Hardware and Embedded Algorithms for Real-Time Variable-Rate Fertiliser

Transcription

1 Hardware and Embedded Algorithms for Real-Time Variable-Rate Fertiliser Co-Authors: Gregory Falzon (PARG, UNE) Troy Jensen (NCEA, USQ) Bernard Schroeder (NCEA, USQ) David Lamb (PARG, UNE) Joshua Stover precision.agriculture

2 Introduction Low-powered systems in Precision Agriculture Automated variable-rate fertiliser Embedded machine learning Simulation and testing of embedded hardware

3 Background: Low Powered Systems Limitations for robotic PA field hardware Power Computational Data Storage Embedded designs not suitable as prototypes and POC Custom circuits and PCB's Slow, potentially expensive iteration cycle Use of an integrated, single-board computer helps

4 Background: Single-Board Computers Single-Board Computer (SBC) designs avoid some limitations by integrating: System-on-Chip (SoC) On-board peripherals External communication interfaces Volatile (RAM) and Non-Volatile (SD card) storage Popular designs using ARM architecture available now

5 Background: IPO Model Basic processing model commonly used in software engineering Can be used to generalise PA applications and solutions

6 Background: Machine Learning Machine Learning techniques currently being used in PA ANN (Various): Yield prediction (Pantazi et al., 2016) CNN: Plant phenotyping (Yahata et. al., 2017) ELM: Robotic vision / object detection (Sadgrove et. al., 2017) SVM: VRF Zone Segmentation (Falzon et al., 2012) Dynamic Aerial Survey (DAS) Used SVM to segment wheat field into VRF zones Batch processing of optical index data (NDVI)

7 Background: N Requirements / VRF Technological methods for N requirement estimation Optical Indices Multispectral Soil Conductivity Remote sensing platforms Satellite / Aerial / Ground VRF prescription maps Generated offline in batches Automated on-the-fly generation would be ideal

8 Methodology: VRF Metrics Fast prediction time Potentially high-speed environment (e.g. aircraft) Accurate (>85%) prediction of estimated N requirements Available inputs: Optical indices (NDVI, REIP, etc.) Multispectral imagery Ground based spectroscopy Ground conductivity (EM38) Fault tolerant

9 Methodology: Data Overview 18Ha Z30 stage wheat, Warialda NSW RNDVI (670nm) [Holland Scientific Crop Circle ACS211] Soil apparent electrical conductivity (EC a ) [Geonics EM38-MK2] Binary classifications made by expert agronomist 2399 total geo-referenced data instances (699 positive, 1700 negative)

10 Methodology: Goals Demonstrate testing on target hardware Simulation testing (hardware-in-the-loop) Use machine learning on low-powered systems Compare machine learning algorithms for VRF

11 Methodology: Simulation Testing

12 Methodology: Simulation Testing Microcontrollers programmed with dataset values used as input Allows for replaying of real-world data collection Easily migrated to final target hardware

13 Methodology: Fuzzy Box Algorithm Binary Classification Algorithm Designed with real-time and low-powered systems in mind Compliments the Dynamic Aerial Survey project Splits the range of each sensor range into increasing number of bins Avoids pre-scaling steps, no detriment to accuracy

14 Methodology: Fuzzy Box Algorithm Needs Fertiliser No Fertiliser

15 Results: Complexity Comparison Space Complexity Time Complexity ANN (Fully Connected) O(n 2 ) [1] O(n 2 ) [1] SVM (C-SVC) O(m 2 ) [2] O(m 3 ) [2] Fuzzy Box O(c n ) O(n) n sensors [1] Owechko and Shams, 1994 [2] Tsang et al., 2005 m training instances c box splits 7 20 splits 14GiB!

16 Results: Prediction Performance Accuracy Precision F 1 Score Gmean ANN SVM (C-SVC) Fuzzy Box (5) (even) Fuzzy Box (5) (percentile) Fuzzy Box (10) (even) Fuzzy Box (10) (percentile)

17 Results: Time Performance Training Time (ms) Prediction Time (s) Spatial BCM2837 Core i7 BCM2837 Core 2.2GHz (BCM2837) ANN mm SVM (C-SVC) mm Fuzzy Box (5) (even) mm Fuzzy Box (5) (percentile) mm Fuzzy Box (10) (even) mm Fuzzy Box (10) (percentile) mm *Calculated from the speed of Air Tractor AT-502B (60m/s)

18 Conclusion Simulation testing of real-world environments Eases software development No physical equipment required Machine learning is viable for embedded VRF Low prediction time ( < 2cm SR ) High prediction accuracy

19 Future Work Modelling and testing of the entire behaviour cycle Including output VRF stage Use latest research in correlating sensor data and N requirements Embedded vision based VRF prediction Optimising Fuzzy Box algorithm

20 Thank You! Joshua Stover (PARG, UNE) Gregory Falzon (PARG, UNE) Troy Jensen (NCEA, USQ) Bernard Schroeder (NCEA, USQ) David Lamb (PARG, UNE)

21 Real-Time Actuator Controller Dual system approach System 1 System 2 Updates the internal buffer with the latest sensor values Prediction is performed by the ML algorithm on the latest data, using trained model Prediction data is written to a buffer in shared memory Prediction data is read from shared memory buffer Output smoothing is applied Failsafe systems are checked VRF actuator is set to correct position

22 Methodology: Fuzzy Box ML algorithm Binary Classification Algorithm Designed with real-time and low-powered systems in mind Compliments the Dynamic Aerial Survey project Splits the range of each feature (f) into a number (S) of bins avoid pre-scaling steps, no detriment to performance For each S, an f-dimensional array is created to store the ratio of +/- class instances which fall into each box. For 2 features, this becomes a heat map similar to a KDE

23 Fuzzy Box: Splits vs. Accuracy

24 Performance Metrics Precision TPR / TPR + FPR Measure of correctly classified positive instances. Accuracy looks at TPR and TNR, over all instances. A good indication of how well the model can predict positive results. Geometric Mean GMEAN 1 = Sqr( TPR * P ) Geometric mean of P and TPR A type of mean or average. Indicates the central tendency or typical value. Takes TPR and P into account. Another good indication of how well the model can predict positive results. F-1 Score

25 Performance Metrics F-1 Score 2 * (P * TPR) / (P + TPR) Measures of a test s accuracy. Harmonic average of precision and recall. Best value is 1.