Breeding to adapt agriculture to climate change: affordable phenotyping solutions

Current Opinion in Plant Biology

Volumn 45, Issue , 2018, Pages 237-247

  Araus, José L ^a   Kefauver, Shawn C ^a  

^a UNIVERSITY OF BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURE; CLIMATE CHANGE; PHENOTYPE; PLANT BREEDING; PROCEDURES;

AGRICULTURE; CLIMATE CHANGE; PHENOTYPE; PLANT BREEDING;

EID: 85047460775     PISSN: 13695266     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pbi.2018.05.003     Document Type: Review

References (95)

1. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19 (2014), 52–61 A comprehensive view of crop phenotyping components and the most cited article since 2013 in Trends in Plant Science (https://www.journals.elsevier.com/trends-in-plant-science/most-cited-articles).
2. Fiorani, F., Schurr, U., Future scenarios for plant phenotyping. Annu Rev Plant Biol 64 (2013), 267–291.
3. Fahlgren, N., Gehan, M.A., Baxter, I., Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Current Opin Plant Biol 24 (2015), 93–99 Comprehensive description of the different categories of remote sensing techniques used for phenotyping.
4. Reynolds, M., Langridge, P., Physiological breeding. Curr Opin Plant Biol 31 (2016), 162–171.
5. Chapman, S.C., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M.F., Holland, E., Zheng, B., Ling, T.J., Jimenez-Berni, J., Pheno-copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4 (2014), 279–301.
6. Blum, A., Plant water relations, plant stress and plant production. Plant Breeding for Water-limited Environments, 2011, Springer, New York, 11–15.
7. Araus, J.L., Elazab, A., Vergara, O., Cabrera-Bosquet, L., Serret, M.D., Zaman-Allah, M., Cairns, J.E., New technologies for phenotyping. Phenomics, 2015, Springer International Publishing, 1–14.
8. Haghighattalab, A., Pérez, L.G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., Ortiz-Monasterio, I., Singh, R.P., Goodin, D., Poland, J., Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, 12, 2016, 35 Combination of both high throughput ability and affordability applied to large phenotyping trials, including comparisons of RGB with low-cost modified RGB and multispectral sensors.
9. Nebiker, S., Lack, N., Abächerli, M., Läderach, S., Light-weight Multispectral UAV Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases. Vol XX, 2016, ISPRS-Int Archiv Photogram, Remot Sens Spatial InfSci, 963–970.
10. Zhang, J., Yang, C., Song, H., Hoffmann, W.C., Zhang, D., Zhang, G., Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens, 8, 2016, 257.
11. Berra, E., Gibson-Poole, S., MacArthur, A., Gaulton, R., Hamilton, A., Estimation of the spectral sensitivity functions of un-modified and modified commercial off-the-shelf digital cameras to enable their use as a multispectral imaging system for UAVs. Int Arch Photogram, Remot Sens Spat Info Sci, 40, 2015, 207.
12. Cabrera-Bosquet, L., Fournier, C., Brichet, N., Welcker, C., Suard, B., Tardieu, F., High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol 212 (2016), 269–281.
13. Deery, D.M., Jimenez-Berni, J., Jones, H., Sirault, X., Furbank, R., Proximal remote sensing buggies and potential applications for field-based. Agronomy 4 (2014), 349–379.
14. Kirchgessner, N., Liebisch, F., Yu, K., Pfeifer, J., Friedli, M., Hund, A., Walter, A., The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol 44 (2017), 154–168.
15. Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., Hawkesford, M.J., Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44 (2017), 143–153.
16. Casadesús, J., Villegas, D., Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding. J Integr Plant Biol 56 (2014), 7–14 Describes in detail the capacity of conventional RGB cameras for quantifying two key physiological components for crop phenotyping.
17. Kefauver, S.C., Vicente, R., Vergara-Díaz, O., Fernandez-Gallego, J.A., Kerfal, S., Lopez, A., Melichar, J.P.E., Serret Molins, M.D., Araus, J.L., Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hybrid and conventional barley. Front Plant Sci, 8, 2017, 1733 Comparison of UAV and field sensor data acquisition limitations and capacities, with recommendations on the optimal use of each including a wide variety of state-of-the-art and low cost sensors.
18. Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B., Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sens Environ 56 (1996), 194–202.
19. 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, 1–5.
20. Mano, M., Igawa, M., A nondestructive method to estimate plant height, stem diameter and biomass of rice under field conditions using digital image analysis. J Environ Sci Nat Resour 10 (2017), 1–7.
21. Travlos, I., Mikroulis, A., Anastasiou, E., Fountas, S., Bilalis, D., Tsiropoulos, Z., Balafoutis, A., The use of RGB cameras in defining crop development in legumes. Adv Anim Biosci 8 (2017), 224–228.
22. Elazab, A., Ordóñez, R.A., Savin, R., Slafer, G.A., Araus, J.L., Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques. Eur J Agron 73 (2016), 11–24.
23. Vergara-Díaz, O., Zaman-Allah, M.A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B.M., Cairns, J.E., Araus, J.L., A novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization. Front Plant Sci, 7, 2016, 666.
24. Yousfi, S., Kellas, N., Saidi, L., Benlakehal, Z., Chaou, L., Siad, D., Herda, F., Karrou, M., Vergara, O., et al. Comparative performance of remote sensing methods in assessing wheat performance under mediterranean conditions. Agric Water Manag 164 (2016), 137–147.
25. Gracia-Romero, A., Kefauver, S.C., Vergara-Díaz, O., Zaman-Allah, M.A., Prasanna, B.M., Cairns, J.E., Araus, J.L., Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front Plant Sci, 8, 2017, 2004.
26. Zhou, B., Elazab, A., Bort, J., Vergara, O., Serret, M.D., Araus, J.L., Low-cost assessment of wheat resistance to yellow rust through conventional RGB images. Comput Electron Agric 116 (2015), 20–29.
27. Mutka, A.M., Fentress, S.J., Sher, J.W., Berry, J.C., Pretz, C., Nusinow, D.A., Bart, R., Quantitative, image-based phenotyping methods provide insight into spatial and temporal dimensions of plant disease. Plant Physiol, 2016, 10.1104/pp.16.00984.
28. Jin, X., Liu, S., Baret, F., Hemerlé M., Comar, A., Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remot Sens Environ 198 (2017), 105–114 A good example of a potential application of RGB images, other than formulating vegetation indices.
29. Fernandez-Gallego, J.A., Aparicio-Gutierrez, N., Nieto-Taladriz, M.T., Araus, J.L., Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images. Plant Methods, 14, 2018, 22.
30. Chéné Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Belin, É., Chapeau-Blondeau, F., On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric 82 (2012), 122–127.
31. Paulus, S., Behmann, J., Mahlein, A.K., Plümer, L., Kuhlmann, H., Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 14 (2014), 3001–3018.
32. Brocks, S., Bendig, J., Bareth, G., Toward an automated low-cost three-dimensional crop surface monitoring system using oblique stereo imagery from consumer-grade smart cameras. J Appl Remote Sens, 10, 2016, 046021.
33. Holman, F.H., Riche, A.B., Michalski, A., Castle, M., Wooster, M.J., Hawkesford, M.J., High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remot Sens, 8, 2016, 1031.
34. Rose, J.C., Kicherer, A., Wieland, M., Klingbeil, L., Töpfer, R., Kuhlmann, H., Towards automated large-scale 3D phenotyping of vineyards under field conditions. Sensors, 16, 2016, 2136.
35. Malambo, L., Popescu, S.C., Murray, S.C., Putman, E., Pugh, N.A., Horne, D.W., Richardson, G., Sheridan, R., Rooney, W.L., et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int J Appl Earth Observ Geoinfo 64 (2018), 31–42.
36. Pauli, D., Chapman, S.C., Bart, R., Topp, C.N., Lawrence-Dill, C., Poland, J., Gore, M.A., The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiol, 1, 2016, 00592.
37. Xu, Y., Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129 (2016), 653–673.
38. Xu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., Prasanna, B.M., Olsen, M.S., Enhancing genetic gain in the era of molecular breeding. J Exp Bot 68 (2017), 2641–2666 Compilation of the different factors that limit genetic advances, including spatial variability.
39. Zaman-Allah, M., Vergara, O., Araus, J.L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Albà A.H., Das, B., et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 2015, 35.
40. Lynch, J.P., Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot 112 (2013), 347–357.
41. Fenta, B.A., Beebe, S.E., Kunert, K.J., Burridge, J.D., Barlow, K.M., Lynch, J.P., Foyer, C.H., Field phenotyping of soybean roots for drought stress tolerance. Agronomy 4 (2014), 418–435.
42. Postic, F., Doussan, C., Benchmarking electrical methods for rapid estimation of root biomass. Plant Methods, 12, 2016, 33.
43. Sharma, S., Carena, M.J., BRACE: a method for high throughput maize phenotyping of root traits for short-season drought tolerance. Crop Sci 56 (2016), 2996–3004.
44. Rosales, A., Galicia, L., Oviedo, E., Islas, C., Palacios-Rojas, N., Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs. J Agric Food Chem 59 (2011), 10781–10786.
45. Gebreselassie, M.N., Ader, K., Boizot, N., Millier, F., Charpentier, J.P., Alves, A., Simões, R., Rodrigues, J.C., Bodineau, G., et al. Near-infrared spectroscopy enables the genetic analysis of chemical properties in a large set of wood samples from Populus nigra (L.) natural populations. Ind Crops Products 107 (2017), 159–171.
46. Quentin, A.G., Rodemann, T., Doutreleau, M.F., Moreau, M., Davies, N.W., Millard, P., Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière. Tree Physiol 37 (2017), 131–141.
47. Yang, Z., Nie, G., Pan, L., Zhang, Y., Huang, L., Ma, X., Zhang, X., Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. PeerJ, 5, 2017, e3867.
48. van Maarschalkerweerd, M., Husted, S., Recent developments in fast spectroscopy for plant mineral analysis. Front Plant Sci, 6, 2015, 169.
49. 13C discrimination in forages. J Range Manag 1 (1995), 132–136.
50. 13C in mature kernels of durum wheat. Aust J Agric Res 52 (2001), 809–816.
51. 15N in plant tissues with near-infrared reflectance spectroscopy. New Phytol 184 (2009), 732–739.
52. 18O and nitrogen and ash contents for improved yield potential and drought adaptation in maize. J Agric Food Chem 59 (2010), 467–474.
53. Garriga, M., Romero-Bravo, S., Estrada, F., Escobar, A., Matus, I.A., Del Pozo, A., Astudillo, C.A., Lobos, G.A., Assessing wheat traits by spectral reflectance: do we really need to focus on predicted trait-values or directly identify the elite genotypes group?. Front Plant Sci, 8, 2017, 280.
54. Serbin, S.P., Dillaway, D.N., Kruger, E.L., Townsend, P.A., Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. J Exp Bot 63 (2011), 489–502.
55. Heckmann, D., Schlüter, U., Weber, A.P.M., Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Mol Plant 10 (2017), 878–890.
56. Silva-Perez, V., Molero, G., Serbin, S.P., Condon, A.G., Reynolds, M.P., Furbank, R.T., Evans, J.R., Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. J Exp Bot, 2017, 10.1093/jxb/erx421 Proof of concept of the hyperspectral reflectance as a proxy to assess a wide range of relevant traits for breeding, including photosynthetic characteristics.
57. Yendrek, C.R., Tomaz, T., Montes, C.M., Cao, Y., Morse, A.M., Brown, P.J., McIntyre, L.M., Leakey, A.D., Ainsworth, E.A., High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiol 173 (2017), 614–626.
58. Weber, V.S., Araus, J.L., Cairns, J.E., Sanchez, C., Melchinger, A.E., Orsini, E., Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Res 128 (2012), 82–90.
59. Thorp, K.R., Wang, G., Bronson, K.F., Badaruddin, M., Mon, J., Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Comput Elect Agric 136 (2017), 1–12.
60. Hernandez, J., Lobos, G.A., Matus, I., del Pozo, A., Silva, P., Galleguillos, M., Using ridge regression models to estimate grain yield from field spectral data in bread wheat (Triticum aestivum L.) grown under three water regimes. Remote Sens 7 (2015), 2109–2126.
61. Montesinos-López, O.A., Montesinos-López, A., Crossa, J., de los Campos, G., Alvarado, G., Suchismita, M., Rutkoski, J., González-Pérez, L., Burgueño, J., Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods, 13, 2017, 4.
62. Montesinos-López, A., Montesinos-López, O.A., Cuevas, J., Mata-López, W.A., Burgueño, J., Mondal, S., Huerta, J., Singh, R., Autrique, E., González-Pérez, L., Crossa, J., Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data. Plant Methods, 13, 2017, 62.
63. Vergara-Díaz, O., Chairi, F., Vicente, R., Fernandez-Gallego, J.A., Nieto-Taladriz, M.T., Aparicio, N., Kefauver, S.C., Araus, J.L., Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes. J Exp Bot, 2018, 10.1093/jxb/ery109 ery109 (in press).
64. Gago, J., Douthe, C., Coopman, R., Gallego, P., Ribas-Carbo, M., Flexas, J., Escalona, J., Medrano, H., UAVs challenge to assess water stress for sustainable agriculture. Agric Water Manag 153 (2015), 9–19.
65. Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci, 38, 2017, 1111.
66. Stöcker, C., Bennett, R., Nex, F., Gerke, M., Zevenbergen, J., Review of the current state of UAV regulations. Remote Sens, 9, 2017, 459.
67. Crain, J.L., Wei, Y., Barker, J., Thompson, S.M., Alderman, P.D., Reynolds, M., Zhang, N., Poland, J., Development and deployment of a portable field phenotyping platform. Crop Sci 56 (2016), 965–975.
68. White, J.W., Conley, M.M., A flexible, low-cost cart for proximal sensing. Crop Sci 53 (2013), 1646–1649.
69. Winterhalter, L., Mistele, B., Schmidhalter, U., Evaluation of active and passive sensor systems in the field to phenotype maize hybrids with high-throughput. Field Crops Res 154 (2013), 236–345.
70. Rebetzke, G.J., Jimenez-Berni, J.A., Bovill, W.D., Deery, D.M., James, R.A., High-throughput phenotyping technologies allow accurate selection of stay-green. J Exp Bot 67 (2016), 4919–4924.
71. Chivasa, W., Mutanga, O., Biradar, C., Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review. Int J Remote Sens 38 (2017), 6816–6845.
72. Tattaris, M., Reynolds, M.P., Chapman, S.C., A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci, 7, 2016, 1131 One of the few examples of comparing phenotyping platforms, from ground to satellite levels.
73. Hand, E., CubeSats promise to fill weather data gap. Science 350 (2015), 1302–1303.
74. 2017 Nano/microsatellite forecast, SpaceWorks Enterprises, Inc. (SEI) (http://spaceworksforecast.com/docs/SpaceWorks_Nano_Microsatellite_Market_Forecast_2017.pdf).
75. 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 Crops Res 155 (2014), 38–41.
76. Patrignani, A., Ochsner, T.E., Canopeo: a powerful new tool for measuring fractional green canopy cover. Agron J 107 (2015), 2312–2320.
77. Chung, Y.S., Choi, S.C., Silva, R.R., Kang, J.W., Eom, J.H., Kim, C., Case study: estimation of sorghum biomass using digital image analysis with Canopeo. Biomass Bioenerg 105 (2017), 207–210.
78. Confalonieri, R., Paleari, L., Foi, M., Movedi, E., Vesely, F.M., Thoelke, W., Agape, C., Borlini, G., Ferri, I., Massara, F., Motta, R., PocketPlant3D: analysing canopy structure using a smartphone. Biosyst Eng 164 (2017), 1–12.
79. Orlando, F., Movedi, E., Coduto, D., Parisi, S., Brancadoro, L., Pagani, V., Guarneri, T., Confalonieri, R., Estimating leaf area index (LAI) in vineyards using the PocketLAI Smart-App. Sensors, 16, 2016, 2004.
80. De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bianchini, N., Smith, J., Collins, C., VitiCanopy: a free computer App to estimate canopy vigor and porosity for grapevine. Sensors, 16, 2016, 585.
81. Aquino, A., Diago, M.P., Millán, B., Tardáguila, J., A new methodology for estimating the grapevine-berry number per cluster using image analysis. Biosyst Eng 156 (2017), 80–95.
82. Rebetzke, G.J., Chenu, K., Biddulph, B., Moeller, C., Deery, D.M., Rattey, A.R., Bennett, D., Barrett-Lennard, E.G., Mayer, J.E., A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40 (2013), 1–13.
83. Bitella, G., Rossi, R., Bochicchio, R., Perniola, M., Amato, M., A novel low-cost open-hardware platform for monitoring soil water content and multiple soil-air-vegetation parameters. Sensors 14 (2014), 19639–19659.
84. Junker, A., Muraya, M.M., Weigelt-Fischer, K., Arana-Ceballos, F., Klukas, C., Melchinger, A.E., Meyer, R.C., Riewe, D., Altmann, T., Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Front Plant Sci, 5, 2015, 770.
85. Perez-Sanz, F., Navarro, P.J., Egea-Cortines, M., Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms. GigaSci 6 (2017), 1–18.
86. Hunt, E.R., Doraiswamy, P.C., McMurtrey, J.E., Daughtry, C.S., Perry, E.M., Akhmedov, B., A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Observ Geoinf 21 (2013), 103–112.
87. Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C., Internet of Things in agriculture, recent advances and future challenges. Biosyst Eng 164 (2017), 31–48 An excellent review with detailed descriptions of the future and coming importance of not only the Internet of Things (IoT), but also wireless sensor networks (WSNs) and mobile phones in agriculture.
88. Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., Borriello, G., Open data kit: tools to build information services for developing regions. Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, 2010, ACM, 18.
89. Brunette, W., Sodt, R., Chaudhri, R., Goel, M., Falcone, M., Van Orden, J., Borriello, G., Open data kit sensors: a sensor integration framework for android at the application-level. Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, 2012, ACM, 351–364.
90. González-Esquiva, J.M., Oates, M.J., García-Mateos, G., Moros-Valle, B., Molina-Martínez, J.M., Ruiz-Canales, A., Development of a visual monitoring system for water balance estimation of horticultural crops using low cost cameras. Comput Electron Agric 141 (2017), 15–26.
91. Boryan, C., Yang, Z., Mueller, R., Craig, M., Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int 26 (2011), 341–358.
92. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., et al. High-resolution global maps of 21st-century forest cover change. Science 342 (2013), 850–853.
93. Pekel, J.F., Cottam, A., Gorelick, N., Belward, A.S., High-resolution mapping of global surface water and its long-term changes. Nature 540 (2016), 418–422.
94. Kuhlgert, S., Austic, G., Zegarac, R., Osei-Bonsu, I., Hoh, D., Chilvers, M.I., Roth, M.G., Bi, K., TerAvest, D., Weebadde, P., Kramer, D.M., MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. Open Sci, 10, 2016, 160592.
95. Araus, J.L., Kefauver, S.C., Zaman-Allah, M., Olsen, M.S., Cairns, J.E., Translating high throughput phenotyping into genetic gain. Trends Plant Sci, 2018, 10.1016/j.tplants.2018.02.001.