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Robotics & Control in Agriculture Abubakr Muhammad Director, - PowerPoint PPT Presentation

Robotics & Control in Agriculture Abubakr Muhammad Director, Laboratory for Cyber Physical Networks and Systems Dept of Electrical Engineering SBA School of Science & Engineering Lahore University of Management Sciences (LUMS), Pakistan


  1. Robotics & Control in Agriculture Abubakr Muhammad Director, Laboratory for Cyber Physical Networks and Systems Dept of Electrical Engineering SBA School of Science & Engineering Lahore University of Management Sciences (LUMS), Pakistan EE361. Lectures on Control Engineering in Environment & Sustainability Spring 2015, LUMS

  2. Objectives • PLO-7: Environment and Sustainability (PEC  ) • Deeper motivation: Connecting technology to real- world and societal grand challenges  • Accessible introduction to cutting-edge research  • Pay attention to the Right Problems!  • Demonstrate how student involvement helps develop high impact research 

  3. Outline • Motivation: The importance and context of Agriculture in Pakistan • Precision Agriculture – A systems perspective • Feedback Control in Precision Ag – Auto-steering – Variable rate control • Ag Robotics: A new frontier in farm automation • The case for Automation & Robotics in Pakistan • Conclusions and outlook

  4. Agriculture at the Center Agriculture in Pakistan provides Agriculture • Food • Raw material • Foreign trade Commerce Industry 25% GDP, >50% of population in agriculture Pakistan’s Ag Profile Traditional : Rice, Cotton, Wheat, Sugarcane, Maize Upcoming : Livestock, Fisheries, Forestry, Horticulture

  5. Agricultural Footprint ~25% under cultivation of which 80% is irrigated.

  6. The Green Revolution in the Indus Basin • Irrigation canal networks (pre- and post-partition) • Emergence of agricultural research resulting in High Yield Varieties HYV (1950s) • New “miracle” seeds, fertilizers, mechanization, groundwater pumping, multiple cropping, processing, storage, financial measures etc. (1960s-1980s) • The boat we probably missed was the Gene revolution! (1990s-) (GM crops: herbicide-, disease-, drought-, insect- resistant varieties)

  7. Problems Related to Low Productivity Poor Economics 1. Under-utilization or over-exploitation of cultivable land, manpower 2. Uneconomic holdings (farm sizes) and defective land tenure system 3. Pricing and subsidies Poor Methodologies 1. Insufficient or inefficient use of Inputs (pesticide, fertilizers, seeds, mechanization, irrigation) 2. Water issues: logging, salinity, scarcity 3. Agricultural research and extension / education Poor Infrastructures Rural infrastructures and services ( roads, energy, distribution, storage…) 1. 2. Markets and financial institutions (access, credit, insurance, smuggling) Negative Driving Forces 1. Climate change, droughts, floods and natural disasters 2. Diseases and pests 3. Population growth and demographic transitions 4. Urbanization, globalization

  8. Problems Related to Low Productivity Poor Economics 1. Under-utilization or over-exploitation of cultivable land, manpower 2. Uneconomic holdings (farm sizes) and defective land tenure system 3. Pricing and subsidies Poor Methodologies 1. Insufficient or inefficient use of Inputs (pesticide, fertilizers, seeds, mechanization, irrigation) Robotics, 2. Water issues: logging, salinity, scarcity Automation 3. Agricultural research and extension / education & Control Poor Infrastructures Rural infrastructures and services ( roads, energy, distribution, storage…) 1. 2. Markets and financial institutions (access, credit, insurance, smuggling) Negative Driving Forces 1. Climate change, droughts, floods and natural disasters 2. Diseases and pests 3. Population growth and demographic transitions 4. Urbanization, globalization

  9. The Precision Ag Revolution Measure and respond against variability while optimizing returns. Courtesy. Tristan Perez, QUT, Australia

  10. The Precision Ag Revolution Measure and respond against variability while optimizing returns. Key technologies (1990-2010) • Variable rate input • GPS enabled auto-steering • Satellite imagery • Minimal / No tilling

  11. Control Architecture • ECUs on implements connected via CANBus Courtesy. Crop Protection, Australia

  12. GPS enabled Auto-steer Courtesy. Crop Protection, Australia Inter-row sowing

  13. Using Satellite Imagery for Yield Maps Courtesy. Andrew Robson, UNE

  14. Variable-Rate Treatment • Once mapped, how to act? (see e.g. weed map below) • Variable-rate (local measurements) Vs. Fixed rate (bulk measurement) Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  15. Variable-Rate Treatment • Real-time adaptive field spraying over weeds Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  16. Automatic Sensing & Spraying Courtesy. Horticulture Innovation Australia, Sugar Research Australia

  17. Active Spray Boom Suspension • Active suspension systems to counter soil unevenness. • The two hydraulic actuators counteract tractor yawing and jolting by moving the sledge in the opposite direction. Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  18. Modeling and Control • Textbook quarter-car suspension model • Disturbance, control • Disturbance step response

  19. Control Specification (Freq. Domain) • Tractor accelerations below 0.5 Hz are due to operator maneuvers • Only vibrational modes of the boom below 10 Hz contribute to an uneven spray deposition pattern . • Therefore isolator should attenuate boom accelerations between 0.5 and 10 Hz. Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  20. Model Identification • Plant identification • Separate modes – Rotational – Translational

  21. Controller Performance in Field • Sensitivity function E(s) = S(s)R(s) – S(s)G(s)W(s) + T(s)V(s) • Loop shaping • Boom tip movement (with and without control) Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  22. Fertilizer Spreader • Precise spreading of liquid menure • Disturbance: Variable vehicle speed • Variability: Slurry setpoint variation Ref. De Baerdemaeker et al. IEEE Control Systems Magazine, 2001

  23. End of Part 1

  24. Agricultural Robotics Courtesy. Eldert Van Henten, Waganengin University

  25. Ag Robotics Platforms • Farm mapping and autonomy • Yield estimation e.g. Almonds and Apples • Tree database e.g. Almonds • Precision weed sensing • Horticulture Courtesy. Horticulture Innovation Australia. CMU. USDA. Usyd, Bulent Ecevit Univ, Technion, Aalto

  26. ACFR Platform

  27. Robotics in Horticulture • Agronomic solutions - crop nutrition, canopy structure, pest numbers/identification, weed detection/removal, yield • Physiological solutions - flowering, fruit set, maturity indices (colour/sugar), forcasting/yield, abiotic stress (cold injury, drought, heat, salinity and metals) • Social solutions – safe, skilled and increased capability Ref. Anthony Kachenko, Horticulture Innovation Australia

  28. Yield Mapping • 3D vision algorithms • Active and Passive Sensing Courtesy. USDA, ACFR

  29. Fruit Picking Automation Courtesy. UC Davis

  30. Automatic weed spot spraying Courtesy. Horticulture Innovation Australia, Sugar Research Australia

  31. Automated Harvesting: Indoor / Greenhouse Courtesy. Eldert Van Henten, Waganengin University

  32. High Trellis Twining • Requires special string and knot for PNW windy environment • Tie on “infinitive” long cable, none similar mechanism usable • Very large number of knots (>4,000/ac) done in a short time window • Operating at a high elevation on unprepared ground surface with wind Courtesy. Qin Zhang. Washington State University

  33. Autonomous Land Vehicles for Demining & Agriculture ALVeDA & MDRD (2010-2013) Collaboration: RRLab, TU Kaiserslautern Funding: DAAD, LUMS, National Instruments Field Experiments: Channel mapping in Lahore (left). Objective: Push performance limits with low-cost vision sensors and simple mechatronics. Scanning a minefield in Beirut (right). Robot Vision: Terrain Classification, RGB-D & Monocular SLAM, Visual Servoing, Soil Estimation in a Bucket Excavator.

  34. Aerial Mapping of Irrigation Canals for Silt Deposition Collaboration: RRLab, TU Kaiserslautern Funding: DAAD, LUMS Examples of Siltation and bank deterioration in the Indus Basin. Motivation: Automation of annual canal cleaning operation in the world’s largest irrigation network. Proposed System Architecture. Guassian Processes (GP) based vol. Localization and navigation (online), estimation Mapping (offline)

  35. Ag Robotics Community • Emerging area: http://www.fieldrobot.com/ieeeras/ • Summer schools (Sydney 2015), workshops, special issues • IEEE AgRA (Robotics & Automation Society) technical committee.

  36. Some Basic Questions … • Why Automation in developing countries like Pakistan? – Devolution of governance – Ensuring rights – Conflict resolution Entitlements Participation • Major challenges – Natural resources – Food and Agriculture – Critical infra-structures Accountability – Security – Healthcare

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