Literature DB >> 11540182

Machine vision monitoring of plant health.

A Hetzroni1, G E Miles, B A Engel, P A Hammer, R X Latin.   

Abstract

Techniques 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 indicative 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.

Entities:  

Keywords:  NASA Discipline Life Support Systems; Non-NASA Center

Mesh:

Substances:

Year:  1994        PMID: 11540182     DOI: 10.1016/0273-1177(94)90298-4

Source DB:  PubMed          Journal:  Adv Space Res        ISSN: 0273-1177            Impact factor:   2.152


  3 in total

Review 1.  Signature Optical Cues: Emerging Technologies for Monitoring Plant Health.

Authors:  Oi Wah Liew; Pek Ching Jenny Chong; Bingqing Li; Anand K Asundi
Journal:  Sensors (Basel)       Date:  2008-05-16       Impact factor: 3.576

Review 2.  Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Authors:  Jayme Garcia Arnal Barbedo
Journal:  Springerplus       Date:  2013-12-07

3.  Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis.

Authors:  Bo Li; Michelle T Hulin; Philip Brain; John W Mansfield; Robert W Jackson; Richard J Harrison
Journal:  Plant Methods       Date:  2015-12-24       Impact factor: 4.993

  3 in total

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