國立虎尾科技大學 |

A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic./
作者:	Tiwari, Astha.
面頁冊數:	1 online resource (47 pages)
附註:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355929676

A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.
Tiwari, Astha.

A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic. - 1 online resource (47 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Utah State University, 2018.

Includes bibliographical references

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified. This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further help in monitoring forager traffic. Bee traffic is the number of bees moving in a given area in front of the hive over a given period of time. And, forager traffic is the number of bees entering and/or exiting the hive over a given period of time. Forager traffic is an important variable to monitor food availability, food demand, colony age structure, impact of pesticides, etc. on bee hives. This will lead to improved remote monitoring and general hive status and improved real time detection of the impact of pests, diseases, pesticide exposure and other hive management problems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355929676Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

A Deep Learning Approach to Recognizing Bees in Video Analysis of Bee Traffic.
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500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Vladimir Kulyukin.
502 $a Thesis (M.S.)--Utah State University, 2018.
504 $a Includes bibliographical references
520 $a Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified. This work uses Artificial Intelligence and Computer Vision approaches to develop and analyze techniques to help in continuous monitoring of bee traffic which will further help in monitoring forager traffic. Bee traffic is the number of bees moving in a given area in front of the hive over a given period of time. And, forager traffic is the number of bees entering and/or exiting the hive over a given period of time. Forager traffic is an important variable to monitor food availability, food demand, colony age structure, impact of pesticides, etc. on bee hives. This will lead to improved remote monitoring and general hive status and improved real time detection of the impact of pests, diseases, pesticide exposure and other hive management problems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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