IoT Middleware for Precision Agriculture: workforce monitoring in olive fields
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Presentation at the Smart Farming workshop in the Cyber Physical Week 2018
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IoT Middleware for Precision Agriculture: workforce monitoring in olive fields
- 1. IoT Middleware for Precision Agriculture José Camacho Alberto Cunha Miguel L. Pardal Workforce Monitoring in Olive Fields Smart Farming Workshop April 10th 2018
- 2. Food 2016 2 2050 7x109 people 7.95x108 without regular food supply UN data:
- 3. Agricultural challenge Efficiency Produce same with less time and effort Productivity Produce more Agricultural variability Profitability Balance costs and benefits 3
- 4. Tecnologies Actuators (Water pumps, Faucets, …) Sensor networks (Communication infrastructures in agricultural field) Global Positioning System (location and navigation – people and machines) Geographic Information System (terrain maps) Sensors (Humidity, Temperature, Atmosferic Pressure, …) 6
- 5. Agricultural production model “Integrated planning model for citrus agribusiness system using systems dynamics” por Ferreira, J. et al. 7
- 6. Current solutions require significant investment and are not suited for manual labor 8
- 7. Monitoring the workforce in specialty crops (Fruits, Flowers, Vegetables) Case Study: Olive Trees Case Study 9
- 8. Detect location and activities of the workers with off-the-shelf devices Smartphone Wearable 10
- 9. Challenges Tree interference Granularity Important in specialty crops Cost Ergonomics Minimum disturbance to worker’s movements Infrastructure Low communication and power resources 11
- 11. Location tracking • Precise worker location • GPS is used in large scale, mechanized agriculture • Two sources were evaluated: GPS and Dead Reckoning 14
- 12. Global Positioning System • 4 satellites are needed to determine receptor location • Available in most Smartphones • Average error of 5 to 10 meters outside, in the field • Trees cause signal interference 15
- 13. X YZ Accelerometer + Magnetometer Detect worker steps Give a direction to the steps Inertial Navigation technology Dead Reckoning 16
- 14. Dead Reckoning 2. Look for peaks above 12 m/s2 3. Discard other peaks in a period of 350ms 1. Calculate amplitude of acceleration applied to Smartphone sensor 4. Calculate new position with azimuth angle given by magnetometer Amplitude acceleration for 2 steps 17
- 15. GPS vs Dead Reckoning GPS Dead Reckoning Coordinates system Absolute Relative Dependency on external system Satellites GPS or GLONASS No Influence of trees in signal Yes No Power consumption High Moderate Average location error High (5 to 10 meters) Reduced Error accumulation No Yes Comparison between navigation technology available in Smartphones 18
- 16. Our solution Correct accumulated error from Dead Reckoning with absolute coordinates provided by GPS When GPS precision is better than 8 meters, the two navigation systems’ coordinates are averaged 19 + Dead Reckoning GPS
- 17. Results 1. Path correction Real path Dead Reckoning + GPS GPS Test performed with worker walking in a traditional olive field 20
- 18. Results 2. Energy consumption test Analyze power consumption using just GPS technology. Smartphone with a 3000mAh battery (Samsung Galaxy S7) Two tests were performed: a – capture location at each 60 seconds b – capture location at each 10 seconds Consumption: 2% of battery every 10 minutes Problem: May not be enough for a typical work day (8 hours) 21
- 19. Related work “A wearable module for recording worker position in orchards” Yannis Ampatzidis et. Al Monitoring system • Capture worker locations • Uses a dedicated Dead Reckoning device • GPS installed in agricultural machine • Solution capable of capturing the worker’s paths 22
- 21. Detect activities during the day through Machine Learning classification algorithms: BayesNet and MultilayerPerceptron from Weka library X YZ + + Accelerometer Magnetometer Gyroscope Activity detection accX accY accZ compX compY compZ gyroX gyroY gyroZ 26
- 22. Activity detection 27 Activities list: • Walk forward • Walk backward • Run • Pick fruit • Dig Photo of worker wearing smartphone in sleeve during activity detection Harvest process
- 23. Activity detection Sliding window solution 1 .. 75 … 150 151 74 75 … 150 .. 225 Initial window Window advance 3 seconds = 150 points x 20 ms 1,5 seconds intersection To each window’s data, we compute: average, minimum, maximum, kurtosis and standard-deviation Total of 45 features extracted from each window (3 sensors x 3 axis x 5 statistics)
- 24. Results CFS Subset Evaluator ➡ 11 features Most use data from accelerometer 29 Equipment: • Samsung Galaxy S7 • Battery 3000mAh • Sensors: • Accelerometer, Magnetometer, Gyroscope Learning: ~2 min of captured data for each activity
- 25. Results 1. Classification of activities by 1 worker Activity BayesNet MultilayerPerceptron Walk/Forward 75% 87,5% Run 100% 100% Walk/Backward 80,7% 82,46% Pickfruits 91,67% 91,67% Dig 98,04% 98,04% Learning period: less than 30 minutes Total captured data points: 255 Percentage of correctly classified activities >90% Good performance at distinguishing specific agriculture activities. Less effective at distinguishing walking forward and backward. 30
- 26. Results - Activities 2. Classification of activities by 2 workers Learning period: 0 minutes Activity BayesNet MultilayerPerceptron Walk/Forward 38,98% 42,37% Pick Fruits 81,35% 81,35% Dig 89,83% 91,52% Activity BayesNet MultilayerPerceptron Walk/Forward 38,60% 8,77% Pick Fruits 91,23% 92,98% Dig 87,5% 87,5% 2nd worker – percentage of correctly classified activities 1st worker – percentage of correctly classified activities 31
- 27. Conclusion • Solution allows monitoring workforce harvesting specialty crops • Solution goes beyond existing works by adding activity detection • Location monitoring (Dead Reckoning + GPS) • Able to locate workers in the field • Limited by power consumption • Activity detection • Percentage of correctly classified activities is above 90% 32
- 28. Future work • New sources of location data • Bluetooth Low Energy beacons • GSM antennas triangulation • Terrain layout with satellite images • Combine work team data • Address ethical concerns of privacy • Integrate worker data with crop field data: • Soil sensors • Machinery • Meteorology 33
- 29. Thank you Miguel.Pardal@tecnico.ulisboa.pt X YZ Master work by José Camacho Co-advised by Alberto Cunha