To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (× Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies.
References
[1]
Bruinsma, J. The Resource Outlook to 2050: By How Much Do Land, Water and Crop Yields Need to Increase by 2050? Proceedings of FAO Expert Meeting on How to Feed the World in 2050, Rome, Italy, 24–26 June 2009.
J？rg, P.; Guido, T.; Andreas, L.; Johannes, M.; Arno, R. Method for Opto-Electronic On-Line Measurement of Crop Density in site-Specific Farming (in German); Bornimer Agrartechnische Berichte; ATB: Potsdam, Germany, 2004; pp. 153–158.
[9]
Ehlert, D.; Horn, H.-J.; Adamek, R. Measuring crop biomass density by laser triangulation. Comput. Electr. Agr. 2008, 61, 117–125.
[10]
Busemeyer, L.; Klose, R.; Linz, A.; Thiel, M.; Wunder, E.; Ruckelshausen, A. Agro-sensor systems for outdoor plant phenotyping in low and high density crop field plots. Proceedings of Landtechnik 2010—Partnerschaften für neue Innovationspotentiale, Düsseldorf, Germnay, 27–28 October 2010; pp. 213–218.
[11]
Ehlert, D.; Heisig, M.; Adamek, R. Suitability of a laser rangefinder to characterize winter wheat. Prec. Agric. 2010, 11, 650–663.
[12]
Saeys, W.; Lenaerts, B.; Craessaerts, G.; de Baerdemaeker, J. Estimation of the crop density of small grains using LiDAR sensors. Biosyst. Eng. 2009, 102, 22–30.
[13]
Dzinaj, T.; Kleine H？rstkamp, S.; Linz, A.; Ruckelshausen, A.; B？ttger, O.; Kemper, M.; Marquering, J.; Naescher, J.; Trauts, D.; Wisserodt, E. Multi-Sensor-System zur Unterscheidung von Nutzpflanzen und Beikr？utern. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz 1998, XVI, 233–242.
[14]
Montes, J.; Technow, F.; Dhillon, B.; Mauch, F.; Melchinger, A. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 2011, 121, 268–273.
[15]
Erdle, K.; Mistele, B.; Schmidhalter, U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 2011, 124, 74–84.
[16]
Ferrio, J.; Villegas, D.; Zarco, J.; Aparicio, N.; Araus, J.; Royo, C. Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Res. 2005, 94, 126–148.
[17]
Gutierrez, M.; Reynolds, M.P.; Raun, W.R.; Stone, M.L.; Klatt, A.R. Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions. Crop Sci. 2010, 50, 197–214.
[18]
Pan, G.; Li, F.-M.; Sun, G.-J. Digital camera based measurement of crop cover for wheat yield prediction. Proceedings of IGARSS 2007: IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 797–800.
[19]
Fender, F.; Hanneken, M.; Linz, A.; Ruckelshausen, A.; Spicer, M. Imaging for Crop Detection Based on Light Curtains and Multispectral Cameras (in German); Bornimer Agrartechnische Berichte; ATB: Potsdam, Germany, 2005; pp. 7–16.
[20]
Ruckelshausen, A.; Dzinaj, T.; Gelze, F.; Kleine-H？rstkamp, S.L.A. Microcontroller-based multi-sensor system for online crop/weed detection. Proceedings of the International Brighton Conference “Weeds”, Brighton, UK, 15–18 November 1999; pp. 601–606.
[21]
Klose, R.; Pellington, J.; Ruckelshausen, A. Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Proceedings of 1st International Workshop on Computer Image Analysis in Agriculture, Potsdam, Germany, 27–28 August 2009; pp. 93–105.
[22]
Thiel, M.; Rath, T.; Ruckelshausen, A. Plant moisture measurement in field trials based on NIR spectral imaging—A feasability study. Proceedings of 2nd International Workshop on Computer Image Analysis in Agriculture, Budapest, Hungary, 26–27 August 2010; pp. 16–29.
[23]
Klose, R.; M？ller, K.; Vielst？dte, C.; Ruckelshausen, A. Modular System architecture for individual plant phenotyping with an autonomous field robot. Proceedings of the 2nd International Conference of Machine Control & Guidance, Bonn, Germany, 9–11 March 2010; pp. 299–307.
[24]
Lancashire, P.D.; Bleiholder, H.; van Boom, T.D.; Langelüddeke, P.; Stauss, R.; Weber, E.; Witzenberger, A. A uniform decimal code for growth stages of crops and weeds. Ann. Appl. Biol. 1991, 119, 561–601.
