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WEED DETECTION FOR PRECISION WEED MANAGEMENT

WEED DETECTION FOR PRECISION WEED MANAGEMENT. Kefyalew Girma. SOIL/BAE 4213-2002. Why ?. Uneven distribution Density can vary widely within one field Conventional method time-consuming and not proven cost-effective Need for the on-the-go weed detection and treatment. The BOTTOMLINE…….

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WEED DETECTION FOR PRECISION WEED MANAGEMENT

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  1. WEED DETECTION FOR PRECISION WEED MANAGEMENT Kefyalew Girma SOIL/BAE 4213-2002

  2. Why ? • Uneven distribution • Density can vary widely within one field • Conventional method time-consuming and not proven cost-effective • Need for the on-the-go weed detection and treatment

  3. The BOTTOMLINE……. • Site-specific weed control involves the use of correct treatment for the local weed populations which leads to: • reduction in herbicide use on well-kept fields • maximize economic return to the farmer

  4. Where are the weeds? • The weed population must be automatically detected and evaluated across the field • This has led to the research on optical methods for weed detection

  5. The Principle behind automatic weed detection • Map/Sensor-based • Plant species have a different reflection in the visible and near-infrared (NIR) range • These differences can be used for automatic classification of crop and weed.

  6. The Challenge

  7. Absorption of Visible Light by Photo- pigments Sunlight Chlorophyll b Phycocyanin Absorption -Carotene Chlorophyll a 300 400 500 600 700 800 Wavelength, nm Lehninger, Nelson and Cox as presented in SOIL/BAE4213

  8. Success Story • Statistical separability of weeds from soybean with Spectral Vision RDACSH3 hyperspectral sensor solid blue regions indicate separability (Sprague & Bunting, 2001).

  9. Success Stories… • Spectral data resulted in 91, 63, 63, 100, 46, and 33% correct classification of velvetleaf, redroot pigweed, broadleaf signalgrass, cotton, johnsongrass, and corn, respectively • Based upon airborne images populations generally at or above threshold densities could be correctly classified 2/3 of the time (Reynolds and Shaw, 2000)

  10. Success Stories… • In field plant parts can be correctly classified as crop or weed in about 90 % of the cases, based on spectral information (Bennett and Pannell, 1998) • On maize, sugarbeet and 11 common weeds, Up to 94% of the reflection spectra of plants were classified correctly as crop or weed (Feyaerts et al. 1999)

  11. Success Stories… • In wheat and pea , among several species, large patches of wild oat and interrupted windgrass were detected (Lass and Donn Thill, 1998)

  12. Success Story • Pure and Mixed Weed Species Spectral Signatures at 2 meter resolution using AISA (increased no. of bands) • Upper Midwest Aerospace Consortium(UMAC) Upper Midwest Aerospace Consortium(UMAC)

  13. Success Stories “I have to admit I wouldn’t have been convinced to start a weed control program without having the images to show me just how infested those particular pastures are.” Rancher in N. Dakota (UMAC,2002)

  14. Concerns ... • The limited spectral resolution of multi-spectral sensor is often compounded by their typically poor spatial resolution

  15. Concerns... • Canopy Structure similarity • Effect of stress • Lack of powerful algorithms • Investment and benefit

  16. The Way ahead …. Sensor resolution Thresholds Algorithms Unique plant features

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