Please use this identifier to cite or link to this item: https://elibrary.khec.edu.np:8080/handle/123456789/407
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dc.contributor.advisorEr. Sandesh Shrestha-
dc.contributor.authorAnoop Shrestha (740405)-
dc.contributor.authorBobby Maka (740411)-
dc.contributor.authorGaurab Tamakhu (740414)-
dc.contributor.authorSubin Shrestha (740445)-
dc.date.accessioned2022-09-13T11:59:41Z-
dc.date.available2022-09-13T11:59:41Z-
dc.date.issued2022-08-
dc.identifier.urihttps://elibrary.khec.edu.np/handle/123456789/407-
dc.description.abstractOur project is on Animal Intrusion Detection and Alert System. This project is used to protect the farmland from animals by using Raspberry Pi and Deep Learning (YOLO algorithm). It is a big challenge to the farmers to protect the crops from the animals. By this project, farmers get information of intruded animals in specific area. The techniques that are already being used by farmers like electric fence is ineffective. In this report, we are presenting a practical procedure to ward them off, by creating a system which detects the animal and creates the different sound that irritates the animal and also alerts the authorized person by sending a message. The animal can be detected by the model. After the detection, the animal is classified and the restricted animal is repelled by using flash light and irritating sound. This project mainly contributes to protect the crops by repelling the animals.en_US
dc.language.isoenen_US
dc.subjectObject detection, YOLOv3, Convolutional Neural Network, OpenCV, Raspberry Pien_US
dc.titleANIMAL INTRUSION DETECTION AND ALERT SYSTEMen_US
dc.typeReporten_US
local.college.nameKhwopa Engineering College-
local.degree.departmentDepartment of Electronics & communication Engineering-
local.degree.nameBE Electronics & communication Engineering-
local.degree.levelBachelor's Degree-
local.item.accessionnumberD.1260-
Appears in Collections:Electronics & communication Engineering Report

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