AbstractTechniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are inductive of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition.


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    Titel :

    Machine vision monitoring of plant health


    Beteiligte:
    Hetzroni, A. (Autor:in) / Miles, G.E. (Autor:in) / Engel, B.A. (Autor:in) / Hammer, P.A. (Autor:in) / Latin, R.X. (Autor:in)

    Erschienen in:

    Advances in Space Research ; 14 , 11 ; 203-212


    Erscheinungsdatum :

    1994-01-01


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Machine Vision Monitoring of Plant Health

    Hetzroni, A. / Miles, G. E. / Engel, B. A. et al. | British Library Conference Proceedings | 1994


    Machine Vision Monitoring of Plant Health

    Hetzroni, A. | Online Contents | 1994



    Monitoring of Plant Development in Controlled Environment with Machine Vision

    Ling, P. P. / Giacomelli, G. A. / Russell, T. et al. | British Library Conference Proceedings | 1996


    Machine Health Monitoring

    Bresser, Paul / Griffiths, Ray | SAE Technical Papers | 1993