Field Phenotyping Robot Design and Validation for the Crop Breeding

Abstract: Crop breeding is the focus of global agricultural hi-tech companies and requires phenotypes screening after growing and cultivating. However, rice phenotypic traits are not easily obtained with high-throughput in the field due to the complexity of unstructured environment. This paper presents a design solution of the field robot to construct visual space for measurement of rice traits. The truss system equipped with manipulator modules and vision system locates the object by the position information recorded in the phase of growing seedlings. 3 manipulators including the rice-separating manipulator, the height measuring manipulator and the panicle-expanding manipulator were designed by imitating people’s actions such as pushing adjacent rice, expanding the panicle and rubbing rice long in order to reduce the overlap. Combined with them, 3 imaging sensors including the CCD camera, the structured light sensor and the laser sensor were introduced to measure a phenotype quantitatively. The simulations were designed to obtain workspaces of manipulators. The results showed that the volume of reachable space of clapboards of the rice-separating manipulator was 1.6 × 10- 3 cubic meters, the effective movement of the height measuring manipulator was 1300mm and the maximum work area of the panicle-expanding manipulator was 32500 square millimeters. The results are consistent with those of manual operations. In conclusion, this paper describes an effective and reliable approach to acquiring phenotypic traits with high-throughput in the field.

[1]  Q. Luo,et al.  High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography. , 2011, The Review of scientific instruments.

[2]  B. Mueller‐Roeber,et al.  A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. , 2011, The New phytologist.

[3]  Jeffrey W. White,et al.  Field-based phenomics for plant genetics research , 2012 .

[4]  Hui Feng,et al.  Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer , 2015, Journal of experimental botany.

[5]  Falk Schreiber,et al.  HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.

[6]  E. Finkel.  Imaging. With 'phenomics,' plant scientists hope to shift breeding into overdrive. , 2009, Science.

[7]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[8]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[9]  Konrad Walus,et al.  Delivering high-resolution landmarks using inkjet micropatterning for spatial monitoring of leaf expansion , 2011, Plant Methods.

[10]  Qian Liu,et al.  Development of a whole-feeding and automatic rice thresher for single plant , 2013, Math. Comput. Model..

[11]  A. M. Edwards,et al.  Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time , 2015, Journal of experimental botany.

[12]  P. Langridge,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010, Science.

[13]  L. Xiong,et al.  Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. , 2013, Current opinion in plant biology.

[14]  Pierre Graebling,et al.  Robust Structured Light Coding for 3D Reconstruction , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Ross A Frick,et al.  Accurate inference of shoot biomass from high-throughput images of cereal plants , 2011, Plant Methods.