A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Computers and Electronics in Agriculture

Volumn 128, Issue , 2016, Pages 181-192

  Bai, Geng ^a   Ge, Yufeng ^a   Hussain, Waseem ^a   Baenziger, P Stephen ^a   Graef, George ^a  

^a UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Canopy reflectance; Canopy temperature; High throughput field phenotyping; LabVIEW; RGB image

Indexed keywords

AGRICULTURE; CORRELATION METHODS; CROPS; DATA ACQUISITION; REFLECTION; SPECTROMETERS;

CANOPY REFLECTANCE; CANOPY TEMPERATURE; LABVIEW; PHENOTYPING; RGB IMAGES;

THROUGHPUT;

CANOPY REFLECTANCE; CROP IMPROVEMENT; GPS; GROWING SEASON; LIMITING FACTOR; NDVI; PHENOTYPE; PLANT BREEDING; RADIOMETER; SENSOR; SOYBEAN; WHEAT;

GLYCINE MAX; TRITICUM AESTIVUM;

EID: 84987933135     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.08.021     Document Type: Article

References (35)

1. Andrade-Sanchez, P., Gore, M.A., Heun, J.T., Thorp, K.R., Carmo-Silva, A.E., French, A.N., Salvucci, M.E., White, J.W., Development and evaluation of a field-based high-throughput phenotyping platform. Funct. Plant Biol. 41 (2014), 68–79.
2. Ahamed, T., Tian, L., Jiang, Y., Zhao, B., Liu, H., Ting, K.C., Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections. Biosyst. Eng. 112 (2012), 93–107.
3. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plants Sci. 19 (2013), 52–61.
4. Campbell, M.T., Knecht, A.C., Berger, B., Brien, C.J., Wang, D., Walia, H., Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice. Plant Physiol. 168 (2015), 1476–1489.
5. Comar, A., Burger, P., de Solan, B., Baret, F., Daumard, F., Hanocq, J.-F., A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Funct. Plant Biol. 39 (2012), 914–924.
6. Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., Furbank, R., Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 5 (2014), 349–379.
7. Fahlgren, N., Feldman, M., Gehan, M., Wilson, M.S., Shyu, C., Bryant, D.W., Hill, S.T., McEntee, C.J., Warnasooriya, S.N., Kumar, I., Ficor, T., Turnipseed, S., Gilbert, K.B., Brutnell, T.P., Carrington, J.C., Mockler, T.C., Baxter, I., A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecul. Plant 8 (2015), 1–16.
8. Fanourakis, D., Briese, C., Max, J.F.J., Kleinen, S., Putz, A., Fiorani, F., Ulbrich, A., Schurr, U., Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture. Plant Meth., 10, 2014, 9.
9. Farooque, A.A., Chang, Y.K., Zaman, Q.U., Groulx, D., Schumann, A.W., Esau, T.J., Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time. Comput. Electron. Agri. 91 (2013), 135–144.
10. Furbank, R.T., Tester, M., Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16:12 (2011), 635–644.
11. Gamon, J.A., Serrano, L., Surfus, J.S., The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112 (1997), 492–501.
12. Garbulsky, M.F., Peñuelas, 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 Sens. Environ. 115 (2011), 281–297.
13. Ge, Y., Bai, G., Stoerger, V., Schnable, J.C., Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Comput. Electron. Agri. 127 (2016), 625–632.
14. Guanter, L., Rossini, M., Colombo, R., Meroni, M., Frankenberg, C., Lee, J.-E., Joiner, J., Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space. Remote Sens. Environ. 133 (2013), 52–61.
15. Kipp, S., Mistele, B., Baresel, P., Schmidhalter, U., High-throughput phenotyping early plant vigor of winter wheat. Eur. J. Agron. 52 (2014), 271–278.
16. Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R., Moreno, J., Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens. Environ. 113 (2009), 2037–2051.
17. Montes, J.M., Technow, F., Dhillon, B.S., Mauch, F., Melchinger, A.E., High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 121 (2011), 268–273.
18. Nelissen, H., Moloney, M., Inzé, D., Translational research: from pot to plot. Plant Biotechnol. J. 12 (2014), 277–285.
19. Noh, H., Zhang, Q., Shin, B., Han, S., Feng, L., A neural network model of maize crop nitrogen stress assessment for a multi-spectral imaging sensor. Biosyst. Eng. 94 (2006), 477–485.
20. Passioura, J.B., Phenotyping for drought tolerance in grain crops: when is it useful to breeders?. Funct. Plant Biol. 39 (2012), 851–859.
21. Paulus, S., Behmann, J., Mahlein, A.K., Plumer, L., Kuhlmann, H., Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 14 (2014), 3001–3018.
22. Paulus, S., Schumann, H., Kuhlmann, H., Léon, J., High-precision laser scanning system for capturing 3D plant architecture and analysis growth of cereal plants. Biosyst. Eng. 121 (2014), 1–11.
23. Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A., Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, 2012, 1293.
24. Romano, G., Zia, S., Spreer, W., Sanchez, C., Cairns, J., Araus, J.L., Müller, J., Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput. Electron. Agri. 79 (2011), 67–74.
25. Sankaran, S., Khot, L.R., Espinoza, C.Z., Jarolmasjed, S., Santhuvalli, V.R., Vandemark, G.J., Miklas, P.N., Carter, A.H., Pumphrey, M.O., Knowles, N.R., Pavek, M.J., Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron. 70 (2015), 112–123.
26. Scotford, I.M., Miller, P.C.H., Estimating tiller density and leaf area index of winter wheat using spectral reflectance and ultrasonic sensing techniques. Biosyst. Eng. 89 (2004), 395–408.
27. Sharma, B., Ritchie, G.L., High-throughput phenotyping of cotton in multiple irrigation environments. Crop Sci. 55 (2015), 958–969.
28. Shendure, J., Ji, H., Next-generation DNA sequencing. Nat. Biotechnol. 26 (2008), 1135–1145.
29. Sims, D.A., Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81 (2002), 337–354.
30. Sui, R., Thomasson, J.A., Hanks, J., Wooten, J., Ground-based sensing system for weed mapping in cotton. Comput. Electron. Agri. 60 (2008), 31–38.
31. Sui, R., Thomasson, J.A., Ground-based sensing system for cotton nitrogen status determination. Trans. ASABE 49 (2006), 1983–1991.
32. Wang, P., Lan, Y., Luo, X., Zhou, Z., Wang, Z., Wang, Y., Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle. Int. J. Agri. Biol. Eng. 7 (2014), 75–81.
33. White, J.W., Conley, M.M., A flexible, low-cost cart for proximal sensing. Crop Sci. 53 (2013), 1646–1649.
34. White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., et al. Field-based phenomics for plant genetics research. Field Crops Res. 133 (2012), 101–122.
35. Winterhalter, L., Mistele, B., Jampatong, S., Schmidhalter, U., High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur. J. Agron. 35 (2011), 22–32.