3D Monitoring of Woody Crops Using a Medium-Sized Field Inspection Vehicle

In this work, a crop inspection system is presented. A mobile platform, based on a commercial electric vehicle, is equipped with different on-board sensors to inspection annual crops (maize, cereal, etc.) and multi-annual crops (orchards, vineyards, etc.). The use of a low-cost RGB-D sensor, the Microsoft Kinect v2 sensor, for the inspection of woody crops is tested. A method to generate automatic 3D reconstructions of large areas, such as a complete crop row, from the information directly supplied by the RGB-D sensor is shown as well as a procedure to correct the drift that appears in the reconstruction of crop rows. All these methods were tested and validated in real fields at different times throughout 2016. The development presented in this paper is a promising technology to achieve better crop management, which will increase crop yield.

[1]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time dynamic 3D surface reconstruction and interaction , 2011, SIGGRAPH '11.

[2]  Jiawen Chen,et al.  Scalable real-time volumetric surface reconstruction , 2013, ACM Trans. Graph..

[3]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[4]  Scott D. Roth,et al.  Ray casting for modeling solids , 1982, Comput. Graph. Image Process..

[5]  John J. Leonard,et al.  Kintinuous: Spatially Extended KinectFusion , 2012, AAAI 2012.

[6]  Claus G. Sørensen,et al.  The vehicle routing problem in field logistics: Part II , 2009 .

[7]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[8]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[9]  José Dorado,et al.  An Approach to the Use of Depth Cameras for Weed Volume Estimation , 2016, Sensors.

[10]  D. Bochtis,et al.  AE—Automation and Emerging TechnologiesThe vehicle routing problem in field logistics part I , 2009 .

[11]  Ming Zeng,et al.  Octree-based fusion for realtime 3D reconstruction , 2013, Graph. Model..

[12]  Changying Li,et al.  Size estimation of sweet onions using consumer-grade RGB-depth sensor , 2014 .

[13]  Lutz Plümer,et al.  Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping , 2014, Sensors.

[14]  Daniel Cremers,et al.  Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences , 2013, 2013 IEEE International Conference on Computer Vision.

[15]  Roland Siegwart,et al.  Conference Presentation Slides on Kinect v2 for Mobile Robot Navigation: Evaluation and Modeling , 2015 .

[16]  Adrian Hilton,et al.  Reliable Surface Reconstructiuon from Multiple Range Images , 1996, ECCV.

[17]  Angela Ribeiro,et al.  Efficient Distribution of a Fleet of Heterogeneous Vehicles in Agriculture: A Practical Approach to Multi-path Planning , 2015, 2015 IEEE International Conference on Autonomous Robot Systems and Competitions.

[18]  Matthias Nießner,et al.  Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..

[19]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[20]  Katsushi Ikeuchi,et al.  Consensus surfaces for modeling 3D objects from multiple range images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[21]  Vladlen Koltun,et al.  Dense scene reconstruction with points of interest , 2013, ACM Trans. Graph..