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 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. Scouting … Too coarse resolution of multispectral sensors

4. 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 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

5. 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 The weed population must be automatically detected and evaluated across the field This has led to the research on optical methods for weed detection

6. 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. 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.

7. The Challenge

8. Sunlight Chlorophyll b  -Carotene Phycocyanin Chlorophyll a 300400 500600 700 800 Wavelength, nm Absorption Lehninger, Nelson and Cox as presented in SOIL/BAE4213 Absorption of Visible Light by Photo- pigments

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

10. 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) 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)

11. 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) 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)

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

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

14. 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)

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

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

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