Plant Stress Detection by Thermography: Comparison
Please note this is a comparison between versions V2 by Rita Xu and V1 by Marisa Pérez-Bueno.

Leaf and canopy temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Thermography, often combined with other imaging sensors and data-mining techniques, is crucial in the implementation of a more automatized, precise and sustainable agriculture.

  • Remote sensing
  • proximal sensing
  • biotic stress
  • plant stress
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  1. Stevanović, M.; Popp, A.; Lotze-Campen, H.; Dietrich, J.P.; Müller, C.; Bonsch, M.; Schmitz, C.; Bodirsky, B.L.; Humpenöder, F.; Weindl, I. The impact of high-end climate change on agricultural welfare. Science Advances 2016, 2, e1501452; DOI:10.1126/sciadv.1501452.
  2. Carvajal-Yepes, M.; Cardwell, K.; Nelson, A.; Garrett, K.A.; Giovani, B.; Saunders, D.G.O.; Kamoun, S.; Legg, J.P.; Verdier, V.; Lessel, J., et al. A global surveillance system for crop diseases. Science 2019, 364, 1237-1239; DOI:10.1126/science.aaw1572.
  3. Zhan, J.; Thrall, P.H.; Papaïx, J.; Xie, L.; Burdon, J.J. Playing on a Pathogen's Weakness: Using Evolution to Guide Sustainable Plant Disease Control Strategies. Annu. Rev. Phytopathol. 2015, 53, 19-43; DOI:10.1146/annurev-phyto-080614-120040.
  4. 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; DOI:10.1094/pdis-03-15-0340-fe.
  5. Jones, H. Plants and microclimate: a quantitative approach to environmental plant physiology, Third Edition ed.; Cambridge University Press: United Kingdom, 2014; Vol. 56.
  6. Prashar, A.; Yildiz, J.; McNicol, J.W.; Bryan, G.J.; Jones, H.G. Infra-red thermography for high throughput field phenotyping in Solanum tuberosum. PLoS One 2013, 8, e65816; DOI:10.1371/journal.pone.0065816.
  7. Fuchs, M.; Tanner, C.B. Infrared thermometry of vegetation. Agron. J. 1966, 58, 597-601.
  8. Milthorpe, F.L.; Spencer, E.J. Experimental studies of the factors controlling transpiration. J. Exp. Bot. 1957, 8, 413-437; DOI:10.1093/jxb/8.3.413.
  9. Scarth, G.W.; Loewy, A.; Shaw, M. Use of the infrared total absorption method for estimating the time course of photosynthesis and transpiration. Canadian Journal of Research 1948, 26c, 94-107; DOI:10.1139/cjr48c-010.
  10. Jones, H.G. Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant, Cell and Environment 1999, 22, 1043-1055; DOI:10.1046/j.1365-3040.1999.00468.x.
  11. Jones, H.G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. In Adv. Bot. Res., Academic Press: 2004; Vol. 41, pp. 107-163.
  12. Ishimwe, R.; Abutaleb, K.; Ahmed, F. Applications of thermal imaging in agriculture - A review. Advances in Remote Sensing 2014, 3, 13; DOI:10.4236/ars.2014.33011.
  13. Khanal, S.; Fulton, J.; Shearer, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 2017, 139, 22-32; DOI:10.1016/j.compag.2017.05.001.
  14. Chaerle, L.; Van der Straeten, D. Seeing is believing: imaging techniques to monitor plant health. Biochim. Biophys. Acta 2001, 1519, 153-166.
  15. Zeng, W.; Melotto, M.; He, S.Y. Plant stomata: a checkpoint of host immunity and pathogen virulence. Curr. Opin. Biotechnol. 2010, 21, 599-603; DOI:10.1016/j.copbio.2010.05.006.
  16. Roitsch, T.; Cabrera-Bosquet, L.; Fournier, A.; Ghamkhar, K.; Jiménez-Berni, J.A.; Pinto, F.; Ober, E.S. Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Sci. 2019, 282, 2-10; DOI:10.1016/j.plantsci.2019.01.011.
  17. Sperschneider, J. Machine learning in plant–pathogen interactions: empowering biological predictions from field scale to genome scale. New Phytol. 2019, 10.1111/nph.15771; DOI:10.1111/nph.15771.
  18. Saglam, A.; Chaerle, L.; Van Der Straeten, D.; Valcke, R. Promising monitoring techniques for plant science: Thermal and chlorophyll fluorescence imaging. In Photosynthesis, Productivity and Environmental Stress, 2019; 10.1002/9781119501800.ch12pp. 241-266.
  19. Singh, A.; Ganapathysubramanian, B.; Singh, A.K.; Sarkar, S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2016, 21, 110-124; DOI:10.1016/j.tplants.2015.10.015.
  20. Gao, Z.; Luo, Z.; Zhang, W.; Lv, Z.; Xu, Y. Deep learning application in plant stress imaging: A review. AgriEngineering 2020, 2, 430-446; DOI:10.3390/agriengineering2030029.
  21. Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors (Basel) 2018, 18, 2674; DOI:10.3390/s18082674.
  22. Still, C.; Powell, R.; Aubrecht, D.; Kim, Y.; Helliker, B.; Roberts, D.; Richardson, A.D.; Goulden, M. Thermal imaging in plant and ecosystem ecology: applications and challenges. Ecosphere 2019, 10, e02768; DOI:10.1002/ecs2. 2768.
  23. Jones, H.G. Thermal imaging and infrared sensing in plant ecophysiology. In Advances in Plant Ecophysiology Techniques, Springer: 2018; 10.1016/S0065-2296(04)41003-9pp. 135-151.
  24. Messina, G.; Modica, G. Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sens. 2020, 12, 1491; DOI:10.3390/rs12091491.
  25. Vialet-Chabrand, S.; Lawson, T. Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment. J. Exp. Bot. 2019, 70, 2839-2855; DOI:10.1093/jxb/erz068.
  26. Kelly, J.; Kljun, N.; Olsson, P.-O.; Mihai, L.; Liljeblad, B.; Weslien, P.; Klemedtsson, L.; Eklundh, L. Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera. Remote Sens. 2019, 11, 567; DOI:10.3390/rs11050567.
  27. Prashar, A.; Jones, H.G. Infra-red thermography as a high-throughput tool for field phenotyping. Agronomy 2014, 4, 397-417.
  28. Sagan, V.; Maimaitijiang, M.; Sidike, P.; Eblimit, K.; Peterson, K.T.; Hartling, S.; Esposito, F.; Khanal, K.; Newcomb, M.; Pauli, D. UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap cameras. Remote Sens. 2019, 11, 330; DOI:10.3390/rs11030330.
  29. Zhang, J.; Huang, Y.; Pu, R.; Gonzalez-Moreno, P.; Yuan, L.; Wu, K.; Huang, W. Monitoring plant diseases and pests through remote sensing technology: A review. Comput. Electron. Agric. 2019, 165, 104943; DOI:10.1016/j.compag.2019.104943.
  30. Li, Z.; Guo, R.; Li, M.; Chen, Y.; Li, G. A review of computer vision technologies for plant phenotyping. Comput. Electron. Agric. 2020, 176, 105672; DOI:10.1016/j.compag.2020.105672.
  31. Costa, J.M.; Marques da Silva, J.; Pinheiro, C.; Barón, M.; Mylona, P.; Centritto, M.; Haworth, M.; Loreto, F.; Uzilday, B.; Turkan, I., et al. Opportunities and limitations of crop phenotyping in southern European countries. Front. Plant Sci. 2019, 10, 1125; DOI:10.3389/fpls.2019.01125.
  32. Barbedo, J.G.A. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 2019, 3, 40; DOI:10.3390/drones3020040.
  33. Maes, W.H.; Steppe, K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 2019, 24, 152-164; DOI:10.1016/j.tplants.2018.11.007.
  34. Zhang, C.; Valente, J.; Kooistra, L.; Guo, L.; Wang, W. Opportunities of UAVs in orchard management. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2019, XLII-2/W13, 673-680; DOI:10.5194/isprs-archives-XLII-2-W13-673-2019.
  35. Pineda, M.; Barón, M.; Pérez-Bueno, M.-L. Thermal imaging for plant stress detection and phenotyping. Remote Sens. 2021, 13, 68; DOI:10.3390/rs13010068.
  36. Sawinski, K.; Mersmann, S.; Robatzek, S.; Bohmer, M. Guarding the green: pathways to stomatal immunity. Mol. Plant Microbe In. 2013, 26, 626-632; DOI:10.1094/MPMI-12-12-0288-CR.
  37. Agurla, S.; Raghavendra, A.S. Convergence and divergence of signaling events in guard cells during stomatal closure by plant hormones or microbial elicitors. Front. Plant Sci. 2016, 7, 1332; DOI:10.3389/fpls.2016.01332.
  38. Barón, M.; Pineda, M.; Pérez-Bueno, M.L. Picturing pathogen infection in plants. Z. Naturforsch. C Bio. Sci. 2016, 71, 355-368; DOI:10.1515/znc-2016-0134.
  39. Grimmer, M.K.; John Foulkes, M.; Paveley, N.D. Foliar pathogenesis and plant water relations: a review. J. Exp. Bot. 2012, 63, 4321-4331; DOI:10.1093/jxb/ers143.
  40. Smigaj, M.; Gaulton, R.; Suárez, J.C.; Barr, S.L. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity. Forest Ecol. Manag. 2019, 433, 699-708; DOI:10.1016/j.foreco.2018.11.032.
  41. Montero, R.; Pérez-Bueno, M.L.; Barón, M.; Florez-Sarasa, I.; Tohge, T.; Fernie, A.R.; El Aou Ouad, H.; Flexas, J.; Bota, J. Alterations in primary and secondary metabolism in Vitis vinifera 'Malvasía de Banyalbufar' upon infection with Grapevine leafroll associated virus 3 (GLRaV-3). Physiol. Plant. 2016, 157, 442-452; DOI:10.1111/ppl.12440.
  42. Chaerle, L.; Hagenbeek, D.; De Bruyne, E.; Valcke, R.; Van der Straeten, D. Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol. 2004, 45, 887-896; DOI:10.1093/pcp/pch097.
  43. Chaerle, L.; Pineda, M.; Romero-Aranda, R.; Van der Straeten, D.; Barón, M. Robotized thermal and chlorophyll fluorescence imaging of Pepper mild mottle virus infection in Nicotiana benthamiana. Plant Cell Physiol. 2006, 47, 1323-1336; DOI:10.1093/pcp/pcj102.
  44. Berdugo, C.A.; Zito, R.; Paulus, S.; Mahlein, A.K. Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathol. 2014, 63, 1344-1356; DOI:10.1111/ppa.12219.
  45. Wang, L.; Poque, S.; Valkonen, J.P. Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant Methods 2019, 15, 116; DOI:10.1186/s13007-019-0501-1.
  46. Schurr, U.; Schuberth, B.; Aloni, R.; Pradel, K.S.; Schmundt, D.; Jahne, B.; Ullrich, C.I. Structural and functional evidence for xylem-mediated water transport and high transpiration in Agrobacterium tumefaciens-induced tumors of Ricinus communis. Botanica Acta 1996, 109, 405-411; DOI:10.1111/j.1438-8677.1996.tb00590.x.
  47. Boccara, M.; Boue, C.; Garmier, M.; De Paepe, R.; Boccara, A.C. Infra-red thermography revealed a role for mitochondria in pre-symptomatic cooling during harpin-induced hypersensitive response. Plant J. 2001, 28, 663-670; DOI:10.1046/j.1365-313x.2001.01186.x.
  48. Maes, W.H.; Minchin, P.E.H.; Snelgar, W.P.; Steppe, K. Early detection of Psa infection in kiwifruit by means of infrared thermography at leaf and orchard scale. Funct. Plant Biol. 2014, 41, 1207-1220; DOI:10.1071/fp14021.
  49. Zheng, X.Y.; Spivey, N.W.; Zeng, W.; Liu, P.P.; Fu, Z.Q.; Klessig, D.F.; He, S.Y.; Dong, X. Coronatine promotes Pseudomonas syringae virulence in plants by activating a signaling cascade that inhibits salicylic acid accumulation. Cell Host and Microbe 2012, 11, 587-596; DOI:10.1016/j.chom.2012.04.014.
  50. Pérez-Bueno, M.L.; Pineda, M.; Díaz-Casado, E.; Barón, M. Spatial and temporal dynamics of primary and secondary metabolism in Phaseolus vulgaris challenged by Pseudomonas syringae. Physiol. Plant. 2015, 153, 161-174; DOI:10.1111/ppl.12237.
  51. Pérez-Bueno, M.L.; Granum, E.; Pineda, M.; Flors, V.; Rodríguez-Palenzuela, P.; López-Solanilla, E.; Barón, M. Temporal and spatial resolution of activated plant defense responses in leaves of Nicotiana benthamiana infected with Dickeya dadantii. Front. Plant Sci. 2016, 6, 1209; DOI:10.3389/fpls.2015.01209.
  52. Pineda, M.; Pérez-Bueno, M.L.; Barón, M. Detection of bacterial infection in melon plants by classification methods based on imaging data. Front. Plant Sci. 2018, 9, 164; DOI:10.3389/fpls.2018.00164.
  53. Pérez-Bueno, M.L.; Pineda, M.; Cabeza, F.; Barón Ayala, M. Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Front. Plant Sci. 2016, 7, 1790; DOI:10.3389/fpls.2016.01790.
  54. Pineda, M.; Luisa Perez-Bueno, M.; Paredes, V.; Baron, M. Use of multicolour fluorescence imaging for diagnosis of bacterial and fungal infection on zucchini by implementing machine learning. Funct. Plant Biol. 2017, 44, 563-572; DOI:10.1071/FP16164.
  55. Hellebrand, H.J.; Herppich, W.B.; Beuche, H.; Dammer, K.-H.; Linke, M.; Flath, K. Investigations of plant infections by thermal vision and NIR imaging. International Agrophysics 2006, 20, 1-10.
  56. Yao, Z.; He, D.; Lei, Y. Thermal imaging for early nondestructive detection of wheat stripe rust. In Proceedings of 2018 ASABE Annual International Meeting; p. 1.
  57. Lindenthal, M.; Steiner, U.; Dehne, H.W.; Oerke, E.C. Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Phytopathology 2005, 95, 233-240; DOI:10.1094/PHYTO-95-0233.
  58. Wen, D.-M.; Chen, M.-X.; Zhao, L.; Ji, T.; Li, M.; Yang, X.-T. Use of thermal imaging and Fourier transform infrared spectroscopy for the pre-symptomatic detection of cucumber downy mildew. Eur. J. Plant Pathol. 2019, 155, 405-416; DOI:10.1007/s10658-019-01775-2.
  59. Oerke, E.C.; Steiner, U.; Dehne, H.W.; Lindenthal, M. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J. Exp. Bot. 2006, 57, 2121-2132; DOI:10.1093/jxb/erj170.
  60. Jafari, M.; Minaei, S.; Safaie, N. Detection of pre-symptomatic rose powdery-mildew and gray-mold diseases based on thermal vision. Infrared Physics and Technology 2017, 85, 170-183; DOI:10.1016/j.infrared.2017.04.023.
  61. Raza, S.; Prince, G.; Clarkson, J.P.; Rajpoot, N.M. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One 2015, 10, e0123262; DOI:10.1371/journal.pone.0123262.
  62. Oerke, E.C.; Fröhling, P.; Steiner, U. Thermographic assessment of scab disease on apple leaves. Precis. Agric. 2011, 12, 699-715; DOI:10.1007/s11119-010-9212-3.
  63. Belin, É.; Rousseau, D.; Boureau, T.; Caffier, V. Thermography versus chlorophyll fluorescence imaging for detection and quantification of apple scab. Comput. Electron. Agric. 2013, 90, 159-163; DOI:10.1016/j.compag.2012.09.014.
  64. Sandmann, M.; Grosch, R.; Graefe, J. The use of features from fluorescence, thermography, and NDVI imaging to detect biotic stress in lettuce. Plant Dis. 2018, 102, 1101-1107; DOI:10.1094/PDIS-10-17-1536-RE.
  65. Wang, M.; Ling, N.; Dong, X.; Zhu, Y.; Shen, Q.; Guo, S. Thermographic visualization of leaf response in cucumber plants infected with the soil-borne pathogen Fusarium oxysporum f. sp. cucumerinum. Plant Physiol. Biochem. 2012, 61, 153-161; DOI:10.1016/j.plaphy.2012.09.015.
  66. Rispail, N.; Rubiales, D. Rapid and efficient estimation of pea resistance to the soil-borne pathogen Fusarium oxysporum by infrared imaging. Sensors (Basel) 2015, 15, 3988-4000; DOI:10.3390/s150203988.
  67. Mastrodimos, N.; Lentzou, D.; Templalexis, C.; Tsitsigiannis, D.I.; Xanthopoulos, G. Development of thermography methodology for early diagnosis of fungal infection in table grapes: The case of Aspergillus carbonarius. Comput. Electron. Agric. 2019, 165, 104972; DOI:10.1016/j.compag.2019.104972.
  68. Baranowski, P.; Jedryczka, M.; Mazurek, W.; Babula-Skowronska, D.; Siedliska, A.; Kaczmarek, J. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One 2015, 10, e0122913; DOI:10.1371/journal.pone.0122913.
  69. Chaerle, L.; Hagenbeek, D.; Vanrobaeys, X.; Van Der Straeten, D. Early detection of nutrient and biotic stress in Phaseolus vulgaris. Int. J. Remote Sens. 2007, 28, 3479-3492; DOI:10.1080/01431160601024259.
  70. Ploetz, R.; Schaffer, B. Effects of flooding and Phytophthora root rot on net gas exchange and growth of avocado. Phytopathology 1989, 79, 204-208.
  71. Granum, E.; Pérez-Bueno, M.L.; Calderón, C.E.; Ramos, C.; de Vicente, A.; Cazorla, F.M.; Barón, M. Metabolic responses of avocado plants to stress induced by Rosellinia necatrix analysed by fluorescence and thermal imaging. Eur. J. Plant Pathol. 2015, 142, 625-632; DOI:10.1007/s10658-015-0640-9.
  72. Pérez-Bueno, M.L.; Pineda, M.; Vida, C.; Fernández-Ortuño, D.; Torés, J.A.; de Vicente, A.; Cazorla, F.M.; Barón, M. Detection of white root rot in avocado trees by remote sensing. Plant Dis. 2019, 103, 1119-1125; DOI:10.1094/PDIS-10-18-1778-RE.
  73. Aldea, M.; Hamilton, J.G.; Resti, J.P.; Zangerl, A.R.; Berenbaum, M.R.; DeLucia, E.H. Indirect effects of insect herbivory on leaf gas exchange in soybean. Plant Cell Environ. 2005, 28, 402-411; DOI:10.1111/j.1365-3040.2005.01279.x.
  74. Tang, J.Y.; Zielinski, R.E.; Zangerl, A.R.; Crofts, A.R.; Berenbaum, M.R.; DeLucia, E.H. The differential effects of herbivory by first and fourth instars of Trichoplusia ni (Lepidoptera: Noctuidae) on photosynthesis in Arabidopsis thaliana. J. Exp. Bot. 2006, 57, 527-536; DOI:10.1093/jxb/erj032.
  75. Nabity, P.D.; Zavala, J.A.; DeLucia, E.H. Herbivore induction of jasmonic acid and chemical defences reduce photosynthesis in Nicotiana attenuata. J. Exp. Bot. 2013, 64, 685-694; DOI:10.1093/jxb/ers364.
  76. Nabity, P.D.; Hillstrom, M.L.; Lindroth, R.L.; DeLucia, E.H. Elevated CO2 interacts with herbivory to alter chlorophyll fluorescence and leaf temperature in Betula papyrifera and Populus tremuloides. Oecologia 2012, 169, 905-913; DOI:10.1007/s00442-012-2261-8.
  77. Joalland, S.; Screpanti, C.; Liebisch, F.; Varella, H.V.; Gaume, A.; Walter, A. Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets. Plant Methods 2017, 13, 14; DOI:10.1186/s13007-017-0223-1.
  78. Ortiz-Bustos, C.M.; Pérez-Bueno, M.L.; Barón, M.; Molinero-Ruiz, L. Use of blue-green fluorescence and thermal imaging in the early detection of sunflower infection by the root parasitic weed Orobanche cumana Wallr. Front. Plant Sci. 2017, 8, 833; DOI:10.3389/fpls.2017.00833.
  79. Sankaran, S.; Mishra, A.; Ehsani, R.; Davis, C. A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 2010, 72, 1-13; DOI:10.1016/j.compag.2010.02.007.
  80. Schmitz, A.; Kiewnick, S.; Schlang, J.; Sikora, R.A. Use of high resolution digital thermography to detect Heterodera schachtii infestation in sugar beets. Communications in Agricultural and Applied Biological Sciences 2004, 69, 359-363.
  81. Wang, Y.; Zia-Khan, S.; Owusu-Adu, S.; Miedaner, T.; Müller, J. Early detection of Zymoseptoria tritici in winter wheat by infrared thermography. Agriculture 2019, 9, 139; DOI:10.3390/agriculture9070139.
  82. Park, J.; Kim, K.W. Outdoor infrared imaging for spatial and temporal thermography: A case study of necrotic versus healthy leaf areas on woody plants. J. Phytopathol. 2020, 10.1111/jph.12959; DOI:10.1111/jph.12959.
  83. Aldea, M.; Hamilton, J.G.; Resti, J.P.; Zangerl, A.R.; Berenbaum, M.R.; Frank, T.D.; Delucia, E.H. Comparison of photosynthetic damage from arthropod herbivory and pathogen infection in understory hardwood saplings. Oecologia 2006, 149, 221-232; DOI:10.1007/s00442-006-0444-x.
  84. Sankaran, S.; Maja, J.M.; Buchanon, S.; Ehsani, R. Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques. Sensors 2013, 13, 2117-2130; DOI:10.3390/s130202117.
  85. Zarco-Tejada, P.J.; Camino, C.; Beck, P.S.A.; Calderon, R.; Hornero, A.; Hernández-Clemente, R.; Kattenborn, T.; Montes-Borrego, M.; Susca, L.; Morelli, M., et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 2018, 4, 432-439; DOI:10.1038/s41477-018-0189-7.
  86. 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; DOI:10.3390/rs8040276.
  87. Omran, E.-S.E. Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Archives of Agronomy and Soil Science 2017, 63, 883-896; DOI:10.1080/03650340.2016.1247952.
  88. Calderón, R.; Navas-Cortés, J.A.; Zarco-Tejada, P.J. Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sens. Environ. 2015, 7, 5584-5610; DOI:10.3390/rs70505584.
  89. 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; DOI:10.1016/j.rse.2013.07.031.
  90. Calderón, R.; Montes-Borrego, M.; Landa, B.; Navas-Cortés, J.; Zarco-Tejada, P. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precis. Agric. 2014, 15, 639-661; DOI:10.1007/s11119-014-9360-y.
  91. Loladze, A.; Rodrigues, F.A.; Toledo, F.; San Vicente, F.; Gerard, B.; Boddupalli, M.P. Application of remote sensing for phenotyping tar spot complex resistance in maize. Front. Plant Sci. 2019, 10, 10; DOI:10.3389/fpls.2019.00552.
  92. Zhang, C.Y.; Chen, W.D.; Sankaran, S. High-throughput field phenotyping of Ascochyta blight disease severity in chickpea. Crop Protect. 2019, 125, 11; DOI:10.1016/j.cropro.2019.104885.