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Towards Automated Detection of Stress in Tree Fruit Production
 

Towards Automated Detection of Stress in Tree Fruit Production

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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.

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.

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    Towards Automated Detection of Stress in Tree Fruit Production Towards Automated Detection of Stress in Tree Fruit Production Presentation Transcript

    • 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)
    • 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)
    • 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
    • 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
    • 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