Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review

Biosystems Engineering

Volumn 151, Issue , 2016, Pages 374-398

  Katsoulas, Nikolaos ^a,b   Elvanidi, Angeliki ^a   Ferentinos, Konstantinos P ^b   Kacira, Murat ^c   Bartzanas, Thomas ^b   Kittas, Constantinos ^a,b  

^a UNIVERSITY OF THESSALY   (Greece)

^b INFORMATION TECHNOLOGIES INSTITUTE   (Greece)

^c University of Arizona   (United States)

Author keywords

Nutrient status; Reflectance index; Remote sensing; Vegetation indices; Water status

Indexed keywords

DATA HANDLING; FORESTRY; GREENHOUSES; INFORMATION ANALYSIS; NUTRIENTS; PLANTS (BOTANY); REFLECTION; REFLECTOMETERS; REMOTE SENSING; STRESSES;

DATA ANALYSIS TECHNIQUES; ENVIRONMENTAL CONDITIONS; NUTRIENT STATUS; REAL TIME DATA ANALYSIS; REFLECTANCE INDEX; SPEAKING PLANT APPROACH; VEGETATION INDEX; WATER STATUS;

CROPS;

EID: 84994291519     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2016.10.003     Document Type: Review

References (133)

1. Aguilar, C., Zinnert, J.C., Polo, M.J., Young, D.R., NDVI as an indicator for changes in water availability to woody vegetation. Ecological Indicators 23 (2012), 290–300.
2. Aikio, M., Hyperspectral prism-grating-prism imaging spectrograph. 2001, Electrical Engineering, VTT Publishing 435.
3. Amatya, S., Karkee, M., Alva, A.K., Larbi, P., Adhikari, B., Hyperspectral imaging for detecting water stress in potatoes. Paper Number: 12-1345197 American society of agricultural and biological engineers, meeting Presentation, 2012.
4. Aparicio, N., Villegas, D., Royo, C., Casadesus, J., Araus, J.L., Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions. International Journal of Remote Sensing 25:6 (2004), 1131–1152.
5. Arngren, M., Hyperspectral NIR camera. Hyperspectral Camera, Technical note, 1.2, 2011.
6. Asner, G.P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 64 (1998), 234–253.
7. Bajwa, S.G., Mishra, A.R., Norman, R.J., Canopy reflectance response to plant nitrogen accumulation in rice. Precision Agriculture 11:5 (2010), 488–506.
8. Bandyopadhyay, K.K., Pradhan, S., Sahoo, R.N., Singh, R., Gupta, V.K., Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management. Agricultural Water Management 146 (2014), 115–123.
9. Bastiaanssen, W.G.M., Molden, D.J., Makin, I.W., Remote sensing for irrigated agriculture: Examples for research and possible applications. Agriculture Water Management 46:2 (2000), 137–155.
10. Basyouni, R., Dunn, B., Use of reflectance sensors to monitor plant nitrogen status in horticultural plants. Oklahoma Cooperative Extension Service. HLA-6719, 2013, 1–4.
11. Bautista-Gallego, J., Rodriguez-Gomez, F., Barrio, E., Querol, A., Garrido-Fernandez, A., Arroyo-Lopez, F., Exploring the yeast biodiversity of green table olive industrial fermentations for technological applications. International Journal of Food Microbiology 147 (2011), 89–96.
12. Bonstema, J., Sweet pepper harvesting robot. 2015 EU Robotics Forum, 11–13 March 2015, Vienna, Austria.
13. Borzuchowski, J., Schulz, K., Retrieval of leaf area index (LAI) and soil water content (WC) using hyperspectral remote sensing under controlled glass house conditions for spring barley and sugar beet. Remote Sensing 2:7 (2010), 1702–1721.
14. Bowman, W.D., Hubick, K.T., Von Caemmerer, S., Farquhar, G.D., Short-term changes in leaf carbon isotope discrimination in salt- and water-stressed grasses. Plant Physiology 90 (1989), ,162–166.
15. Brunn, A., Naughton, D., Weichelt, H., Douglass, S., Thiele, M., Oxfort, M., et al. The calibration procedure of the multispectral imaging instruments on board the Rapideye™ remote sensing satellites. Proceedings of the international calibration and orientation workshop, EuroCOW 2010, 10–12 February 2010, Castelldefels, Spain, 2010.
16. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., Grégoire, J.M., Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment 77 (2001), 22–33.
17. Chung-Ru, H., Principle of remote sensing. APEC SAKE Training Program November 3, 2008, National Taiwan Ocean University. Department of Marine Environmental Informatics, Keelung, Chinese Taipei, 2008, 36.
18. Clevers, J.G.P.W., Kooistra, L., Schaepman, M.E., Using spectral information from the NIR water absorption features for the retrieval of canopy water content. International Journal of Applied Earth Observation and Geoinformation 10 (2008), 338–397.
19. Cordon, G.B., Lagorio, M.G., Absorption and scattering coefficients: A biophysical-chemistry experiment using reflectance spectroscopy. Journal of Chemical Education 84:7 (2007), 1167–1170.
20. Croft, H., Chen, J.M., Zhang, Y., The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecological Complexity 17 (2014), 119–130.
21. Datt, B., Remote sensing of water content in eucalyptus Leaves. Australian Journal of Botany 47 (1999), 909–923.
22. Daughtry, C.S.T., Biehl, L.L., Changes in the spectral properties of detached birch leaves. Remote Sensing of Environment 17 (1985), 281–289.
23. Delalieux, S., Van Aardt, J.A.N., Keulemans, W., Coppin, P., Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral analysis. Imaging Spectroscopy 14 (2005), 677–689.
24. Duggin, M.J., On the natural limitations of target differentiation by means of spectral discrimination techniques. Proceedings of the 9th international symposium on remote sensing of environment (Ann Arbor, Michigan: Environmental research institute of Michigan), 1974, 499–514.
25. Eitel, J.U.H., Gessler, P.E., Smith, A.M.S., Robberecht, R., Suitability of existing novel spectral indices to remotely detect water stress in Opulus spp. Forest Ecology and Management 229 (2006), 170–182.
26. Elsayed, S., Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley. Field Crop Research 177 (2015), 148–160.
27. Fatnassi, E., Chaouachi, J.S., Klibi, W., Planning and operating a shared goods and passengers on-demand rapid transit system for sustainable city-logistics. Transportation Research Part B 81 (2015), 440–460.
28. 2 as a target fpr increasing photosynthesis and photosynthesis water-use efficiency. Photosynthesis Research 117:1–3 (2013), 45–59.
29. Gammon, J.A., Peňuelas, J., Field, C.B., A narrow-wave band spectral index that track diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41 (1992), 35–44.
30. Garbulsky, M.F., Peňuelas, J., Gamon, J., Inoue, Y., Filella, I., The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies, A review and meta-analysis. Remote Sensing of Environment 115 (2011), 281–297.
31. Genc, L., Demirel, K., Camoglu, G., Asik, S., Smith, S., Determination of plant water stress using spectral reflectance measurements in watermelon (Citrullus vulgaris), American-Eurasian. Journal of Agriculture and Environmental Science 11:2 (2011), 296–304.
32. Gianquinto, G., Orsini, F., Fecondini, M., Mezzetti, M., Sambo, P., Bona, S., A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal Agronomy 35 (2011), 135–143.
33. González-Fernández, A.B., Rodríguez-Pérez, J.R., Marcelo, V., Using field spectrometry and a plant probe accessory to determine leaf water content in commercial vineyards. Agricultural Water Management 156 (2015), 43–50.
34. Govender, M., Chetty, K., Bulock, H., A review of hyperspectral remote sensing and its application in vegetation and water resources studies. African Journal, Water SA 3:2 (2007), 145–152.
35. Govender, M., Dye, P.J., Weiersbye, I.M., Witkowski, E.T.F., Ahmed, F., Review of commonly used remote sensing and ground-based technologies to measure plant water stress. African Journal, Water SA 35:5 (2009), 741–752.
36. Graeff, S., Claupein, W., Identification and discrimination of water stress in wheat leaves (Triticum aestivium L.) by means of reflectance measurements. Irrigation Science 26 (2007), 61–70.
37. Grant, W.B., Brothers, A.M., Bogan, J.R., Differential absorption lidar signal averaging. Applied Optics 27 (1987), 1934–1938.
38. Gutschick, V.P., Biotic and abiotic consequences of differences in leaf structure. New Phytologist 143 (1999), 3–18.
39. Hernández, E.I., Melendez-Pastor, I., Navarro-Pedreño, J., Gómez, I., Spectral indices for the detection of salinity effects in melon plants. Scientia Agricola 71 (2014), 324–330.
40. Hodanova, D., Leaf optical properties. Sesták, Z., (eds.) Photosynthesis during leaf development, 1985, Academic Praha, Prague, 107–127.
41. Hsiao, S.C., Chen, S., Yang, I.C., Chen, C.T., Tsai, C.Y., Chuang, Y.K., et al. Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging. Computer and Electronics in Agriculture 72 (2010), 127–133.
42. Hu, X., Sun, L., Liu, J., Ding, L., Wang, X., Li, Y., et al. Calibration for the solar reflective bands of medium resolution spectral imager on board FY-3A. IEEE Transactions on Geoscience and Remote sensing 50:12 (2012), 4915–4928.
43. Huang, Z., Zeng, H., Rapid near infrared raman spectroscopy system for real tie in vivo skin measurements. Optic letters 26:22 (2001), 1782–1784.
44. Hunt, E.R., Rock, B.N., Detection of change in leaf water content using near and middle infrared reflectance. Remote Sensing of Environment 30 (1989), 43–54.
45. 2 assimilation rate, chlorophyll fluorescence and photochemical reflectance index in relation to leaf temperature. Plant Production Science 8:2 (2005), 131–138.
46. Jackson, R.D., Huete, A.R., Interpreting vegetation indices. Preventive Veterinary Medicine 11 (1991), 185–200.
47. Jacquemoud, S., Baret, F., Prospect: A model of leaf optical properties. Remote Sensing Environment 34 (1990), 75–91.
48. Jacquemoud, S., Ustin, S.L., Modeling leaf optical properties. Photobiological sciences online. Environmental Photobiology, 2008 http://photobiology.info/#Environ.
49. Jain, N., Ray, S.S., Singh, J.P., Panigrahy, S., Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agriculture 8 (2007), 225–239.
50. Jones, C.L., Weckler, P.R., Maness, N.O., Jayasekara, R., Stone, M.L., Chrz, D., Remote sensing to estimate chlorophyll concentration in spinach using multi-spectral plant reflectance. American Society of Agricultural and Biological Engineers 50:6 (2007), 2267–2273.
51. Jones, C.L., Weckler, P.R., Maness, N.O., Stone, M.L., Jayasekara, R., Estimating water stress in plant using hyperspectral sensing. Annual international meeting sponsored by ASAE/CSAE, 2004, 2–11 Paper Number 043065.
52. Kacira, M., Ling, P.P., Design and development of an automated and non-contact sensing system for continuous monitoring of plant health and growth. Transactions of American Society for Engineers 44:4 (2001), 889–896.
53. Kacira, M., Sasae, S., Okushima, L., Ling, P.P., Plant response-based sensing for control strategies in sustainable greenhouse production. Journal of Agricultural Meteorology of Japan 61:1 (2005), 15–22.
54. Katsoulas, N., Elvanidi, A., Ferentinos, K.P., Bartzanas, T., Kittas, C., Calibration methodology of a hyperspectral imaging system for greenhouse plant water stress estimation. Proceedings of the 6th Balkan symposium on Vegetables and Potatoes, September 29 – October 2, 2014 Zagreb, Croatia, 2014 Acta Horticulturae, in press.
55. Kim, Y., Glenn, D.M., Park, J., Ngugi, H.K., Lehman, B.L., Hyperspectral image analysis for plant stress detection. American society of agricultural and biological engineers, Annual international meeting, Pittsburgh, Pennsylvania, June 20-June 23, 2010, 2010 Paper Number 1009114.
56. King, J.B., Layzell, B.D., Canvin, T.D., The role of dark carbon dioxide fixation in root nodules of soybean. Plant Physiology 81:1 (1986), 200–205.
57. Kittas, C., Bartzanas, T., Jaffrin, A., Temperature gradients in a partially shaded large greenhouse equipped with evaporative cooling pads. Biosystems Engineering 85:1 (2003), 87–94.
58. Kittas, C., Elvanidi, A., Katsoulas, N., Ferentinos, K.P., Bartzanas, T., Reflectance indices for the detection of water stress in greenhouse tomato (Solanum lycopersicum). Acta Horticulturae 1112 (2016), 63–70.
59. Knipling, E.B., Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing Environment, 1970, 155–159.
60. Köksal, E.S., Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators. Agricultural Water Management 98 (2011), 1317–1328.
61. Köksal, E.S., Güngör, Y., Yildirim, Y.E., Spectral reflectance characteristics of sugar beet under different levels of irrigation water and relationships between growth parameters and spectral indexes. Irrigation and Drainage 60 (2010), 187–195.
62. Kozma-Bognár, V., Berke, J., New evaluation techniques of hyperspectral data. Journal of Systemics, Cybernetics and Informatics 8:5 (2010), 49–53.
63. Kruse, J.K., Remote sensing of moisture and nutrient stress. Retrospective Theses and Dissertations. Paper 1176, 2004, Iowa State University UMI Number: 3158350, ProQuest Information and Learning Company, MI, USA.
64. Lan, Y., Zhang, H., Lacey, R., Hoffmann, W.C., Wu, W., Development of an integrated sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: the CIGR Ejournal, XI, 2009 Manuscript IT 08 1115.
65. Lawrence, K.C., Park, B., Heitschmidt, G.W., Windham, W.R., Thai, C.N., Evaluation of LED and tungsten-halogen lighting for fecal contaminant detection. American Society of Agricultural and Biological Engineers 23:6 (2007), 811–818.
66. Lawrence, K.C., Park, B., Windham, W.R., Mao, C., Calibration of a pushbroom hyperspectral imaging system for agricultural inspection. Transactions of the American Society of Agricultural Engineers 46 (2003), 513–521.
67. Lepine, L.C., Ollinger, S.V., Ouimette, A.P., Martin, M.E., Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping. Remote Sensing of Environment 173 (2016), 174–186.
68. Li, Q., He, X., Wang, Y., Liu, H., Xu, D., Guo, F., Review of spectral imaging technology in biomedical engineering: Achievement and challenges. Journal of Biomedical Optics, 18(10), 2013 100901-1–100901-28.
69. Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U., Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy 52:B (2014), 198–209.
70. Li, G., Wan, S., Zhou, J., Yang, Z., Qin, P., Leaf chlorophyll fluorescence, hyperspectral reflectance, pigments content, malondialdehyde and proline accumulation responses of castor bean (Ricinus communis L.) seedlings to salt stress levels. Industrial Crops and Products 31 (2010), 13–19.
71. Liaghat, S., Balasundram, S.K., A review: The role of remote sensing in precision agriculture. American Journal of Agricultural and Biological Sciences 5:1 (2010), 50–55.
72. Ling, P.P., Giacomelli, G.A., Russell, T., Monitoring of plant development in controlled environment with machine vision. Advances in Space Research 18 (1996), 4101–4112.
73. Liu, Q.X., Jindra, M., Ueda, H., Hiromi, Y., Hirose, S., Drosophila MBF1 is a co-activator for tracheae defective and contributes to the formation of tracheal and nervous systems. Development 130:4 (2003), 719–728.
74. Liu, L., Wang, J., Huang, W., Zhao, C., Zhang, B., Tong, Q., Estimating winter wheat plant water content using red edge parameters. International Journal of Remote Sensing 25:17 (2004), 3331–3342.
75. Loh, W.Y., Classification and regression trees. Department of statistics 1:1 (2011), 14–23.
76. Magney, T.S., Vierling, L.A., Eitel, J.U.H., Huggins, D.R., Garrity, S.R., Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat. Remote sensing of Environment 173 (2016), 84–97.
77. Malenovskŷ, Z., Mishra, K.B., Zemek, F., Rascher, U., Nedbal, L., Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. Journal of Experiment Botany 60:11 (2009), 2987–3004.
78. Mänd, P., Hallik, L., Peñuelas, J., Nilson, T., Duce, P., Emmett, B.A., et al. Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrub lands along a north–south climatic gradient. Remote Sensing of Environment 114:3 (2010), 626–636.
79. Maracci, G., Schmuck, G., Hosgood, B., Andreoli, G., Interpretation of reflectance spectra by plant physiological parameters. Proceedings of international geoscience and remote sensing symposium (IGARSS’91), remote Sensing: Global monitoring for earth management. Helsinki university of technology, Espoo (Finland), 3 – 6 June 1991, 1991, IEEE, New York, 2303–2306.
80. Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Giovannelli, A., Cantini, C., et al. Assessing gas exchange, sap flow and water relations using tree canopy spectral reflectance indices in irrigated and rainfed Olea europaea L. Environmental and Experimental Botany 99 (2014), 43–52.
81. Mazzetto, F., Calcante, A., Mena, A., Comparing commercial optical sensors for crop monitoring tasks in precision viticulture. Journal of Agricultural Engineering 1 (2009), 11–18.
82. Merlier, E., Hmimina, G., Dufrêne, E., Soudani, K., Explaining the variability of the photochemical reflectance index (PRI) at the canopy-scale: Disentangling the effects pf phenological and physiological changes. Journal of Photochemistry and Photobiology B: Biology 151 (2015), 161–171.
83. Milton, E.J., Review article principles of field spectroscopy. International Journal of Remote Sensing 8:12 (1987), 1807–1827.
84. Mouroulis, P., Spectral and spatial uniformity in pushbroom imaging spectrometers. Proceeding of SPIE, 3753, 1999, 133–141.
85. Nigon, T.J., Mulla, D.J., Rosen, C.J., Cohen, Y., Alchanatis, V., Knight, J., Hyperpsectral aerial imagery for detecting nitrogen stress in two potato cultivars. Computers and Electronics in Agriculture 112 (2015), 36–46.
86. Nilsson, O., Effects of Agrobacterium rhizogenes rol-gene expression on plant growth and hormone metabolism. Thesis (Diss. doct.), 1995 Umeaa: Swedish university of agricultural sciences. Department of forest genetics and plant physiology.
87. Noble, S.D., Li, D., Segmentation of greenhouse cucumber plants in multi-spectral imagery. Proceedings of the international conference of agricultural engineering, CIGR-AgEng, Valencia, Spain, 2012, 1–5.
88. Padilla, J., Pérez-Rodríguez, L., Mougeot, F., Ludwig, S., Redpath, S.M., Intra-sexual competition alters the relationship between testosterone and ornament expression in a wild territorial bird. Hormones and Behavior 65:5 (2014), 435–444.
89. Panda, C., Basic principles of remote sensing. 2012, Indian Agricultural Statistics Research Institute e-book, II 65-II 88.
90. Panigada, C., Rossini, M., Meroni, M., Cilia, C., Busetto, L., Amaducci, S., et al. Fluorescence, PRI and canopy temperature for water stress detection in cereal crops. International Journal of Applied Earth Observation and Geoinforation 30 (2014), 167–178.
91. Pederson, V.D., Nutter, F.W.J., Disease efficiency and lesion size as criteria for screening barley genotypes for resistance to net blotch (Pyrenophora teres). North Dakota Academy of Science, 35, 1982, 83.
92. Peňuelas, J., Isla, R., Fillela, I., Araus, J.L., Visible and near infrared reflectance assessment of salinity effects on barley. Crop Science 37:1 (1997), 198–202.
93. Peňuelas, J., Filella, I., Biel, C., Serrano, L., Savé, R., The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14:10 (1993), 1887–1905.
94. Peňuelas, J., Filella, I., Elvira, S., Inclan, R., Reflectance assessment of summer ozone fumigated Mediterranean white pine seedlings. Environmental and Experimental Botany 35:3 (1995), 299–307.
95. Peňuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B., Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing of Environment 48 (1994), 135–146.
96. Peňuelas, J., Inoue, Y., Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36:3 (1999), 355–360.
97. Pinto Da Silva, P., Douglas, S.T., Branton, D., Localization of A-antigen sites on human erythrocyte ghosts. Nature (London) 232 (1971), 194–196.
98. Polder, G., Gerie van der Heijden, W.A.M.L., Paul Keizer, C., Young, I.T., Calibration and characterization of imaging spectrographs. Near Infrared Spectroscopy 11 (2003), 193–210.
99. Ray, S.S., Das, G., Singh, J.P., Panigrahy, S., Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing 27:24 (2006), 5373–5387.
100. Sarlikioti, V., Driever, S.M., Marcellis, L.F.M., Photochemical reflectance index as a mean of monitoring early water stress. Annals of Applied Biology 157:1 (2010), 81–89.
101. Schowengerdt, A., Optical radiation models. Remote Sensing (third edition) Models and Methods for Image Processing, 2007, Academic Press, Burlington, 45–73 XI-XIII.
102. Sclemmer, M.R., Francis, D.D., Shanahan, J.F., Scepers, J.S., Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy Journal 97 (2005), 106–112.
103. Shahenshah, Yasuda, Y., Mao-song, L., Isoda, A., Assessment of photochemical reflectance index as a tool for evaluation of chlorophyll fluorescence parameters in cotton and peanut cultivars under water stress condition. Agricultural Sciences in China 9:5 (2010), 662–670.
104. Shimada, S., Funatsuka, E., Ooda, M., Takyu, M., Developing the monitoring method for plant water stress. Journal or Arid Land Studies 22:1 (2012), 251–254.
105. Silleos, G.N., Alexandridis, T.K., Gitas, I.Z., Perakis, K., Vegetation indices: Advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto International 21:4 (2006), 21–28.
106. Soudani, K., Hmimina, G., Delpierre, N., Pontailler, J.Y., Aubinet, M., Bonal, D., et al. Ground-based network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sensing of Environment 123 (2012), 234–245.
107. Story, D., Kacira, M., Design and implementation of a computer vision-guided greenhouse crop diagnostics system. Machine Vision and Applications 26 (2015), 495–506.
108. Story, D., Kacira, M., Kubota, C., Akoglu, A., Lingling, A., Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture 74 (2010), 238–243.
109. Suárez, L., Zarco-Tejada, P.J., Berni, A.J., González-Dugo, V., Fereres, E., Modelling PRI for water stress detection using radiative transfer models. Remote Sensing of Environment 113:4 (2009), 730–744.
110. Suárez, L., Zarco-Tejada, P.J., Gonzálelez-Dugo, V., Berni, J.A.J., Fereres, E., Detecting water stress in orchard crops using PRI from airborne imagery. 6th EARSeL SIG IS workshop imaging Spectroscopy: Innovative tool for scientific and commercial environmental applications, March 16 – 19, 2009, Tel- Aviv, Israel, 2008.
111. Suárez, L., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Pérez-Priego, O., Miller, J.R., Jiménez-Muňoz, V., et al. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sensing Environment 112 (2007), 560–575.
112. Summy, K.R., Little, C.R., Mazariegos, R.A., Everitt, J.H., Davis, M.R., French, J.V., et al. Detecting stress in glasshouse plants using color infrared imagery: A potential new application for remote sensing. Subtropical Plant Science 55:1 (2003), 51–58.
113. Sun, P., Grignetti, A., Liu, S., Casacchia, R., Salvatori, R., Pietrini, F., et al. Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress. International Journal of Remote Sensing 29 (2008), 1725–1743.
114. Takakura, T., Kozai, T., Tachibana, K., Jordan, K.A., Direct digital control of plant growth (I). Transactions of the ASAE 17 (1974), 1150–1154.
115. Thenot, F., Méthy, M., Winkel, T., The photochemical reflectance index (PRI) as water –stress index. International Journal of Remote Sensing 23:23 (2002), 5135–5139.
116. Tsirogiannis, I.L., Katsoulas, N., Savvas, D., Karras, G., Kittas, C., Relationships between reflectance and water status in a greenhouse rocket (Eruca sativa Mill.) cultivation. European Journal of Horticulture of Science 78 (2013), 275–282.
117. Working report, 2011-26 Tuominen, J., Lipping, T., (eds.) Detection of environmental change using hyperspectral remote sensing at Olkiluoto repository site, 2011, Posiva, Eurajoki, 51.
118. Vagni, F., Survey of hyperspectral and multispectral imaging technologies., 2007, Research and Technology Organization Technical Report TR-SET—065-P3-NATO.
119. Van Gaalen, K.E., Flanagan, L.B., Peddle, D.R., Photosynthesis, chlorophyll fluorescence and spectral reflectance in spangnum moss at varying water contents. Oecologia 153 (2007), 19–28.
120. Verdebout, J., Jacquemoud, S., Schmuck, G., Optical properties of leaves: Modeling and experimental studies. Hill, J., Mégier, J., (eds.) Imaging spectrometry - A tool for environmental observations Eurocourses: Remote sensing, Vol. 4, 1994, 169–191.
121. Vigneau, N., Ecarnotb, M., Rabatela, G., Roumet, P., Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Research 122:1 (2011), 25–31.
122. Vogelmann, T.C., Bornman, F.J., Josserand, S., Photosynthesis gradients and spectral regime within leaves of Medicago sativa. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 323 (1989), 411–421.
123. Vogelmann, J.E., Rock, B.N., Moss, D.M., Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14:8 (1993), 1563–1575.
124. Willoughby, C.T., Folkman, M.A., Figueroa, M.A., Application of hyperspectral imaging spectrometer system to industrial inspection. Proc. SPIE 2599, three-dimensional and unconventional imaging for industrial inspection and metrology, 1996, 264–272.
125. Winkel, T., Thenot, F., Méthy, M., The photochemical reflectance index (PRI) as a water-stress index. International Journal of Remote Sensing 23:23 (2002), 5135–5139.
126. Yao, H., Huang, Y., Hruska, Z., Thomson, S.J., Reddy, K.N., Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate. Computer and Electronics in Agriculture 89 (2012), 145–157.
127. Yi, Q., Bao, A., Wang, Q., Zhao, J., Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and Electronic Agriculture 90 (2013), 144–151.
128. Zakaluk, R., Sri Ranjan, R., Predicting the leaf water potential of potato plants using RGB reflectance. Canadian Biosystems Engineering 50 (2008), 7.1–7.12.
129. Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.J., 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 Environment 117 (2011), 322–337.
130. Zarco-Tejada, P.J., Miller, J.R., Mohammed, G.H., Noland, T.L., Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation. Remote Sensing of Environment 74:3 (2000), 582–595.
131. Zarco-Tejada, P.J., González-Dugo, V., Williams, L.E., Suárez, L., Berni, J.A.J., Goldhamer, D., Fereres, E., A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment 138 (2013), 38–50.
132. Zhao, D., Raja Reddy, K., Gopal Kakani, V., Reddy, V.R., Nitrogen deficiency effects on plant growth, leaf photosynthesis and hyperspectral reflectance properties of sorghum. European Journal of Agronomy 22 (2005), 391–403.
133. Zhou, T., Mao, H., Zhang, X., Hyperspectral imaging technology for detection of moisture content of tomato leaves. Proceedings of the 2011 4th international congress on image and signal processing (CISP), Shanghai, China, 15, 2011, 167–171.