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פותח על ידי קלירמאש פתרונות בע"מ -
Machine vision monitoring of plant health
Year:
1994
Source of publication :
Advances in Space Research
Authors :
חצרוני, אמוץ
;
.
Volume :
14
Co-Authors:
Hetzroni, A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Miles, G.E., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Engel, B.A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Hammer, P.A., Department of Horticulture, Purdue University, West Lafayette, IN 47907-1146, United States
Latin, R.X., Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1146, United States
Facilitators :
From page:
203
To page:
212
(
Total pages:
10
)
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 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. © 1994.
Note:
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עוד תגיות
תוכן קשור
More details
DOI :
10.1016/0273-1177(94)90298-4
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
18942
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:25
Scientific Publication
Machine vision monitoring of plant health
14
Hetzroni, A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Miles, G.E., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Engel, B.A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Hammer, P.A., Department of Horticulture, Purdue University, West Lafayette, IN 47907-1146, United States
Latin, R.X., Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1146, United States
Machine vision monitoring of plant health
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 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. © 1994.
Scientific Publication
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