle Scholar
  60. Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indexes associated with physiological-changes in nitrogen-limited and water-limited sunflower leaves. Remote Sensing of Environment,48(2), 135–146. Scholar
  61. Penuelas, J., Pinol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing,18(13), 2869–2875. Scholar
  62. Pernezny, K., Datnoff, L. E., Mueller, T., & Collins, J. (1996). Losses in fresh-market tomato production in Florida due to target spot and bacterial spot and the benefits of protectant fungicides. Plant Disease,80(5), 559–563. Scholar
  63. Pernezny, K., & Raid, R. N. (2001). Occurrence of bacterial leaf spot of Escarole caused by Pseudomonas cichorii in the Everglades agricultural area of Southern Florida. Plant Disease, 85(11), 1208. Scholar
  64. Pernezny, K., Stoffella, P., Collins, J., Carroll, A., & Beaney, A. (2002). Control of target spot of tomato with fungicides, systemic acquired resistance activators, and a biocontrol agent. Plant Protection Science,38(3), 81–88.CrossRefGoogle Scholar
  65. Potnis, N., Timilsina, S., Strayer, A., Shantharaj, D., Barak, J. D., Paret, M. L., et al. (2015). Bacterial spot of tomato and pepper: Diverse Xanthomonas species with a wide variety of virulence factors posing a worldwide challenge. Molecular Plant Pathology,16(9), 907–920. Scholar
  66. Pydipati, R., Burks, T. F., & Lee, W. S. (2006). Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture,52(1–2), 49–59. Scholar
  67. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., et al. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal,93(1), 131–138. Scholar
  68. Reynolds, C. F., Kupfer, D. J., Houck, P. R., Hoch, C. C., Stack, J. A., Berman, S. R., et al. (1988). Reliable discrimination of elderly depressed and demented patients by electroencephalographic sleep data. Archives of General Psychiatry,45(3), 258–264.CrossRefGoogle Scholar
  69. Roujean, J. L., & Breon, F. M. (1995). Estimating par absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment,51(3), 375–384. Scholar
  70. Salami, E., Barrado, C., & Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing,6(11), 11051–11081. Scholar
  71. Schlub, R., L, Smith, L., J, Datnoff, L., E, & Pernezny, K. (2007). An overview of target spot of tomato caused by Corynespora cassiicola. ll International Symposium on Tomato Disease SHS Acta Horticulturae, p. 808.Google Scholar
  72. Sharma, S., & Bhattarai, K. (2019). Progress in developing bacterial spot resistance in tomato. Agronomy-Basel, 9(1), 26. Scholar
  73. Shazia, A., Khan, S. M., Khan, M. F., Hameed, U., et al. (2018). Antifungal activity of different systemic fungicide against Fusarium oxysporum f. sp. Lycopersici associated with tomato wilt and emergence of resistance in pathogen. Pakistan Journal of Phytopathology,30(2), 169–176.CrossRefGoogle Scholar
  74. Shi, Y., Huang, W. J., Ye, H. C., Ruan, C., Xing, N. C., Geng, Y., et al. (2018). Partial least square discriminant analysis based on normalized two-stage vegetation indices for mapping damage from rice diseases using PlanetScope datasets. Sensors, 18(6), 1901. Scholar
  75. Singh, S. R., & Allen, D. J. (1979). Cowpea pests and diseases (Vol. 2). Ibadan, Nigeria: International Institute of Tropical Agriculture.Google Scholar
  76. Smigaj, M., Gaulton, R., Barr, S. L, & Suárez, J. C. (2015). UAV-borne thermal imaging for forest health monitoring: Detection of disease induced canopy temperature increase. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (Vol. XL-3/W3).Google Scholar
  77. Sukhova, E., & Sukhov, V. (2018). Connection of the photochemical reflectance index (PRI) with the photosystem II quantum yield and nonphotochemical quenching can be dependent on variations of photosynthetic parameters among investigated plants: A meta-analysis. Remote Sensing, 10(5), 771. Scholar
  78. Tamouridou, A. A., Pantazi, X. E., Alexandridis, T., Lagopodi, A., Kontouris, G., & Moshou, D. (2018). Spectral identification of disease in weeds using multilayer perceptron with automatic relevance determination. Sensors, 18(9), 2770. Scholar
  79. Thomas, S., Kuska, M. T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., et al. (2018). Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. Journal of Plant Diseases and Protection,125(1), 5–20. Scholar
  80. Vincini, M., Frazzi, E., & D’Alessio, P. (2007). Comparison of narrow-band and broad-band vegetation indexes for canopy chlorophyll density estimation in sugar beet. In J. V. Stafford (Ed.), Precision agriculture ‘07: Proceedings of the 6th European Conference on Precision Agriculture (pp. 189–196). Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
  81. Vivaldini, K. C. T., Martinelli, T. H., Guizilini, V. C., Souza, J. R., Oliveira, M. D., Ramos, F. T., et al. (2019). UAV route planning for active disease classification. Autonomous Robots,43(5), 1137–1153. Scholar
  82. Wang, F.-M., Huang, J.-F., Xu, J.-F., & Wang, X.-Z. (2008). Wavebands selection tor rice information extraction based on spectral bands inter-correlation. Spectroscopy and Spectral Analysis,28(5), 1098–1101.PubMedGoogle Scholar
  83. Zarco-Tejada, P. J., Gonzalez-Dugo, V., & Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment,117, 322–337. Scholar
  84. Zitter, T. A. (1985). Bacterial disease of tomato. Cooperative Extension. New York: Cornell University. Fact sheet page: 735.50.Google Scholar

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Authors and Affiliations

  1. 1.Agricultural and Biological Engineering Department, Southwest Florida Research and Education CenterUniversity of FloridaImmokaleeUSA
  2. 2.Plant Pathology Department, Southwest Florida Research and Education CenterUniversity of FloridaImmokaleeUSA

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