B04 - Fire Blight - Deteccion Con Multiespectral

7/24/2019 B04 - Fire Blight - Deteccion Con Multiespectral

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Towards Automated Detection of

Stress in Tree Fruit Production

J. Park, H. Ngugi, M. Glenn, J.Kim & B. Lehman


2/19

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 ismonitored 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.
http://images.google.com/hosted/life/f?q=source:life+apollo+11&prev=/images%3Fq%3Dsource:life%2Bapollo%2B11%26hl%3Den&imgurl=e0ce8a55b1600304http://upload.wikimedia.org/wikipedia/commons/7/7c/CIA_seal.jpg


3/19

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?


4/19

Sensor technology for use in

tree fruit production

All sensor-based systems rely on reflected light from

a portion of the electromagnetic spectrum (EMS)


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Reflection(%)

Wavelength (nm)

well watered stressed

Changes in chlorophyll activity

Reflection spectrum of apple leaves

Reduced water content

Visible light Near Infra-red radiation


6/19

Types of sensors being evaluated

in the CASC project

Thermalcameras

NDVI sensors

Hyperspectralcameras

Color cameras


7/19

Detecting fire blight in orchards

Bacterial disease caused by Erwinia amylovor

Often leads to death in young trees


8/19


9/19

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!


10/19

Options for

scouting orchards

for fire blight


11/19

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


12/19

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


13/19

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 developingthe detection algorithms

This is the approach followed in the CASC project


14/19

Sensors evaluated for blight detection

700 nm


15/19

700 nm

Target for early detection:


16/19

Detection of fire blight with

hyperspectral sensor


17/19

Sensors mounted on the APM


18/19

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


19/19

Acknowledgments

Documents

B04 - Fire Blight - Deteccion Con Multiespectral