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Apple yield mapping using hyperspectral machine vision
Year:
2007
Authors :
Alchanatis, Victor
;
.
Ostrovsky, Viacheslav
;
.
Volume :
Co-Authors:
Alchanatis, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel
Safren, O., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel, Dept. of Industrial Engineering, Ben-Gurion University of Negev, Israel
Levi, O., Dept. of Industrial Engineering, Ben-Gurion University of Negev, Israel
Ostrovsky, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel
Facilitators :
From page:
555
To page:
562
(
Total pages:
8
)
Abstract:
For orchard growers, it is important to estimate the quantity of fruit on the trees at different stages of their growth. This study proposes a method of automatically detecting apples in digital images that can be used for automating the yield estimation of apples on trees at different stages of their growth by means of machine vision. This investigation concentrates on estimating yield of green varieties of apples. To achieve this goal, hyperspectral imaging was applied. A multistage algorithm was developed which utilizes PCA and ECHO as well as machine vision techniques. The overall correct detection rate was 87.0% with an overall error rate of 14.9%.
Note:
Related Files :
Apples
computer vision
Estimation
Forestry
Fruits
Machine vision
spectroscopy
Vision technique
Yield mapping
Show More
Related Content
More details
DOI :
Article number:
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
30132
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:52
Scientific Publication
Apple yield mapping using hyperspectral machine vision
Alchanatis, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel
Safren, O., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel, Dept. of Industrial Engineering, Ben-Gurion University of Negev, Israel
Levi, O., Dept. of Industrial Engineering, Ben-Gurion University of Negev, Israel
Ostrovsky, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet-Dagan, Israel
Apple yield mapping using hyperspectral machine vision
For orchard growers, it is important to estimate the quantity of fruit on the trees at different stages of their growth. This study proposes a method of automatically detecting apples in digital images that can be used for automating the yield estimation of apples on trees at different stages of their growth by means of machine vision. This investigation concentrates on estimating yield of green varieties of apples. To achieve this goal, hyperspectral imaging was applied. A multistage algorithm was developed which utilizes PCA and ECHO as well as machine vision techniques. The overall correct detection rate was 87.0% with an overall error rate of 14.9%.
Scientific Publication
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