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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Animal Detection In Farms Using OpenCV |
| Authors Name: | S. Raji , M.Mubashira Nazneen , T.Sai Poojitha , Y.Lavanya |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217317
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| Published Paper Id: | IJNRD2404160 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | ABSTRACT TITLE: ANIMAL DETECTION IN FARMS USING OPENCV Agriculture is the most important sector of Indian Economy but the issue of damage to crops by wild animals has turned into an important social issue in current occasions. So far, many of the farmers reply on guards to guard their crops which increases the overhead costs. But, due to current climate conditions, crop failure rate has increased dramatically. Debt in agricultural sector has increased tremendously. In these situations, a farmer cannot expect further destruction of crops and neither can afford increase costs in farming. Thus, there is need for a system to detect any intrusion which can help the farmers to drive away these animals as soon as they learn about their intrusion. Computer vision is applicable to many fields like medical field, robotics, remote sensing, machine vision, content-based image retrieval. Computer vision also applied in the security field to perform automatic surveillance and access control and attendance management. In agriculture fields near to forest areas have a severe threat from wild animals, which attacks regularly on farms. These attacks causing huge damage to agricultural crops subsequently causes significant financial losses to farmers. Some measures are taken by the farmers by installing electrical fences to the farms, big flood lights in the farm. Some even resort to hiring guards. Installing an electrical fence is much costlier to equip huge farms and kills so many animals, which is even illegal in certain places and affects the biodiversity. Other existing techniques also are not effective due to several reasons, cost being one of them. In this project, we proposed a new and cost effective solution for agriculture security from animals. It is a proactive solution which gives alerts to the farmers when animals come near to the farms. It also causes certain siren to be played whenever any animals are detected and is directed towards the animal in an attempt to scare them away. Here, we are implementing a solution that recognizes animals when it is captured on camera. |
| Keywords: | OpenCV, Machine Learning, Artificial Intelligence, Single Shot Detector(SSD) |
| Cite Article: | "Animal Detection In Farms Using OpenCV", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b462-b466, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404160.pdf |
| Downloads: | 00051 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404160 Registration ID: 217317 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b462-b466 Country: Anantapur, Andhra Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404160 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404160 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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