Abstract
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.
Highlights
The constantly increasing World population, likely to exceed nine billion by 2050, has led to a growing demand for agricultural products [1]
Whereas plant height can be measured with a single sensor, for example with a laser distance sensor measuring from top view into the plants or with a vertical light curtain penetrating the canopy, more complex traits like for example dry biomass yield require multi-sensor fusion concepts to achieve accurate results [19]
It is equipped with two 3D Time-of-Flight (ToF) cameras, a color camera, three laser distance sensors (LDS), a hyperspectral imaging (HSI) system and two light curtain (LC) imaging systems for phenotyping, a webcam for additional measurement documentation, as well as an incremental rotary encoder with a resolution of 1 mm and GPS receiver for positioning
Summary
The constantly increasing World population, likely to exceed nine billion by 2050, has led to a growing demand for agricultural products [1]. Measurements are limited with regard to the number of plots that can be evaluated and with regard to the traits that can be assessed This lack of suitable phenotyping capabilities is recognized as a major factor limiting the development of improved crop varieties [4,5,6]. Using single sensorrs e enables meaasuring simpple traits likke plant heiight, but fro om a techniccal point off view show ws constrainnts inn the determ mination acccuracy of more m compllex traits lik ke biomass yield. To overcome th his limitationn, thhe conceptt of sensor and data fusion has been sugg gested to enhance thee quality off phenotypic m measuremen nts for compplex traits [19]. Thee objectivess of this study were: (11) to develop a mobile multi-sensoor phenotyp ping platform m f small graain cereals under field conditions,, including the mechannical construuction and the for t hard- annd s software devvelopment for multi-ssensor data collection; (2) to impplement an applicable phenotypinng p procedure fo data colllection andd analysis and for a (3), to evaluate thhe data quaality generaated with thhe p phenotyping g platform
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