The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system
Norbert Kirchgessner A B , Frank Liebisch A , Kang Yu A , Johannes Pfeifer A , Michael Friedli A , Andreas Hund A and Achim Walter AA Institute of Agricultural Sciences, Group of Crop Sciences, ETH Zürich, Universitätstrasse 2, LFW C58, 8092 Zürich, Switzerland.
B Corresponding author. Email: norbert.kirchgessner@usys.ethz.ch
Functional Plant Biology 44(1) 154-168 https://doi.org/10.1071/FP16165
Submitted: 30 April 2016 Accepted: 5 September 2016 Published: 20 October 2016
Abstract
Crop phenotyping is a major bottleneck in current plant research. Field-based high-throughput phenotyping platforms are an important prerequisite to advance crop breeding. We developed a cable-suspended field phenotyping platform covering an area of ~1 ha. The system operates from 2 to 5 m above the canopy, enabling a high image resolution. It can carry payloads of up to 12 kg and can be operated under adverse weather conditions. This ensures regular measurements throughout the growing period even during cold, windy and moist conditions. Multiple sensors capture the reflectance spectrum, temperature, height or architecture of the canopy. Monitoring from early development to maturity at high temporal resolution allows the determination of dynamic traits and their correlation to environmental conditions throughout the entire season. We demonstrate the capabilities of the system with respect to monitoring canopy cover, canopy height and traits related to thermal and multi-spectral imaging by selected examples from winter wheat, maize and soybean. The system is discussed in the context of other, recently established field phenotyping approaches; such as ground-operating or aerial vehicles, which impose traffic on the field or require a higher distance to the canopy.
Additional keywords: crop, growth, image analysis.
References
Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014) Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology 41, 68–79.| Development and evaluation of a field-based high-throughput phenotyping platform.Crossref | GoogleScholarGoogle Scholar |
Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19, 52–61.
| Field high-throughput phenotyping: the new crop breeding frontier.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhs1CrtbjL&md5=1b60756cded27f694ab74a260c975182CAS | 24139902PubMed | 24139902PubMed |
Behmann J, Steinrucken J, Plumer L (2014) Detection of early plant stress responses in hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing 93, 98–111.
| Detection of early plant stress responses in hyperspectral images.Crossref | GoogleScholarGoogle Scholar |
Busemeyer L, Mentrup D, Moller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Wurschum T, Muller J, Rahe F, Ruckelshausen A (2013) BreedVision – a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13, 2830–2847.
| BreedVision – a multi-sensor platform for non-destructive field-based phenotyping in plant breeding.Crossref | GoogleScholarGoogle Scholar | 23447014PubMed | 23447014PubMed |
Chapman S, Merz T, Chan A, Jackway P, Hrabar S, Dreccer M, Holland E, Zheng B, Ling T, Jimenez-Berni J (2014) Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4, 279–301.
| Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping.Crossref | GoogleScholarGoogle Scholar |
Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C (2014) Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. The Plant Cell 26, 4636–4655.
| Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXislGmt74%3D&md5=5e48d49e661d43789fe018a849d55f3eCAS | 25501589PubMed | 25501589PubMed |
Christopher JT, Veyradier M, Borrell AK, Harvey G, Fletcher S, Chenu K (2014) Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics. Functional Plant Biology 41, 1035–1048.
| Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics.Crossref | GoogleScholarGoogle Scholar |
Deepfield Robotics (2015) BoniRob: adaptable multi-purpose robotic platform. Available at http://www.deepfield-robotics.com/de/BoniRob.html. [Verified 16 September 2016].
Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4, 349–379.
| Proximal remote sensing buggies and potential applications for field-based phenotyping.Crossref | GoogleScholarGoogle Scholar |
Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annual Review of Plant Biology 64, 267–291.
| Future scenarios for plant phenotyping.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXosFSktLw%3D&md5=b789b361b0b71f4d522d7e2a100da3a2CAS | 23451789PubMed | 23451789PubMed |
Foulkes MJ, Sylvester-Bradley R, Worland AJ, Snape JW (2004) Effects of a photoperiod-response gene Ppd-D1 on yield potential and drought resistance in UK winter wheat. Euphytica 135, 63–73.
| Effects of a photoperiod-response gene Ppd-D1 on yield potential and drought resistance in UK winter wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXpvFehsLk%3D&md5=71e038f0a0c4b97ee3699bce6ca9c00aCAS |
Friedli M, Kirchgessner N, Grieder C, Liebisch F, Mannale M, Walter A (2016) Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods 12, 9
| Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions.Crossref | GoogleScholarGoogle Scholar | 26834822PubMed | 26834822PubMed |
Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
| Phenomics – technologies to relieve the phenotyping bottleneck.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=b61a1e3f534159863d82ad6b44ce19fdCAS | 22074787PubMed | 22074787PubMed |
Furbank RT, von Caemmerer S, Sheehy J, Edwards G (2009) C4 rice: a challenge for plant phenomics. Functional Plant Biology 36, 845–856.
| C4 rice: a challenge for plant phenomics.Crossref | GoogleScholarGoogle Scholar |
Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux JJ, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 169, 623–635.
| PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.Crossref | GoogleScholarGoogle Scholar | 16411964PubMed | 16411964PubMed |
Grieder C, Hund A, Walter A (2015) Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature. Functional Plant Biology 42, 387–396.
| Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXksVSmsb0%3D&md5=2dea305576ff7c015ba63548dd6eb78aCAS |
Guo W, Rage UK, Ninomiya S (2013) Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Computers and Electronics in Agriculture 96, 58–66.
| Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model.Crossref | GoogleScholarGoogle Scholar |
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.
| Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture.Crossref | GoogleScholarGoogle Scholar |
Hamza MA, Anderson WK (2005) Soil compaction in cropping systems – a review of the nature, causes and possible solutions. Soil & Tillage Research 82, 121–145.
| Soil compaction in cropping systems – a review of the nature, causes and possible solutions.Crossref | GoogleScholarGoogle Scholar |
Hatfield JL, Prueger JH (2010) Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2, 562–578.
| Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices.Crossref | GoogleScholarGoogle Scholar |
Jerbi T, Wuyts N, Cane MA, Faux PF, Draye X (2015) High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest. Functional Plant Biology 42, 858–864.
| High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest.Crossref | GoogleScholarGoogle Scholar |
Jones HG, Serraj R, Loveys BR, Xiong LZ, Wheaton A, Price AH (2009) Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Functional Plant Biology 36, 978–989.
| Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field.Crossref | GoogleScholarGoogle Scholar |
Junker A, Muraya MM, Weigelt-Fischer K, Arana-Ceballos F, Klukas C, Melchinger AE, Meyer RC, Riewe D, Altmann T (2015) Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Frontiers in Plant Science 5, 770
| Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems.Crossref | GoogleScholarGoogle Scholar | 25653655PubMed | 25653655PubMed |
Khanna R, Möller M, Pfeifer J, Liebisch F, Walter A, Siegwart R (2015) ‘Beyond point clouds – 3D mapping and field parameter measurements using UAVs, emerging technologies and factory automation (ETFA). 2015 IEEE 20th conference’. Available at http://ieeexplore.ieee.org/ielx7/7295717/7301399/07301583.pdf?tp=&arnumber=7301583&isnumber=7301399 [Verified 16 September 2016].
Kipp S, Mistele B, Baresel P, Schmidhalter U (2014a) High-throughput phenotyping early plant vigour of winter wheat. European Journal of Agronomy 52, 271–278.
| High-throughput phenotyping early plant vigour of winter wheat.Crossref | GoogleScholarGoogle Scholar |
Kipp S, Mistele B, Schmidhalter U (2014b) Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques. Functional Plant Biology 41, 227
| Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXisF2rs7g%3D&md5=4e9a0f450ad52374d346776af8389adeCAS |
LemnaTec GmbH (2015) Infield Phenotyping by LemnaTec. Available at https://www.youtube.com/watch?v=Wj-U0QH5J_M [Verified 16 September 2016].
Liebisch F, Küng G, Damm A, Walter A (2014) Characterization of crop vitality and resource use efficiency by means of combining imaging spectroscopy based plant traits. In ‘IEEE Conference: Workshop on hyperspectral image and signal processing : evolution in remote sensing (WHISPERS), Lausanne, Switzerland. Vol. 6’. Available at https://www.researchgate.net/publication/263887298_Characterization_of_Crop_Vitality_and_Resource_Use_Efficiency_By_Means_of_Combining_Imaging_Spectroscopy_Based_Plant_Traits [Verified 16 September 2016].
Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11, 9
| Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach.Crossref | GoogleScholarGoogle Scholar | 25793008PubMed | 25793008PubMed |
Mahlein AK, Steiner U, Hillnhutter C, Dehne HW, Oerke EC (2012) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8, 3
| Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases.Crossref | GoogleScholarGoogle Scholar | 22273513PubMed | 22273513PubMed |
Mulla DJ (2013) Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Engineering 114, 358–371.
| Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps.Crossref | GoogleScholarGoogle Scholar |
Munns R, James RA, Sirault XR, Furbank RT, Jones HG (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. Journal of Experimental Botany 61, 3499–3507.
| New phenotyping methods for screening wheat and barley for beneficial responses to water deficit.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVert7jJ&md5=48224a12b558ef60caa2c61587d15aceCAS | 20605897PubMed | 20605897PubMed |
Parent B, Shahinnia F, Maphosa L, Berger B, Rabie H, Chalmers K, Kovalchuk A, Langridge P, Fleury D (2015) Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat. Journal of Experimental Botany 66, 5481–5492.
| Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXitVKnt77M&md5=ffbd6ce6982a9c99a6c8c73234528906CAS | 26179580PubMed | 26179580PubMed |
Peter R, Eschholz TW, Stamp P, Liedgens M (2009) Swiss Flint maize landraces – a rich pool of variability for early vigour in cool environments. Field Crops Research 110, 157–166.
| Swiss Flint maize landraces – a rich pool of variability for early vigour in cool environments.Crossref | GoogleScholarGoogle Scholar |
PHENOSPEX (2014) FieldScan ultra high throughput field phenotyping platform. Available at https://phenospex.com/products/plant-phenotyping/fieldscan-high-throughput-field-phenotpying-platform/ [Verified 16 September 2016].
Pimstein A, Eitel JUH, Long DS, Mufradi I, Karnieli A, Bonfil DJ (2009) A spectral index to monitor the head-emergence of wheat in semi-arid conditions. Field Crops Research 111, 218–225.
| A spectral index to monitor the head-emergence of wheat in semi-arid conditions.Crossref | GoogleScholarGoogle Scholar |
R Development Core Team (2015) ‘R: A language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria)
Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiology 131, 664–675.
| Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXhtlyjs7w%3D&md5=b32bf6184ae90affc6f55f5ae5550a16CAS | 12586890PubMed | 12586890PubMed |
Reynolds M, Foulkes MJ, Slafer GA, Berry P, Parry MAJ, Snape JW, Angus WJ (2009) Raising yield potential in wheat. Journal of Experimental Botany 60, 1899–1918.
| Raising yield potential in wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXmtFSjtbc%3D&md5=60902583d15e5e3822554cd0de028d41CAS | 1:CAS:528:DC%2BD1MXmtFSjtbc%3D&md5=60902583d15e5e3822554cd0de028d41CAS | 19363203PubMed | 19363203PubMed |
Roy DP, Zhang HK, Ju J, Gomez-Dans JL, Lewis PE, Schaaf CB, Sun Q, Li J, Huang H, Kovalskyy V (2016) A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sensing of Environment 176, 255–271.
| A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance.Crossref | GoogleScholarGoogle Scholar |
Sankaran S, Khot LR, Espinoza CZ, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Payek MJ (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. European Journal of Agronomy 70, 112–123.
| Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review.Crossref | GoogleScholarGoogle Scholar |
Svensgaard J, Roitsch T, Christensen S (2014) Development of a mobile multispectral imaging platform for precise field phenotyping. Agronomy 4, 322
| Development of a mobile multispectral imaging platform for precise field phenotyping.Crossref | GoogleScholarGoogle Scholar |
Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327, 818–822.
| Breeding technologies to increase crop production in a changing world.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhslWisLg%3D&md5=b2caf8a1006ace209e819db8aed0f155CAS | 20150489PubMed | 20150489PubMed |
University of Arizona (2015) Precision agriculture. Maricopa Agricultural Center. Available at http://cals-mac.arizona.edu/precision-agriculture. [Verified 16 September 2016].
Ustin SL, Valko PG, Kefauver SC, Santos MJ, Zimpfer JF, Smith SD (2009) Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert. Remote Sensing of Environment 113, 317–328.
| Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert.Crossref | GoogleScholarGoogle Scholar |
van Ginkel M, Reynolds MP, Trethowan R, Hernandez E (2008) Complementing the breeder’s eye with canopy temperature measurements. In ‘International symposium on wheat yield potential: challenges to international wheat breeding’. (Eds MP Reynolds, J Pietragalla, HJ Braun) pp. 134–135. (CIMMYT: Edo. de México, Mexico)
Wahabzada M, Mahlein AK, Bauckhage C, Steiner U, Oerke EC, Kersting K (2016) Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports 6, 22482
| Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XktVaitbo%3D&md5=e3d6461cd1aec883a05fd0516460925dCAS | 26957018PubMed | 26957018PubMed |
Walter A, Scharr H, Gilmer F, Zierer R, Nagel KA, Ernst M (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist 174,
| Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species.Crossref | GoogleScholarGoogle Scholar | 17504459PubMed | 17504459PubMed |
Wang CN, Hsu HC, Wang CC, Lee TK, Kuo YF (2015) Quantifying floral shape variation in 3D using microcomputed tomography: a case study of a hybrid line between actinomorphic and zygomorphic flowers. Frontiers in Plant Science 6, 724
| Quantifying floral shape variation in 3D using microcomputed tomography: a case study of a hybrid line between actinomorphic and zygomorphic flowers.Crossref | GoogleScholarGoogle Scholar | 26442038PubMed | 26442038PubMed |
Watts AC, Ambrosia VG, Hinkley EA (2012) Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sensing 4, 1671–1692.
| Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use.Crossref | GoogleScholarGoogle Scholar |
White JW, Conley MM (2013) A flexible, low-cost cart for proximal sensing. Crop Science 53, 1646–1649.
| A flexible, low-cost cart for proximal sensing.Crossref | GoogleScholarGoogle Scholar |
White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012) Field-based phenomics for plant genetics research. Field Crops Research 133, 101–112.
| Field-based phenomics for plant genetics research.Crossref | GoogleScholarGoogle Scholar |
Zarco-Tejada PJ, Gonzalez-Dugo V, Williams LE, Suarez L, Berni JAJ, Goldhamer D, Fereres E (2013) 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, 38–50.
| A PRI-based water stress index combining structural and chlorophyll effects: assessment using diurnal narrow-band airborne imagery and the CWSI thermal index.Crossref | GoogleScholarGoogle Scholar |
Zhang CH, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13, 693–712.
| The application of small unmanned aerial systems for precision agriculture: a review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtValsr%2FK&md5=4f44a9b0ea0acc4863fbc4607116b52cCAS |
Zheng BY, Biddulph B, Li DR, Kuchel H, Chapman S (2013) Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments. Journal of Experimental Botany 64, 3747–3761.
| Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXht1yrsrzJ&md5=846ba27c826042ad6bc4a50b63ae17efCAS |