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Site specific weed management: mapping the tempo-spatial development of purple nutsedge ( Cyperus rotundus ) Hanan Eizen

The 2 nd International Conference on: Novel and Sustainable Weed Management in Arid and Semi-Arid Agro-Ecosystems. Site specific weed management: mapping the tempo-spatial development of purple nutsedge ( Cyperus rotundus ) Hanan Eizenberg Tal Naamat, Ran Lati , Tal Miller and Roy Efron

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Site specific weed management: mapping the tempo-spatial development of purple nutsedge ( Cyperus rotundus ) Hanan Eizen

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  1. The 2nd International Conference on: Novel and Sustainable Weed Management in Arid and Semi-Arid Agro-Ecosystems Site specific weed management: mapping the tempo-spatial development of purple nutsedge (Cyperusrotundus) Hanan Eizenberg Tal Naamat, Ran Lati, Tal Millerand Roy Efron Dept. of Weed Research, ARO, NeweYa’ar Research Center, Ramat Yishay 30095, Israel

  2. Objective: • The main objective of the entire study is to develop a multiple approach for the detection of purple nutsedge spatial development • To achieve our objective, the state of art in detection technologies for both above and sub-surface spatial development were used

  3. Our approach thus includes: • Spectral analysis of purple nutsedge obtained from satellites or airborne • Leaf shape analysis obtained from image data • photogrammetric (3D) analysis obtained from image data and laser (LIDAR) • Modeling spatial sprouting of the weed under different environmental conditions • Modeling the soil-subsurface development and its reflectance on purple nutsedge spatial sprouting

  4. Topic Technology Modeling spatial growth for the above and sub-surface RGB high resolution camera; 0.5x0.5 mm pixel size. Lati et al. Minirhyzotronnon destructive video camera. Naamat et al. RGB high resolution camera; 0.5x0.5 pixel size. 3D LIDAR scenes and photogrammetric. Lati et al. RGB high resolution camera; 1x1mm pixel size. Efron et al. Close range remote sensing Aerial RGB high resolution camera; 50x50mm pixel size. Tal Miller et al. Far range remote sensing

  5. Examples 1. Monitoring the sub-surface development of purple nutsedge using minirhyzotron video camera

  6. 13.2.08 9.3.08 23.3.08

  7. Examples 2. Developing a robust method for purple nutsedge detection under varies light conditions or How to overcome the shading effect?

  8. Fig. 1 1-a 1-b 1-c 2-b 2-a 2-c 3-a 3-b 3-c 4-a 4-b 4-c

  9. Biological model Above soil surface growth model Purple nutsedge spatial growth model Sub surface growth model

  10. Laser Scanner (description) Color digital camera Laser Scanner

  11. Results from cotton field, NeweYaar, Israel

  12. Ranging - travel time based measurement Knowledge of the laser beam direction  determination of point in space Direct measurement of 3D coordinates of the objects in space Panoramic scan - complete 3D depiction of the surveyed scene can be achieved – generates 3D point clouds Laser Scanner (principles)

  13. Examples 3. Weed Mapping using web cam (RGB)

  14. Remote sensing using close range, high resolution (2 mega-pixel) RGB cameras • Collecting geospatial information using dGPS (sub-meter accuracy) • Logging information onto an onboard computer

  15. GPS Antenna Camera 1 Field Of View Camera 2 Field Of View Computer ECPA - July 2008

  16. 14_right_cam.avi MOV07482.MPG

  17. Binary Image

  18. Preliminary Classified Image

  19. Secondary Classified Image dGPS Data Weed Map

  20. Weed Map • Number of weeds per image • Area of the weeds

  21. Summary Biology Technology Remote sensing for purple nutsedge detection Input of meteorological data (T°C; PAR) Close range Terrestrial sensors RGB, NIR, LIDAR Far range aerial sensors Satellite; airborne Soil Sub-surface model Above surface growth sub-model Developing an algorithm for weed detection – range related Purple nutsedge growth model Fixing data into GIS (using GPS) Creating a purple nutsedge spatial distribution map Field history

  22. Taking home massage Integration of the biological and technological means for purple nutsedge detection support and complements each other, and may provide a new dimensions in its precision management. message

  23. Thanks • To my students, Tal N., Tal M., Ran, Roy. • To my colleagues from the department of Weed Research Yosi, Radi, Dani, Tal L., Guy, Evgeni. • To Victor, Yafit and Sagi. • To the Chief Scientist from the Ministry of Agriculture for funding the research.

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