נגישות
menu      
חיפוש מתקדם
תחביר
חפש...
הספר "אוצר וולקני"
אודות
תנאי שימוש
ניהול
קהילה:
אסיף מאגר המחקר החקלאי
פותח על ידי קלירמאש פתרונות בע"מ -
Detection of green apples in hyperspectral images of apple-tree foliage using machine vision
Year:
2007
Source of publication :
Transactions of the ASABE
Authors :
אוסטרובסקי, ויאצ'סלב
;
.
אלחנתי, ויקטור
;
.
Volume :
50
Co-Authors:
Safren, O., Department of Industrial Engineering, Ben Gurion University, Beer Sheva, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO - Volcani Center, Bet Dagan, Israel, Institute of Agricultural Engineering, ARO - Volcani Center, POB 6, Bet Dagan 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, ARO - Volcani Center, Bet Dagan, Israel
Levi, O., Department of Industrial Engineering, Ben-Gurion University of the Negev, Israel
Facilitators :
From page:
2303
To page:
2313
(
Total pages:
11
)
Abstract:
It is important for orchard owners to be able to estimate the quantity of fruit on the trees at the various growth stages, because a tree that bears too many fruits will yield small fruits. Thus, if growers are interested in controlling the fruit size, knowing in advance that there are too many developing fruits will give them the opportunity to treat the tree. This study proposes a machine vision-based method of automating the yield estimation of apples on trees at different stages of their growth. Since one of the most difficult aspects of apple yield estimation is distinguishing between green varieties of apples or those that are green in the first stages of growth, and the green leaves that surround them, this investigation concentrates on estimating the yield of green varieties of apples. Hyperspectral imaging was used, because it is capable of giving a wealth of information both in the visible and the near-infrared (NIR) regions and thus offers the potential to provide useful results. A multistage algorithm was developed that uses several techniques, such as principle components analysis (PCA) and extraction and classification of homogenous objects (ECHO) for analyzing hyperspectral data, as well as machine vision techniques such as morphological operations, watershed, and blob analysis. The method developed was tested on images taken in a Golden Delicious apple orchard in the Golan Heights, Israel, in two sessions: one during the first stages of growth, and the second just before harvest. The overall correct detection rate was 88.1%, with an overall error rate of 14.1%. © 2007 American Society of Agricultural and Biological Engineers.
Note:
Related Files :
computer vision
Forestry
Fruits
harvesting
Orchards
Plants
trees
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
22754
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:54
Scientific Publication
Detection of green apples in hyperspectral images of apple-tree foliage using machine vision
50
Safren, O., Department of Industrial Engineering, Ben Gurion University, Beer Sheva, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO - Volcani Center, Bet Dagan, Israel, Institute of Agricultural Engineering, ARO - Volcani Center, POB 6, Bet Dagan 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, ARO - Volcani Center, Bet Dagan, Israel
Levi, O., Department of Industrial Engineering, Ben-Gurion University of the Negev, Israel
Detection of green apples in hyperspectral images of apple-tree foliage using machine vision
It is important for orchard owners to be able to estimate the quantity of fruit on the trees at the various growth stages, because a tree that bears too many fruits will yield small fruits. Thus, if growers are interested in controlling the fruit size, knowing in advance that there are too many developing fruits will give them the opportunity to treat the tree. This study proposes a machine vision-based method of automating the yield estimation of apples on trees at different stages of their growth. Since one of the most difficult aspects of apple yield estimation is distinguishing between green varieties of apples or those that are green in the first stages of growth, and the green leaves that surround them, this investigation concentrates on estimating the yield of green varieties of apples. Hyperspectral imaging was used, because it is capable of giving a wealth of information both in the visible and the near-infrared (NIR) regions and thus offers the potential to provide useful results. A multistage algorithm was developed that uses several techniques, such as principle components analysis (PCA) and extraction and classification of homogenous objects (ECHO) for analyzing hyperspectral data, as well as machine vision techniques such as morphological operations, watershed, and blob analysis. The method developed was tested on images taken in a Golden Delicious apple orchard in the Golan Heights, Israel, in two sessions: one during the first stages of growth, and the second just before harvest. The overall correct detection rate was 88.1%, with an overall error rate of 14.1%. © 2007 American Society of Agricultural and Biological Engineers.
Scientific Publication
You may also be interested in