Validation of Automated Farming

This chapter presents first concepts for the improved validation of automated farming solutions. Within the ENABLE-S3 ECSEL JU project, the farming use case team presents developments within the agricultural domain, that can in the future improve the life and working environment of farmers. Applications such as autonomous driving of farming vehicles equipped with sensors and drones supporting hyperspectral cameras, validated by newly defined testing systems like co-simulation of farming vehicles, model-based simulation of farming systems and verification and testing of in-vehicle communication are advances developed during the project. As agricultural activities are very dependent on environmental parameters (e.g. weather, harvest ripeness) and the availability of the actual vehicles (which is very often not the case), the use case team opted for realistic simulators for first validation approaches. In this work, multiple simulators are introduced that combine many agricultural concepts including the simulation of the farming systems (i.e. harvester, tractors and drones). Additionally, introducing autonomy into vehicles requires deterministic in-vehicle communication and the guarantee that messages arrive timely. Validation of in-vehicle communication is introduced to showcase the applicability of the technology. The overall goal of the work performed in this use case is to reduce the testing costs and time of farming scenarios, be less dependent on many factors (like crop availability) and be able to perform continuous validation and verification of the farming systems.

[1]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[2]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[3]  Qiao Li,et al.  Timing Analysis of AVB Traffic in TSN Networks Using Network Calculus , 2018, 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS).

[4]  Jozef Hooman,et al.  Metric Temporal Logic with Durations , 1995, Theor. Comput. Sci..

[5]  Bradford W. Parkinson,et al.  Automatic Steering of Farm Vehicles Using GPS , 2015 .

[6]  Christophe Cariou,et al.  Automatic guidance of a four-wheel-steering mobile robot for accurate field operations , 2009 .

[7]  Marc Boyer,et al.  Complete modelling of AVB in Network Calculus Framework , 2014, RTNS.

[8]  Huimin Wang,et al.  Analysis of NDVI Data for Crop Identification and Yield Estimation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[10]  Antonio J. Plaza,et al.  The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends , 2013, Proceedings of the IEEE.

[11]  S. S. Ray,et al.  Hyperspectral Remote Sensing of Agriculture , 2015 .

[12]  Mustafa Teke,et al.  A short survey of hyperspectral remote sensing applications in agriculture , 2013, 2013 6th International Conference on Recent Advances in Space Technologies (RAST).

[13]  Joshué Pérez,et al.  A complete framework for developing and testing automated driving controllers , 2017 .

[14]  Sebastián López,et al.  A Novel Implementation of a Hyperspectral Anomaly Detection Algorithm For Real Time Applications With Pushbroom Sensors , 2018, 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[15]  Sebastian Lopez,et al.  Setting up an autonomous hyperspectral flying platform for precision agriculture (Conference Presentation) , 2018 .

[16]  Jorge Sousa Pinto,et al.  Runtime verification of autopilot systems using a fragment of MTL-$${\int }$$∫ , 2017, International Journal on Software Tools for Technology Transfer.

[17]  Wolfgang Rosenstiel,et al.  Simulation of falling rain for robustness testing of video-based surround sensing systems , 2016, DATE 2016.

[18]  Diego Domínguez,et al.  On the capabilities and limitations of high altitude pseudo-satellites , 2018 .

[19]  Joshué Pérez,et al.  Urban Motion Planning Framework Based on N-Bézier Curves Considering Comfort and Safety , 2018, Journal of Advanced Transportation.

[20]  Philippe Martinet,et al.  Adaptive and predictive non linear control for sliding vehicle guidance: application to trajectory tracking of farm vehicles relying on a single RTK GPS , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[21]  Christophe Cariou,et al.  Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations , 2009, J. Field Robotics.