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Image processing algorithms for a selective vineyard robotic sprayer
Year:
2009
Authors :
Bechar, Avital
;
.
Volume :
Co-Authors:
Berenstein, R., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel, Institute of Agricultural Engineering, Agricultural Research Organization, Israel
Ben Shahar, O., Department of Computer Science, Ben-Gurion University of the Negev, Israel
Shapiro, A., Department of Mechanical Engineering, Ben-Gurion University of the Negev, Israel
Bechar, A., Institute of Agricultural Engineering, Agricultural Research Organization, Israel
Edan, Y., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel
Facilitators :
From page:
749
To page:
757
(
Total pages:
9
)
Abstract:
This paper presents image processing algorithms for a selective robotic sprayer in vineyards. Two types of machine vision algorithms were developed to directly spray grape clusters and foliage. The first algorithm is based on the difference in the distribution of edges between the foliage and the grape clusters. The second detection algorithm uses a decision tree algorithm for separating the grape clusters from the background based on a training dataset from 100 images. Both image processing algorithms were tested on data from movies acquired in vineyards during the growing season of 2008. Results indicate high reliability of both foliage detection and grape clusters detection. Preliminary results show 90% percent accuracy of grape clusters detection, leading to 30% reduction in the use of pesticides.
Note:
Related Files :
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Clustering algorithms
computer vision
Detection algorithm
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Machine learning
precision agriculture
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More details
DOI :
Article number:
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
30933
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:58
Scientific Publication
Image processing algorithms for a selective vineyard robotic sprayer
Berenstein, R., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel, Institute of Agricultural Engineering, Agricultural Research Organization, Israel
Ben Shahar, O., Department of Computer Science, Ben-Gurion University of the Negev, Israel
Shapiro, A., Department of Mechanical Engineering, Ben-Gurion University of the Negev, Israel
Bechar, A., Institute of Agricultural Engineering, Agricultural Research Organization, Israel
Edan, Y., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel
Image processing algorithms for a selective vineyard robotic sprayer
This paper presents image processing algorithms for a selective robotic sprayer in vineyards. Two types of machine vision algorithms were developed to directly spray grape clusters and foliage. The first algorithm is based on the difference in the distribution of edges between the foliage and the grape clusters. The second detection algorithm uses a decision tree algorithm for separating the grape clusters from the background based on a training dataset from 100 images. Both image processing algorithms were tested on data from movies acquired in vineyards during the growing season of 2008. Results indicate high reliability of both foliage detection and grape clusters detection. Preliminary results show 90% percent accuracy of grape clusters detection, leading to 30% reduction in the use of pesticides.
Scientific Publication
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