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Agriculture case study: Drones for
agriculture in East Africa
Dieudonné Harahagazwe (CIP), Elijah Cheruiyot (CIP) and
Arno...
Presentation outline
1) Evolution of low-cost platforms at CIP
2) Open source sensors and software by CIP
3) Examples of d...
2004
2008
2009
2012
1. Evolution of low-cost platforms at CIP
Balloons:
• Hot air.
• Hellium.
Model planes:
• Combustion.
...
2. Sensors & software by CIP (Open source)
IMAGri v2.0 - Multispectral Imaging System
Description:
The Multispectral Acqui...
NDVI IMAGri v2.0 vs ADC Tetracam
Altitude: 40m
Location: CIP-LIMA
ISAM V3.0 – Image stitching software
Description:
- Stitches 2 or more images into one (mosaic)
- Tests were performed wit...
3. Examples of applications at CIP
(1) Evaluating potato genotypes´ tolerance to
drought
(2) Estimating yield
36 days after planting (d.a.p)
45 d.a.p
84 d.a.p.
Spatial yield monitoring and prediction
NDVI
(3) Pests and diseases
Wavelenght (nm)
300 400 500 600 700 800 900
Reflectance(%)
0
10
20
30
40
50
CONTROL. 23dai
PYVVi. 2...
4. The Community of Practice - CoP
Why a CoP?
• Identify mechanisms that enable UAV ARSIS as a
sustainable tool;
• Identif...
A diversity of stakeholders in a
Community of Practice for innovation
Innovation Flow & Feedback Loops
• Developers
(Hardw...
Key Highlights:
– Costs, accessibility, and user-friendliness
– Involving local institution at different stages is a must
...
International Potato Center – UAV–
platform assembled and tested in
East Africa
Communication:
engaged with journalism
to ...
5. Crop area determination in Tanzania
UAV and accessories
1Check the propellers and drone's motors
2Drone's receiver
3Drone's MicroSD
4Frame
5Tester for batteri...
On-ground preparation and data acquisition
• Assemble and calibrate UAV
• Survey field
• Ground measurement
• Flight plans...
Data acquisition
Field data processing
Land use/cover in Kilosa, Tanzania (sample=100 ha)
Class Area m2
Banana 2246.37
Bare soil 22418.64
Bush/Shrub 165016.74
Co...
6. Key messages
• UAV-based technologies have a huge potential
in agricultural sector
• Quality of products generated is s...
RPAS-based remote sensing supporting
agriculture
CIP provides tailor-made solutions for the utilization
of Remotely Pilote...
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Agriculture case study: Drones for agriculture in East Africa

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Synthesis on the agricultural UAV-based remote sensing systems conducted by the International Potato Center (CIP) in close collaboration with University of Nairobi and University of Missouri, and through a community of practice.

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Agriculture case study: Drones for agriculture in East Africa

  1. 1. Agriculture case study: Drones for agriculture in East Africa Dieudonné Harahagazwe (CIP), Elijah Cheruiyot (CIP) and Arnold Bett (UoN) Drones East Africa Conference - IQPC Nairobi, 20-21 June 2017
  2. 2. Presentation outline 1) Evolution of low-cost platforms at CIP 2) Open source sensors and software by CIP 3) Examples of drone applications in the agricultural sector at CIP 4) Role of community of practice in generating and sustaining drone-based technologies 5) Field work for improving crop statistics in Tanzania – a proof of concept 6) Key messages
  3. 3. 2004 2008 2009 2012 1. Evolution of low-cost platforms at CIP Balloons: • Hot air. • Hellium. Model planes: • Combustion. • Electric. Helicopter: •Combustion. •Eléctric. Multirotor: • Quadracopter. • Octacopter.
  4. 4. 2. Sensors & software by CIP (Open source) IMAGri v2.0 - Multispectral Imaging System Description: The Multispectral Acquisiton system IMAGri v2.0 has a resolution of 640 x 480 pixels. Remarks: • Blueprints, software, camera and optics selection are open access. • Adaptation for different indices (e. g. PRI). • Can be adapted to any UAV that can carry more than 800 g. • The upcoming version will incorporate correction for light conditions.
  5. 5. NDVI IMAGri v2.0 vs ADC Tetracam Altitude: 40m Location: CIP-LIMA
  6. 6. ISAM V3.0 – Image stitching software Description: - Stitches 2 or more images into one (mosaic) - Tests were performed with TETRACAM Micro, Snap, ADC (3 bands) and IMAGri (2 bands). - Current Version can join 5 bands MCA TETRACAM images Remarks: Source code, final product, tutorials and sample image are free access in our website - GNU GPL License
  7. 7. 3. Examples of applications at CIP (1) Evaluating potato genotypes´ tolerance to drought
  8. 8. (2) Estimating yield 36 days after planting (d.a.p) 45 d.a.p 84 d.a.p.
  9. 9. Spatial yield monitoring and prediction NDVI
  10. 10. (3) Pests and diseases Wavelenght (nm) 300 400 500 600 700 800 900 Reflectance(%) 0 10 20 30 40 50 CONTROL. 23dai PYVVi. 23dai Reflectance(%) 0 10 20 30 40 CONTROL. 17dai PYVVi. 17dai 350 400 450 500 550 600 650 700 3 4 5 6 7 8 9 0/50 Greenhouse experiment with Potato Yellow Vein Virus Cassava experiment
  11. 11. 4. The Community of Practice - CoP Why a CoP? • Identify mechanisms that enable UAV ARSIS as a sustainable tool; • Identify actions to achieve the outcome; • Determine how to implement these actions in each context?  What collaborations are necessary?  What capacities and learning are required, and how to develop these?  What opportunities exist to pursue these activities?  How can constraints be addressed?
  12. 12. A diversity of stakeholders in a Community of Practice for innovation Innovation Flow & Feedback Loops • Developers (Hardware & Software) Developing • Application Scientists (Exploring Applications; Field Experiments) Exploring Potential Uses • Users of the information • Enablers Real world Applications
  13. 13. Key Highlights: – Costs, accessibility, and user-friendliness – Involving local institution at different stages is a must – Stepwise – From simple to complex tools – Complementarity with satellite imageries – Multiple crops – Yield assessment? – Is it feasible to discriminate varieties? 32 Participants: – National, Regional and International institutions – 5 CGIAR Centers (CIP, ICRAF, CIAT, IITA and ILRI), and ICIPE Inception Workshop – October 2014, Nairobi
  14. 14. International Potato Center – UAV– platform assembled and tested in East Africa Communication: engaged with journalism to communicate with the public, and training videos about UAV-ARSIS. Workshop II June 6-7, 2016 Identifying innovation pathways through participatory processes in Africa Stakeholder group discussions to identify outcomes and actions Technology Fair: Learning about UAVs & ARSIS
  15. 15. 5. Crop area determination in Tanzania
  16. 16. UAV and accessories 1Check the propellers and drone's motors 2Drone's receiver 3Drone's MicroSD 4Frame 5Tester for batteries 6Conector "Y" for batteries 7Fully charged battery of Transmiteer RC 8Fully charged battery of Laptop 9Image from the place 10Waypoints 11Range Extender 12Plastic seats 13Stopwatch 14Sunglasses Flight with Tetracam 1Camera housing 2TTC camera 3Ground Calibration Target 4Compactflash memory card empty 5Fully charged battery of camera Flight with MicroTTC 1Camera housing 2 MicroTTC camera 3 Ground Calibration Target 4MicroSD memory card empty 5Fully charged battery (Lipo 850mAh ) Flight with Sony camera 1Camera housing 2Camara Sony 3SD memory card empty 4Fully charged battery of camera  Site identification and reconnaissance  Stakeholder participation  Permits  Set mission objectives, which determines: • Type of UAV • Sensors • Image resolutions • Time to acquire data, e.g. growing seasons Pre-mission preparation
  17. 17. On-ground preparation and data acquisition • Assemble and calibrate UAV • Survey field • Ground measurement • Flight plans • Data acquisition with UAV
  18. 18. Data acquisition Field data processing
  19. 19. Land use/cover in Kilosa, Tanzania (sample=100 ha) Class Area m2 Banana 2246.37 Bare soil 22418.64 Bush/Shrub 165016.74 Cowpeas 10282.3 Fallow 18552.65 Flooded land 19001.4 Paddy Rice 347.93 Grass 46334.72 Homestead 7001.58 Maize&Sesame 1194.4 Maize 85613.98 Maize&Beans 1664.7 Maize&PigeonPeas 21110.6 Maize&SunFlower 13434.66 No data 16958.61 Pigeon Pea 37540.3 Rice 1270.89 Road 25954.66 Sesame 401975.84 Sesame&Pigeon 2311.95 Sunflower 189.69 Recently planted land 51084.71 Water 2406.1
  20. 20. 6. Key messages • UAV-based technologies have a huge potential in agricultural sector • Quality of products generated is strongly dependent on the quality of sensors and data processing techniques used. • Establishing a CoP is an excellent approach for developing technologies that respond to local needs for enhanced adoption and sustainability • These technologies will only work in the region if there is an enabling environment (policies and capacity building) – REGIONAL HUB
  21. 21. RPAS-based remote sensing supporting agriculture CIP provides tailor-made solutions for the utilization of Remotely Piloted Aircraft Systems suited with required sensors and open source software to register images, process data and generate the information needed for phenotyping and timely agricultural decision making. Research Team: Roberto Quiroz Adolfo Posadas Hildo Loayza Corinne Valdivia Susan Palacios Mario Balcázar Luis Silva Mariella Carbajal Percy Zorogastúa Felipe de Mendiburu Elijah Cheruiyot Arnold Bett Dieudonné Harahagazwe Rodrigo Morales Mariana Cruz Carolina Barreda Software: http://cipotato.org/csicc/download Videos: http://cipotato.org/csicc/videos Thanks for your attention www.uav4ag.org Contact: Roberto Quiroz at r.quiroz@cgiar.org

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