1. Remote sensing data for detection of Rhizoctonia solani in sugar beets
Tavvs M. Alves
Ph.D. Student
Entomology

2. Fungus attacking sugar beet
Rhizoctonia solani
Fungus attacking sugar beet
Crown rot
Root rot

3. Remote sensing for detection of hot spots Prevent economic losses
Rhizoctonia solani
Patchy distributed
Remote sensing for detection of hot spots
Prevent economic losses

4. Sampling sites were arbitrarily selected Healthy Diseased
Material and Methods
Sampling sites were arbitrarily selected
Healthy
Diseased
Two sugar beet fields Crookston, MN, 2010

5. Material and Methods 20 random plants

6. Flew 3,000 feet above the ground Spatial resolution of 0.5 m
Material and Methods
NIR nm
Green nm
Red nm
Flew 3,000 feet above the ground
Spatial resolution of 0.5 m

7. Material and Methods Subsets of fields
Georectification using Ground Control Points
Model: polynomial of first order
Resampling: nearest-neighbor method

8. Hybrid classification
Material and Methods
Hybrid classification
Unsupervised
Creating 4 classes
Supervised
Guide training samples
Maximum Likelihood to group the pixels

9. Results and Discussion
Spearman’s correlation
AOKI = ρG/ρNIR
Greenness
Leaf chlorophyll concentration

10. Results and Discussion
T-test
Healthy
Diseased
p<0.05
Disease did not affect reflectance in Green
(t=1.26, p=0.24)
15% less NIR
16% more RED

11. Results and Discussion
Classes matched to the map with the rates
Intermediate-intensity class
Border effect

12. Conclusions Reduce the waste of fungicides
Aerial images of sub-meter resolution can be used to detect the symptoms of Rhizoctonia solani in sugar beet fields
POTENTIALS:
Reduce the waste of fungicides
Accurate timing and location
Minimizing the chances of economic losses

13. Univesity of Minnesota Department of Entomology
Thank you.
Tavvs M. Alves