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פותח על ידי קלירמאש פתרונות בע"מ -
Can electrostatic fields limit the take-off of tiny whiteflies (Bemisia tabaci)?
Year:
2020
Source of publication :
Animal
Authors :
ברצ'ילון, נתן
;
.
דרוין, שלי
;
.
הלחמי, אילן
;
.
Volume :
14
Co-Authors:

Geffen, O.- Precision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.), Volcani Center, 68 Hamaccabim Road, Rishon Lezion, 7505101, Israel; Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, Be'er Sheva, 8410501, Israel; Animal Science Institute, Agricultural Research Organization (A.R.O.), Volcani Center, 68 Hamaccabim Road, Rishon Lezion, 7505101, Israel.  
Yitzhaky, Y. - Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, Be'er Sheva, 8410501, Israel. 
 
 

Facilitators :
From page:
2628
To page:
2634
(
Total pages:
7
)
Abstract:

Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens' relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the 'gold standard'). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages. 

Note:
Related Files :
Electrostatic
insects
jumping
Pest-control
Take-off
עוד תגיות
תוכן קשור
More details
DOI :
10.1017/S1751731120001676
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
48846
Last updated date:
02/03/2022 17:27
Creation date:
05/08/2020 22:48
Scientific Publication
Can electrostatic fields limit the take-off of tiny whiteflies (Bemisia tabaci)?
14

Geffen, O.- Precision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.), Volcani Center, 68 Hamaccabim Road, Rishon Lezion, 7505101, Israel; Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, Be'er Sheva, 8410501, Israel; Animal Science Institute, Agricultural Research Organization (A.R.O.), Volcani Center, 68 Hamaccabim Road, Rishon Lezion, 7505101, Israel.  
Yitzhaky, Y. - Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, Be'er Sheva, 8410501, Israel. 
 
 

Can electrostatic fields limit the take-off of tiny whiteflies (Bemisia tabaci)?

Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens' relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the 'gold standard'). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages. 

Scientific Publication
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