This was presented in the summer 2009 at Penn State's field day. It is an update on our work in developing tools to automatically detect plant stress in tree fruit.

1. Towards Automated Detection of
Stress in Tree Fruit Production
J. Park, H. Ngugi, M. Glenn, J.
Kim & B. Lehman

2. The CIA monitors world-wide,
agricultural production with
satellite-based, remote sensing.
During the Cold War, the U.S.
used this information in the sale
of wheat to Russia
World food production is
monitored to anticipate
governmental instability as well
as markets.
From a global scale to a farm
scale, this technology can be
used to improve grower
productivity.

3. Potential applications of monitoring
technology in tree fruit production
• Detection of tree stress
– Moisture stress (drought or excess water)
– Nutrient stress
– Disease and insect stress
• Estimation of expected yield
• Any other use?

4. Sensor technology for use in
tree fruit production
All sensor-based systems rely on reflected light from
a portion of the electromagnetic spectrum (EMS)

5. Reflection spectrum of apple leaves
100
90 well watered stressed
80
70
Reduced water content
Reflection (%)
60
50
40 Visible light Near Infra-red radiation
30
20 Changes in chlorophyll activity
10
0
565
400
424
447
471
494
518
541
588
612
635
659
682
706
729
753
776
800
823
847
870
894
917
941
964
988
Wavelength (nm)

6. Types of sensors being evaluated
in the CASC project
• Thermal
cameras
• NDVI sensors
• Hyperspectral
cameras
• Color cameras

7. Detecting fire blight in orchards
Bacterial disease caused by Erwinia amylovor
Often leads to death in young trees

9. Factors determining successful
fire blight management
• Once infection occurs, successful
management depends on:
– Early detection
– Application of appropriate control measures
such as cutting out infected shoots
– Continued monitoring
All the factors point to the need for
regular scouting!

10. Options for
scouting orchards
for fire blight

11. Current CASC Project Research
• Identification and evaluation of suitable
sensors for automated detection
• Preliminary detection experiments
– Can we detect fire blight with sensors?
– How early can we detect lesions?
• Development of detection algorithms

12. Potential rapid detection systems
for fire blight
• Biological-based detection systems
– Molecular-based techniques
– Can be quite rapid
• Main challenge is sampling (very large numbers
of samples)
– How many shoots (all a potential infection sites)
– Destructive sampling
– Would be very labor-intensive with current technology
– Currently restricted to confirming pathogen identity

13. Potential rapid detection systems
for fire blight cont.
• Sensor-based detection systems
– Rely on sensors to detect plant response to infection
– No destructive sampling or sample preparation
– Can be as rapid as real-time
– Can cover a large area over a short time
• Main challenge: the right sensors and developing
the detection algorithms
• This is the approach followed in the CASC project

14. Sensors evaluated for blight detection
700 nm

15. Detection of fire blight with
hyperspectral sensor
Target for early detection:
<10 cm of diseased tissue
(~7 days after infection)
Inoculated plants in the
green house at: 14, 10, 7, 4
and 2 d before image
acquisition
Hyperspectral images 300
to 1100 m
700 nm

16. Detection of fire blight with
hyperspectral sensor

17. Sensors mounted on the APM

18. What we hope to accomplish
• Detection of diseased shoots within 7
days after infection for fire blight
– No more than 1-3 leaves have visible
symptoms for virulent strains
– Over 85% accuracy rate
• Detection of other types of stress
• Develop a database that to help identify
causes of tree stress

19. Acknowledgments