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A vision-based bee counting algorith...

Reka, Sai Kiran,

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A vision-based bee counting algorithm for electronic monitoring of Langsthroth beehives /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A vision-based bee counting algorithm for electronic monitoring of Langsthroth beehives // Sai Kiran Reka.
作者:	Reka, Sai Kiran,
面頁冊數:	1 electronic resource (46 pages)
附註:	Source: Masters Abstracts International, Volume: 78-02.
Contained By:	Masters Abstracts International78-02.
標題:	Entomology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10125500
ISBN:	9781339835730

A vision-based bee counting algorithm for electronic monitoring of Langsthroth beehives /
Reka, Sai Kiran,

A vision-based bee counting algorithm for electronic monitoring of Langsthroth beehives /Sai Kiran Reka. - 1 electronic resource (46 pages)

Source: Masters Abstracts International, Volume: 78-02.

An algorithm is presented to count bee numbers in images of Langsthroth hive entrances. The algorithm computes approximate bee counts by adjusting the brightness of the image, cropping a white or green area in the image, removing the background and noise from the cropped area, finding the total number of bee pixels, and dividing that number by the average number of pixels in a single bee. On 1005 images with green landing pads, the algorithm achieved an accuracy of 80 percent when compared to the human bee counting. On 776 images with white landing pads, the algorithm achieved an accuracy of 85% compared to the human bee counting.

English

ISBN: 9781339835730Subjects--Topical Terms:

615844
Entomology.
Subjects--Index Terms:

Beehive

A vision-based bee counting algorithm for electronic monitoring of Langsthroth beehives /
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520 $a An algorithm is presented to count bee numbers in images of Langsthroth hive entrances. The algorithm computes approximate bee counts by adjusting the brightness of the image, cropping a white or green area in the image, removing the background and noise from the cropped area, finding the total number of bee pixels, and dividing that number by the average number of pixels in a single bee. On 1005 images with green landing pads, the algorithm achieved an accuracy of 80 percent when compared to the human bee counting. On 776 images with white landing pads, the algorithm achieved an accuracy of 85% compared to the human bee counting.
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590 $a School code: 0241
650 4 $a Entomology. $3 615844
650 4 $a Computer science. $3 523869
650 4 $a Behavioral sciences. $3 529833
653 $a Beehive
653 $a Electronic mointoring
653 $a Forager traffic
653 $a OpenCV
653 $a Raspberry pi
653 $a Vision based detection
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710 2 $a Utah State University. $b Computer Science. $e degree granting institution. $3 3821730
720 1 $a Kulyukin, Vladimir $e degree supervisor.
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