Garuda Robotics x DataScience SG Meetup (Sep 2015)
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The document discusses the use of drones in precision agriculture, specifically for palm oil plantations, highlighting their role in solving customer problems and gathering actionable data. It details workflows for data acquisition, analysis, and presentation, emphasizing the importance of predictive and prescriptive analytics over mere descriptive analysis. Case studies, such as tree counting, demonstrate the advanced capabilities of drone technology for enhancing agricultural efficiency and decision-making.
![Back to our case study ::
Plantation Management
Dry
leaves,
but
next
to
river.
Why?
Empty
space,
but
no
palm
planted.
Why?
Winding
road,
difficult
to
bring
harvested
palm
out.
Redo?
Palm
of
mixed
age:
high
maintenance
cost.
Is
it
1me
to
replant
the
en1re
area?
If
so,
should
the
river
be
shi]ed
for
water
to
drain
be^er?](https://image.slidesharecdn.com/dssgmeetupclean-150928142023-lva1-app6891/85/Garuda-Robotics-x-DataScience-SG-Meetup-Sep-2015-27-320.jpg)
Garuda Robotics x DataScience SG Meetup (Sep 2015)
- 1. Drones At Work :: Capturing Data, Generating Insights, Solving Real Problems Ong Jiin Joo CTO, Garuda Robotics jiinjoo@garuda.io DSSG - 23/9/2015
- 2. Drones at work gathering useful data (1) Not flying for fun (2) Not flying and shoo5ng for aesthe5cs Drones at work: (1) Solve Customer’s Problems (2) Gather Useful Data: Can be analyzed to produce intelligence / insights
- 3. In the next 45 minutes • Share our experience – Behind the scenes – Technology and processes • Data capture workflow • Data analysis workflow • Precision agriculture case study: Tree counting
- 4. Case Study Background Running example in this presenta1on: Precision Agriculture for Palm Oil Planta5ons Planta1on customers want to know: How many trees are there in my planta5on? This affects: (i) Manpower & equipment planning, (ii) fer1lizer purchase and dissemina1on
- 5. In case you haven’t heard of Drones / UAVs …
- 6. Our Workflow Data Acquisi5on / Genera5on Data Storage / Transporta5on Data Analy5cs / Presenta5on 1 2 3
- 7. Data Acquisition :: Planning Project Planner -‐ Define objec1ves, targets, obstacles, deliverables
- 8. Data Acquisition :: Before Flight Prepara5on for deployment On-‐site equipment prepara5on
- 9. Data Acquisition :: Before Flight Onsite systems prepara5ons Mission planning and briefing
- 10. Data Acquisition :: During Flight • Autonomous Flight – Monitor telemetry, video feed . HUD (head up display) Antenna
- 11. Data Acquisition :: After Flight 1. Check Data Integrity • Is the picture clear, focused? 2. Quick Process • Low res img processing
- 12. Data Generation :: Comparing pictures taken over a period of time
- 13. Data Generation :: Combining various electromagnetic spectrum
- 14. Data Transportation :: Live
- 15. Urgency of analysis • When do we need the deliverable – Real Time or within minutes / hours – Non Real Time (days / weeks) • Some analysis require huge amount of compute – such as image recognition
- 16. Tradeoff between using more bandwidth to transport data elsewhere vs. shipping more compute power on site In-‐country Telco Ground Sta1on Drone Cloud Services Wi-‐Fi 3G Dongle Internet backbone
- 17. Fallback plan – transport the old way
- 18. Data Storage Size Photogrammetry Example (simplified) • Fly at 100m, Camera FOV 90° both sides, 1 picture covers 200x200m = 4 ha • Suppose plantation 10,000 ha square (or 10km by 10km) • 80% overlap required ~= shooting 5 times same area • Total size: (10,000/4) * 5MB * 5 = 62.5GB – fits one 64GB SD card.
- 19. Data Analytics • More on this on part 2 of the presentation
- 20. Data Presentation :: Image Stitching • Combine everything or by blocks • Highly repe11ve • Lack control points
- 21. Data Presentation :: Orthomosaics • Geometrically corrected • Can be placed on map Used by surveyors to measure true distance
- 22. Data Presentation :: 3D Reconstruction • Photogrammetry methods Similarly, used by surveyors to measure length, area and volume of interest in 3D space
- 24. Data Analytics Framework Descrip1ve Analy1cs Predic1ve Analy1cs Prescrip1ve Analy1cs + +
- 25. Descriptive Analytics Example: Telco Tower Inspec5on • Is the antenna s1ll slanted at 2.8 degrees from ver1cal? • Any disconnected wires, bird nest, damage from harsh weather? Example: Flare Stack Inspec5on • Is the structural integrity of the flare stack holding up? • Is the flare stack opera1ng at normal temperature?
- 26. Predictive Analytics What will happen next? Example: Solar Panel • What is wrong? • How many times observed • Correlate with electricity yield curve
- 27. Back to our case study :: Plantation Management Dry leaves, but next to river. Why? Empty space, but no palm planted. Why? Winding road, difficult to bring harvested palm out. Redo? Palm of mixed age: high maintenance cost. Is it 1me to replant the en1re area? If so, should the river be shi]ed for water to drain be^er?
- 28. Case Study :: Plantation Management Great! Now I just have to keep it going for 25 years How much fer1lizers do I need to get? How should I distribute them so that my workers don’t just throw excess away? How many trees do I have!?
- 29. Tree Counting • Conventional way(s) – Ground staff count them one by one! – Guesstimate (e.g. 143 trees / ha)
- 30. Tree Counting The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. • More advanced ways 1. Satellite imagery 2. Drone imagery 3. Apply Computer vision
- 31. Tree Counting Posi1ve Features Histogram of • Colour • Intensity • Mean • Standard Devia1on Nega1ve Features (random sampling) Our naïve model! RFC
- 32. Tree Counting • Didn’t work so well… How can we do beZer?
- 33. Ways to improve tree counting • Non-CV techniques – Operations: capture trees at same size and light intensity (vary altitude, time of flight etc.) – Domain info: planting patterns, tree distance, max tree per block – Past data: information from previous flights, manual count, last count • How about CV techniques?
- 34. Ways to improve tree counting Source: Oil Palm Tree Detec2on with High Resolu2on Mul2-‐Spectral Satellite Imagery h?p://www.mdpi.com/2072-‐4292/6/10/9749?trendmd-‐shared=0 13 April 2014
- 35. Ways to improve tree counting Active research area • Some new proposals • Undergoing R&D and trials with our corpus • Trials with customer with existing data about their tree count
- 36. Tree Counting :: Next Steps • Impact from good tree count – Yield prediction and correction – Plantation ops – Prescriptive Analytics together with Arborists • Next things to classify – Healthy trees vs. sick trees – Other trees / crops – Heterogeneous plantations
- 37. Summary • Drones are already at work delivering actionable insights • We can capture the data with our drones, but the challenge is to go beyond the descriptive into the predictive and prescriptive analytics • Lots of opportunities coming soon
- 38. Thank You