The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping

The established processes for measuring physiological and morphological traits (phenotypes) of crops in outdoor test plots are labor intensive and error-prone. Low-cost, reliable, field-based robotic phenotyping will enable geneticists to more easily map genotypes to phenotypes, which in turn will improve crop yields. In this paper, we present a novel robotic ground-based platform capable of autonomously navigating below the canopy of row crops such as sorghum or corn. The robot is also capable of deploying a manipulator to measure plant stalk strength and gathering phenotypic data with a modular array of non-contact sensors. We present data obtained from deployments to Sorghum bicolor test plots at various sites in South Carolina, USA.

[1]  Metsävarojenkäytön laitos,et al.  Soil Interaction model. , 2002 .

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

[3]  Tony E Grift,et al.  Variable field-of-view machine vision based row guidance of an agricultural robot , 2012 .

[4]  Heiner Kuhlmann,et al.  Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping , 2013, BMC Bioinformatics.

[5]  Stephen Nuske,et al.  Texture-based fruit detection via images using the smooth patterns on the fruit , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Matthew N. Dailey,et al.  Automatic morphological trait characterization for corn plants via 3D holographic reconstruction , 2014 .

[7]  Ji Zhang,et al.  Monocular visual navigation of an autonomous vehicle in natural scene corridor-like environments , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Alan M. Lefcourt,et al.  A survey of unmanned ground vehicles with applications to agricultural and environmental sensing , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[9]  C. W. Bac,et al.  Improving obstacle awareness for robotic harvesting of sweet-pepper , 2015 .

[10]  Jeffrey W. White,et al.  Development and evaluation of a field-based high-throughput phenotyping platform. , 2013, Functional plant biology : FPB.

[11]  R. C. Coulter,et al.  Implementation of the Pure Pursuit Path Tracking Algorithm , 1992 .

[12]  Eric Claesen,et al.  Autonomous Fruit Picking Machine: A Robotic Apple Harvester , 2007, FSR.

[13]  C. W. Smith,et al.  Sorghum: origin, history, technology and production. , 2000 .

[14]  Sanjiv Singh,et al.  Results with autonomous vehicles operating in specialty crops , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[16]  Ryan F. McCormick,et al.  Energy sorghum--a genetic model for the design of C4 grass bioenergy crops. , 2014, Journal of experimental botany.

[17]  Nithya Rajan,et al.  High clearance phenotyping systems for season-long measurement of corn, sorghum and other row crops to complement unmanned aerial vehicle systems , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[18]  Achim Walter,et al.  Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach , 2015, Plant Methods.

[19]  Thomas Moore,et al.  A Generalized Extended Kalman Filter Implementation for the Robot Operating System , 2014, IAS.

[20]  Thomas Bak,et al.  Agricultural Robotic Platform with Four Wheel Steering for Weed Detection , 2004 .

[21]  Ji Zhang,et al.  3D perception for accurate row following: Methodology and results , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.