Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Agronomy

Volumn 8, Issue 4, 2018, Pages

  Tripodi, Pasquale ^a   Massa, Daniele ^b   Venezia, Accursio ^a   Cardi, Teodoro ^a  

^a CREA Research Centre for Vegetable and Ornamental Crops   (Italy)

^b Research Centre for Vegetable and Ornamental Crops   (Italy)

Author keywords

Advanced crop management; Automation; Digital imaging; Fluorescence; Genomics; Greenhouse horticulture; Optical sensors; Phenomics; Plant breeding; Vegetation indices

Indexed keywords

EID: 85045959313     PISSN: None     EISSN: 20734395     Source Type: Journal    
DOI: 10.3390/agronomy8040057     Document Type: Review

References (129)

1. Furbank, R.T.; Tester, M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011, 16, 635–644. [CrossRef] [PubMed]
2. Collard, B.C.Y.; Mackill, D.J. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philos. Trans. R. Soc. B 2008. [CrossRef] [PubMed]
3. Gupta, P.K.; Rustgi, S.; Kulwal, P.L. Linkage disequilibrium and association studies in higher plants: Present status and future prospects. Plant Mol. Biol. 2005, 57, 461–485. [CrossRef] [PubMed]
4. Cobb, J.N.; DeClerck, G.; Greenberg, A.; Clark, R.; McCouch, S. Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 2013, 126, 867–887. [CrossRef] [PubMed]
5. Dhondt, S.; Wuyts, N.; Inzé, D. Cell to whole-plant phenotyping: The best is yet to come. Trends Plant Sci. 2013, 8, 1–12. [CrossRef] [PubMed]
6. Fiorani, F.; Schurr, U. Future scenarios for plant phenotyping. Annu. Rev. Plant Biol. 2013, 64, 267–291. [CrossRef] [PubMed]
7. Araus, J.L.; Cairns, J.E. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014, 19, 52–61. [CrossRef] [PubMed]
8. Montepellier Plant Phenotyping Network. Available online: https://www6.montpellier.inra.fr/lepse_eng/M3P (accessed on 28 February 2018).
9. JPPC-the J lich Plant Phenotyping Centre. Available online: http://www.fz-juelich.de/ibg/ibg-2/EN/_organisation/JPPC/JPPC_node.html (accessed on 28 February 2018).
10. IPK Gaterbsleben Phenotyping. Available online: http://www.ipk-gatersleben.de/en/phenotyping/ (accessed on 28 February 2018).
11. APPS. Available online: https://www.plantphenomics.org.au/ (accessed on 28 February 2018).
12. Humplık, J.F.; Lazar, D.; Husickova, A.; Spıchal, L. Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses—A review. Plant Methods 2015, 11, 29. [CrossRef] [PubMed]
13. Shakoor, N.; Lee, S.; Mockler, T.C. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr. Opin. Plant Biol. 2017, 38, 184–192. [CrossRef] [PubMed]
14. Li, L.; Zhang, Q.; Huang, D. A review of imaging techniques for plant phenotyping. Sensors 2014, 14, 20078–20111. [CrossRef] [PubMed]
15. Usha, K.; Singh, B. Potential applications of remote sensing in horticulture-A review. Sci. Hortic. (Amsterdam) 2013, 153, 71–83. [CrossRef]
16. Ruiz-Garcia, L.; Lunadei, L.; Barreiro, P.; Robla, J.I. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors 2009, 9, 4728–4750. [CrossRef] [PubMed]
17. Nishina, H. Development of speaking plant approach technique for intelligent greenhouse. Agric. Agric. Sci. Procedia 2015, 3, 9–13. [CrossRef]
18. Incrocci, L.; Massa, D.; Pardossi, A. New trends in the fertigation management of irrigated vegetable crops. Horticulturae 2017, 3, 37. [CrossRef]
19. Mu oz-Huerta, R.F.; Guevara-Gonzalez, R.G.; Contreras-Medina, L.M.; Torres-Pacheco, I.; Prado-Olivarez, J.; Ocampo-Velazquez, R.V. A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances. Sensors 2013, 13, 10823–10843. [CrossRef] [PubMed]
20. Basu, S.; Rabara, R. Abscisic acid—An enigma in the abiotic stress tolerance of crop plants. Plant Gene 2017, 11, 90–98. [CrossRef]
21. Marschner, H. Marschner’s Mineral Nutrition of Higher Plants, 3rd ed.; Accademic Press: London, UK, 2011.
22. Deery, D.; Jimenez-Berni, J.; Jones, H.; Sirault, X.; Furbank, R. Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy 2014, 4, 349–379. [CrossRef]
23. Fahlgren, N.; Gehan, M.A.; Baxter, I. Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 2015, 24, 93–99. [CrossRef] [PubMed]
24. Pound, M.P.; French, A.P.; Murchie, E.H.; Pridmore, T.P. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 2014, 166, 1688–1698. [CrossRef] [PubMed]
25. Paulus, S.; Dupuis, J.; Mahlein, A.-K.; Kuhlmann, H. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform. 2013, 14, 238. [CrossRef] [PubMed]
26. Chaudhury, A.; Ward, C.; Talasaz, A.; Ivanov, A.G.; Brophy, M.; Grodzinski, B.; Huner, N.P.A.; Patel, R.V.; Barron, J.L. Machine Vision System for 3D Plant Phenotyping. Available online: http://arxiv.org/abs/1705.00540 (accessed on 15 April 2018).
27. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271–282. [CrossRef] [PubMed]
28. Francone, C.; Pagani, V.; Foi, M.; Cappelli, G.; Confalonieri, R. Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures. Field Crop Res. 2014, 155, 38–41. [CrossRef]
29. Golzarian, M.R.; Frick, R.A.; Rajendran, K.; Berger, B.; Roy, S.; Tester, M.; Lun, D.S. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 2011, 7. [CrossRef] [PubMed]
30. Liu, J.; Pattey, E. Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agric. For. Meteorol. 2010, 150, 1485–1490. [CrossRef]
31. Arvidsson, S.; Pérez-Rodríguez, P.; Mueller-Roeber, B. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol. 2011, 191, 895–907. [CrossRef] [PubMed]
32. Iyer-Pascuzzi, A.S.; Symonova, O.; Mileyko, Y.; Hao, Y.; Belcher, H.; Harer, J.; Weitz, J.S.; Benfey, P.N. Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol. 2010, 152, 1148–1157. [CrossRef] [PubMed]
33. Hoyos-Villegas, V.; Houx, J.H.; Singh, S.K.; Fritschi, F.B. Ground-based digital imaging as a tool to assess soybean growth and yield. Crop Sci. 2014, 54, 1756–1768. [CrossRef]
34. Takizawa, H.; Ezaki, N.; Mizuno, S.; Yamamoto, S. Measurement of plants by stereo vision for agricultural applications. In Proceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005, Honolulu, HI, USA, 15–17 August 2005; pp. 359–364.
35. Omasa, K.; Hosoi, F.; Konishi, A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 2007, 58, 881–898. [CrossRef] [PubMed]
36. Llop, J.; Gil, E.; Llorens, J.; Miranda-Fuentes, A.; Gallart, M. Testing the suitability of a terrestrial 2D LiDAR scanner for canopy characterization of greenhouse tomato crops. Sensors 2016, 16, 1435. [CrossRef] [PubMed]
37. Hosoi, F.; Nakabayashi, K.; Omasa, K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors 2011, 11, 2166–2174. [CrossRef] [PubMed]
38. Chen, Y.; Zhu, H.; Ozkan, H.E. Real-time tree foliage density estimation with laser scanning sensor for variable-rate tree sprayer development. In Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting 2013, ASABE 2013, Kansas City, MO, USA, 21–24 July 2013; Volume 3, pp. 2274–2285.
39. Dornbusch, T.; Lorrain, S.; Kuznetsov, D.; Fortier, A.; Liechti, R.; Xenarios, I.; Fankhauser, C. Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis–a novel phenotyping approach using laser scanning. Funct. Plant Biol. 2012, 39, 860–869. [CrossRef]
40. Li, Y.; Fan, X.; Mitra, N.J.; Chamovitz, D.; Cohen-Or, D.; Chen, B. Analyzing growing plants from 4D point cloud data. ACM Trans. Graph. 2013, 32. [CrossRef]
41. Maguire, M.S.; Woldt, W.E.; Frew, E.W.; Smith, J.; Elston, J. Thermal infrared and multi-spectral dual sensor integration for unmanned aircraft systems. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16–19 July 2017.
42. Corti, M.; Gallina, P.M.; Cavalli, D.; Cabassi, G. Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content. Biosyst. Eng. 2017, 158, 38–50. [CrossRef]
43. Thorp, K.R.; Gore, M.A.; Andrade-Sanchez, P.; Carmo-Silva, A.E.; Welch, S.M.; White, J.W.; French, A.N. Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Comput. Electron. Agric. 2015, 118, 225–236. [CrossRef]
44. Misra, A.N.; Misra, M.; Singh, R. Chlorophyll Fluorescence in Plant Biology. In Biophysics; Misra, A.N., Ed.; InTech: London, UK, 2012; p. 220, ISBN 978-953-51-0376-9.
45. Belasque, J.; Gasparoto, M.C.G.; Marcassa, L.G. Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy. Appl. Opt. 2008, 47, 1922–1926. [CrossRef] [PubMed]
46. Wu, Q.; Sun, H.; Li, M.Z.; Yang, W. Development and application of crop monitoring system for detecting chlorophyll content of tomato seedlings. Int. J. Agric. Biol. Eng. 2014, 7, 138–145. [CrossRef]
47. Ding, Y.; Li, M.; Sun, H.; Li, X.; Zhao, R. Diagnosis model of tomato nutrient content based on multispectral images. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 2012, 28, 175–180. [CrossRef]
48. Elvanidi, A.; Katsoulas, N.; Bartzanas, T.; Ferentinos, K.P.; Kittas, C. Crop water status assessment in controlled environment using crop reflectance and temperature measurements. Precis. Agric. 2017, 18, 332–349. [CrossRef]
49. Li, B.; Emr, N.; Malling, E.; Me, K. Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review. Plants 2018, 7, 3. [CrossRef]
50. Durmus, H.; Gunes, E.O.; Kirci, M. Disease detection on the leaves of the tomato plants by using deep learning. In Proceedings of the 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017, Fairfax, VA, USA, 7–10 August 2017.
51. Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 2015, 7, 4026–4047. [CrossRef]
52. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of the Earth Resources Technology Satellite Symposium, Washington, DC, USA, 10–14 December 1973; p. 371.
53. Di Gennaro, S.F.; Rizza, F.; Badeck, F.W.; Berton, A.; Delbono, S.; Gioli, B.; Toscano, P.; Zaldei, A.; Matese, A. UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices. Int. J. Remote Sens. 2017, 1–15. [CrossRef]
54. Agati, G.; Tuccio, L.; Kusznierewicz, B.; Chmiel, T.; Bartoszek, A.; Kowalski, A.; Grzegorzewska, M.; Kosson, R.; Kaniszewski, S. Nondestructive optical sensing of flavonols and chlorophyll in white head cabbage (Brassica oleracea L. var. capitata subvar. alba) grown under different nitrogen regimens. J. Agric. Food Chem. 2016, 64, 85–94. [CrossRef] [PubMed]
55. Liu, Z.; Zhang, F.; Ma, Q.; An, D.; Li, L.; Zhang, X.; Zhu, D.; Li, S. Advances in crop phenotyping and multi-environment trials. Front. Agric. Sci. Eng. 2015, 2, 28–37. [CrossRef]
56. Lenk, S.; Chaerle, L.; Pf ndel, E.E.; Langsdorf, G.; Hagenbeek, D.; Lichtenthaler, H.K.; Van Der Straeten, D.; Buschmann, C.; van der Straeten, D.; Buschmann, C.; et al. Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications. J. Exp. Bot. 2007, 58, 807–814. [CrossRef] [PubMed]
57. Jones, H.G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. In Incorporating Advances in Plant Pathology; Advances in Botanical Research; Academic Press: Cambridge, MA, USA, 2004; Volume 41, pp. 107–163.
58. Nilsson, H.E. Remote sensing and image analysis in plant pathology. Annu. Rev. Phytopathol. 1995, 33, 489–527. [CrossRef] [PubMed]
59. Chaerle, L.; Van Der Straeten, D. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 2000, 5, 495–501. [CrossRef]
60. 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. [CrossRef] [PubMed]
61. Moradi, A.B.; Oswald, S.E.; Nordmeyer-Massner, J.A.; Pruessmann, K.P.; Robinson, B.H.; Schulin, R. Analysis of nickel concentration profiles around the roots of the hyperaccumulator plant Berkheya coddii using MRI and numerical simulations. Plant Soil 2010, 328, 291–302. [CrossRef]
62. Van As, H. Intact plant MRI for the study of cell water relations, membrane permeability, cell-to-cell and long distance water transport. J. Exp. Bot. 2007, 58, 743–756. [CrossRef] [PubMed]
63. Windt, C.W.; Vergeldt, F.J.; De Jager, P.A.; Van As, H. MRI of long-distance water transport: A comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. Plant Cell Environ. 2006, 29, 1715–1729. [CrossRef] [PubMed]
64. 1H NMR imaging uncovers sugar allocation in the living seed. Plant Biotechnol. J. 2011, 9, 1022–1037. [CrossRef] [PubMed]
65. B hler, J.; Huber, G.; Schmid, F.; Bl mler, P. Analytical model for long-distance tracer-transport in plants. J. Theor. Biol. 2011, 270, 70–79. [CrossRef] [PubMed]
66. Kiyomiya, S.; Nakanishi, H.; Uchida, H.; Tsuji, A.; Nishiyama, S.; Futatsubashi, M.; Tsukada, H.; Ishioka, N.S.; Watanabe, S.; Ito, T.; et al. Real time visualization of 13N-translocation in rice under different environmental conditions using positron emitting tracer imaging system. Plant Physiol. 2001, 125, 1743–1754. [CrossRef] [PubMed]
67. Padilla, F.M.; Pe a-Fleitas, M.T.; Gallardo, M.; Thompson, R.B. Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber. Eur. J. Agron. 2017, 84, 1–15. [CrossRef]
68. Hunink, J.E.; Contreras, S.; Soto-García, M.; Martin-Gorriz, B.; Martinez-Álvarez, V.; Baille, A. Estimating groundwater use patterns of perennial and seasonal crops in a Mediterranean irrigation scheme, using remote sensing. Agric. Water Manag. 2015, 162, 47–56. [CrossRef]
69. ‘is, S. Comparison of different chlorophylls determination methods for leafy vegetables. Agron. Res. 2016, 14, 309–316.
70. Galieni, A.; Stagnari, F.; Speca, S.; Pisante, M. Leaf traits as indicators of limiting growing conditions for lettuce (Lactuca sativa). Ann. Appl. Biol. 2016, 169, 342–356. [CrossRef]
71. Kizil, U.; Gen, L.; Inalpulat, M.; Şapolyo, D.; Mirik, M. Lettuce (Lactuca sativa L.) yield prediction under water stress using artificial neural network (ANN) model and vegetation indices [Sejamosios salotos (Lactuca sativa L.) derliaus prognozavimas vandens streso sa(ogonek)lygomis, taikant dirbtinio neurotink]. Zemdirbyste 2012, 99, 409–418.
72. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [CrossRef]
73. Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286–292. [CrossRef]
74. Vogelmann, J.E.; Rock, B.N.; Moss, D.M. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14, 1563–1575. [CrossRef]
75. Birth, G.S.; McVey, G.R. Measuring the color of growing turf with a reflectance spectrophotometer1. Agron. J. 1968, 60, 640–643. [CrossRef]
76. Penuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Save, R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14, 1887–1905. [CrossRef]
77. Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403–5413. [CrossRef]
78. Xue, L.; Yang, L. Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS J. Photogramm. Remote Sens. 2009, 64, 97–106. [CrossRef]
79. Marino, S.; Basso, B.; Leone, A.P.; Alvino, A. Agronomic traits and vegetation indices of two onion hybrids. Sci. Hortic. (Amsterdam) 2013, 155, 56–64. [CrossRef]
80. Kabakeris, T.; Poth, A.; Intreß, J.; Schmidt, U.; Geyer, M. Detection of postharvest quality loss in broccoli by means of non-colorimetric reflection spectroscopy and hyperspectral imaging. Comput. Electron. Agric. 2015, 118, 322–331. [CrossRef]
81. Padilla, F.M.; Pe a-Fleitas, M.T.; Gallardo, M.; Thompson, R.B. Threshold values of canopy reflectance indices and chlorophyll meter readings for optimal nitrogen nutrition of tomato. Ann. Appl. Biol. 2015, 166, 271–285. [CrossRef]
82. De Benedetto, D.; Castrignanò, A.; Diacono, M.; Rinaldi, M.; Ruggieri, S.; Tamborrino, R. Field partition by proximal and remote sensing data fusion. Biosyst. Eng. 2013, 114, 372–383. [CrossRef]
83. Soto, F.; Gallardo, M.; Thompson, R.B.; Pe a-Fleitas, M.T.; Padilla, F.M. Consideration of total available N supply reduces N fertilizer requirement and potential for nitrate leaching loss in tomato production. Agric. Ecosyst. Environ. 2015, 200, 62–70. [CrossRef]
84. Yang, I.-C.; Chen, S. Precision cultivation system for greenhouse production. Smart Sens. Meas. Instrum. 2015, 13, 191–211. [CrossRef]
85. Sridhar, B.B.M.; Witter, J.D.; Wu, C.; Spongberg, A.L.; Vincent, R.K. Effect of biosolid amendments on the metal and nutrient uptake and spectral characteristics of five vegetable plants. Water Air Soil Pollut. 2014, 225. [CrossRef]
86. Marino, S.; Alvino, A. Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato. Eur. J. Remote Sens. 2014, 47, 271–283. [CrossRef]
87. Caramante, M.; Oliva, M.; Ricci, S.; Ruggiero, A.; D’Agostino, N.; Venezia, A.; Mennella, G.; Albrizio, R.; Giorio, P.; Grillo, S. Pepper response to salt stress at physiological, molecular and biochemical level. In Proceedings of the 3rd Spot-ITN Conference, Stress Biology and Crop Fertility, Sorrento, Italy, 18–22 March 2015; p. 50.
88. Both, A.J.; Benjamin, L.; Franklin, J.; Holroyd, G.; Incoll, L.D.; Lefsrud, M.G.; Pitkin, G. Guidelines for measuring and reporting environmental parameters for experiments in greenhouses. Plant Methods 2015, 11, 1. [CrossRef] [PubMed]
89. Apan, A.; Datt, B.; Kelly, R. Detection of pests and diseases in vegetable crops using hyperspectral sensing: A comparison of reflectance data for different sets of symptoms. In Proceedings of the SSC 2005 Spatial Intelligence, Innovation and Praxis: The National biennial Conference of the Spatial Sciences Institute, Melbourne, Australia, 12–16 September 2005.
90. Hahn, F. Spectral bandwidth effect on a Rhizopus stolonifer spores detector and its on-line behavior using red tomato fruits. Can. Biosyst. Eng. 2004, 46, 349–354.
91. Xu, H.R.; Ying, Y.B.; Fu, X.P.; Zhu, S.P. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosyst. Eng. 2007, 96, 447–454. [CrossRef]
92. Xing, J.; Ngadi, M.; Wang, N.; De Baerdemaeker, J. Wavelength selection for surface defects detection on tomatoes by means of a hyperspectral imaging system. In Proceedings of the ASAE Annual International Meeting, Portland, OR, USA, 9–12 July 2006.
93. Polder, G.; van der Heijden, G.W.A.M.; Young, I.T. Hyperspectral image analysis for measuring ripeness of tomatoes. In Proceedings of the ASAE Annual International Meeting, Milwaukee, WI, USA, 9–12 July 2000.
94. Diezma, B.; Lleó, L.; Roger, J.M.; Herrero-Langreo, A.; Lunadei, L.; Ruiz-Altisent, M. Examination of the quality of spinach leaves using hyperspectral imaging. Postharv. Biol. Technol. 2013, 85, 8–17. [CrossRef]
95. Tamburini, E.; Costa, S.; Rugiero, I.; Pedrini, P.; Marchetti, M.G. Quantification of Lycopene, -Carotene, and Total Soluble Solids in Intact Red-Flesh Watermelon (Citrullus lanatus) Using On-Line Near-Infrared Spectroscopy. Sensors 2017, 17, 746. [CrossRef] [PubMed]
96. Simko, I.; Hayes, R.J.; Furbank, R.T. Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors. Front. Plant Sci. 2016, 7, 1985. [CrossRef] [PubMed]
97. Windt, C.W.; Gerkema, E.; Van As, H. Most water in the tomato truss is imported through the xylem, not the phloem: A nuclear magnetic resonance flow imaging study. Plant Physiol. 2009, 151, 830–842. [CrossRef] [PubMed]
98. Zhang, L.; McCarthy, M.J. Measurement and evaluation of tomato maturity using magnetic resonance imaging. Postharvest Biol. Technol. 2012, 67, 37–43. [CrossRef]
99. Kotwaliwale, N.; Curtis, E.; Othman, S.; Naganathan, G.K.; Subbiah, J. Magnetic resonance imaging and relaxometry to visualize internal freeze damage to pickling cucumber. Postharvest Biol. Technol. 2012, 68, 22–31. [CrossRef]
100. Rascher, U.; Blossfeld, S.; Fiorani, F.; Jahnke, S.; Jansen, M.; Kuhn, A.J.; Matsubara, S.; M rtin, L.L.A.; Merchant, A.; Metzner, R.; et al. Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct. Plant Biol. 2011, 38, 968. [CrossRef]
101. Ecarnot, M.; Baczyk, P.; Tessarotto, L.; Chervin, C. Rapid phenotyping of the tomato fruit model, Micro-Tom, with a portable VIS-NIR spectrometer. Plant Physiol. Biochem. 2013, 70, 159–163. [CrossRef] [PubMed]
102. Szuvandzsiev, P.; Helyes, L.; Lugasi, A.; Szántó, C.; Baranowski, P.; Pék, Z. Estimation of antioxidant components of tomato using VIS-NIR reflectance data by handheld portable spectrometer. Int. Agrophys. 2014, 28, 521.
103. Clément, A.; Dorais, M.; Vernon, M. Multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by vis-NIR spectroscopy. J. Agric. Food Chem. 2008, 56, 1538–1544. [PubMed]
104. Xu, H.R.; Yu, P.; Fu, X.P.; Ying, Y.B. On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy. J. Zhejiang Univ. Sci. B 2009, 10, 126–132. [PubMed]
105. Xie, L.; Ying, Y.; Ying, T. Quantification of chlorophyll content and classification of nontransgenic and transgenic tomato leaves using visible/near-infrared diffuse reflectance spectroscopy. J. Agric. Food Chem. 2007, 55, 4645–4650. [CrossRef] [PubMed]
106. Yang, H.Q. Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration. Adv. Eng. Forum 2011, 1, 92–96. [CrossRef]
107. Mishra, Y.; Jankanpaa, H.J.; Kiss, A.Z.; Funk, C.; Schroder, W.P.; Jansson, S. Arabidopsis plants grown in the field and climate chambers significantly differ in leaf morphology and photosystem components. BMC Plant Biol. 2012, 12, 6. [CrossRef] [PubMed]
108. Devacht, S.; Lootens, P.; Baert, J.; Van Waes, J.; Van Bockstaele, E.; Roldán-Ruiz, I. Evaluation of cold stress of young industrial chicory (Cichorium intybus L.) plants by chlorofyll a fluorescence imaging. I. Light induction curve. Photosynthetica 2011, 49, 161–171. [CrossRef]
109. Lootens, P.; Devacht, S.; Baert, J.; Van Waes, J.; Van Bockstaele, E.; Roldán-Ruiz, I. Evaluation of cold stress of young industrial chicory (Cichorium intybus L.) plants byvchlororphyll a fluorescence imaging. II. Dark relaxation kinetics. Photosynthetica 2011, 49, 185–194. [CrossRef]
110. Rodriguez-Moreno, L.; Pineda, M.; Soukupova, J.; Macho, A.P.; Beuzon, C.R.; Baron, M. Early detection of bean infection by Pseudomonas syringae in asymptomatic leaf areas using chlorophyll fluorescence imaging. Photosynth. Res. 2008, 96, 27–35. [CrossRef] [PubMed]
111. Chaerle, L.; Hagenbeek, D.; Vanrobaeys, X.; Van Der Straeten, D. Jones and Schofield early detection of nutrient and biotic stress in Phaseolus vulgaris. Int. J. Remote Sens. 2007, 28, 3479–3492. [CrossRef]
112. Ptushenko, V.V.; Avercheva, O.V.; Bassarskaya, E.M.; Berkovich, Y.A.; Erokhin, A.N.; Smolyanina, S.O.; Zhigalova, T.V. Possible reasons of a decline in growth of Chinese cabbage under a combined narrowband red and blue light in comparison with illumination by high pressure sodium lamp. Sci. Hortic. 2015. [CrossRef]
113. Calatayud, Á.; San Bautista, A.; Pascual, B.; Maroto, J.V.; López-Galarza, S. Use of chlorophyll fluorescence imaging as diagnostic technique to predict compatibility in melon graft. Sci. Hortic. 2013, 149, 13–18. [CrossRef]
114. Hoffmann, A.M.; Noga, G.; Hunsche, M. Fluorescence indices for monitoring the ripening of tomatoes in pre- and postharvest phases. Sci. Hortic. 2015, 191, 74–81. [CrossRef]
115. Limantara, L.; Dettling, M.; Indrawati, R.; Indriatmoko, T.; Brotosudarmoa, P. Analysis on the chlorophyll content of commercial green leafy vegetables. Procedia Chem. 2015, 14, 225–231. [CrossRef]
116. Van der Heijden, G.; Song, Y.; Horgan, G.; Polder, G.; Dieleman, A.; Bink, M.; Palloix, A.; van Eeuwijk, F.; Glasbey, C. Spicy: Towards automated phenotyping of large pepper plants in the greenhouse. Funct. Plant Biol. 2012, 39, 870–877. [CrossRef]
117. Aguilar, M.A.; Pozo, J.L.; Aguilar, F.J.; Sánchez-Hermosilla, J.; Páez, F.C.; Negreiros, J.G. 3D surface modelling of tomato plants using close-range photogrammetry. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 2008, XXXVII, 139–144.
118. Gonzalo, M.J.; Brewer, M.T.; Anderson, C.; Sullivan, D.; Gray, S.; van der Knaap, E. Tomato fruit shape analysis using morphometric and morphology attributes implemented in Tomato Analyzer software program. J. Am. Soc. Hortic. Sci. 2009, 134, 77–87.
119. Hurtado, M.; Vilanova, S.; Plazas, M.; Gramazio, P.; Herraiz, F.J.; Andújar, I. Phenomics of fruit shape in eggplant (Solanum melongena L.) using tomato Analyzer software. Sci. Hortic. 2013, 164, 625–632. [CrossRef]
120. Figàs, M.R.; Prohens, J.; Raigón, M.D.; Fernández-de-Córdova, P.; Fita, A.; Soler, S. Characterization of a collection of local varieties of tomato (Solanum lycopersicum L.) using conventional descriptors and the high-throughput phenomics tool Tomato Analyzer. Genet. Res. Crop Evol. 2015, 62, 189–204. [CrossRef]
121. Knoblauch, M.; Peters, W.S.; Ehlers, K.; van Bel, A.J.E. Reversible calcium-regulated stopcocks in legume sieve tubes. Plant Cell 2001, 13, 1221–1230. [CrossRef] [PubMed]
122. Windt, C.W.; Soltner, H.; Dusschoten, D.; Bl mler, P. A portable Halbach magnet that can be opened and closed without force: The NMR-CUFF. J. Magn. Reson. 2011, 208, 27–33. [CrossRef] [PubMed]
123. Baker, N.R.; Rosenqvist, E. Applications of chlorophyll fluorescence can improve crop production strategies: An examination of future possibilities. J. Exp. Bot. 2004, 55, 1607–1621. [CrossRef] [PubMed]
124. Maxwell, K.; Johnson, G.N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 2000, 51, 659–668. [CrossRef] [PubMed]
125. Ehlert, B.; Hincha, D.K. Chlorophyll fluorescence imaging accurately quantifies freezing damage and cold acclimation responses in Arabidopsis leaves. Plant Methods 2008, 4, 12. [CrossRef] [PubMed]
126. IMAGE J. Available online: https://imagej.nih.gov/ij/ (accessed on 28 February 2018).
127. Lobet, G.; Draye, X.; Perilleux, C. An online database for plant image analysis software tools. Plant Methods 2013, 9, 38. [CrossRef] [PubMed]
128. Brewer, M.T.; Lang, L.; Fujimura, K.; Dujmovic, N.; Gray, S.; van der Knaap, E. Development of a controlled vocabulary and software application to analyse fruit shape variation in tomato and other plant species. Plant Physiol. 2006, 141, 15–25. [CrossRef] [PubMed]
129. Kamilaris, A.; Kartakoullis, A.; Prenafeta-Boldú, F.X. A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 2017, 143, 23–37. [CrossRef]