Towards Automated Detection of

Stress in Tree Fruit Production

J. Park, H. Ngugi, M. Glenn, J.

Kim & B. Lehman

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.

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?

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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Ref

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ion

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Wavelength (nm)

well watered stressed

Changes in chlorophyll activity

Reflection spectrum of apple leaves

Reduced water content

Visible light Near Infra-red radiation

Types of sensors being evaluated

in the CASC project

• Thermal cameras

• NDVI sensors

• Hyperspectral cameras

• Color cameras

Detecting fire blight in orchards

Bacterial disease caused by Erwinia amylovor

Often leads to death in young trees

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!

Options for

scouting orchards

for fire blight

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

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

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

Sensors evaluated for blight detection

700 nm

700 nm

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

Detection of fire blight with

hyperspectral sensor

Detection of fire blight with

hyperspectral sensor

Sensors mounted on the APM

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

Acknowledgments