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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Detection Of Animal Intrusion In Agricultural Field Using Recurrent Neural Networks. |
| Authors Name: | Indu Katimi Reddy , Meghanath Reddy Addula , Kalyani Gajjala , Mithun Kumar Kalikota , Dr.Godlin Atlas L |
| Unique Id: | IJSDR2404135 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | The escalating global concern over the environmental impact of roads underscores the need for comprehensive wildlife management strategies. Roads contribute to habitat loss, fragmentation, and degradation, posing direct and indirect threats to wildlife, especially larger mammals like the Bengal tiger, Indian elephant, and Giraffe, known for their extensive ranges and seasonal movements. While roads play a crucial role in facilitating human connectivity and globalization, their negative consequences on biodiversity, particularly in modified landscapes with a history of intensive land use, warrant urgent attention. In this context, our project focuses on addressing the challenges posed by elephant intrusions, a pervasive issue leading to crop damage, human casualties, and economic losses. Traditional surveillance methods often fall short, especially during nighttime intrusions, necessitating the development of an advanced system for effective elephant detection, alert generation, and repulsion to safeguard human habitats and agricultural lands. The proposed system serves as a vital tool for wildlife management, specifically targeting areas where human infrastructure intersects with natural habitats. By comparing Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms, our research demonstrates the superiority of RNN in terms of accuracy, offering a more robust solution for the detection and repulsion of elephant intrusions. This project aligns with the broader goal of mitigating human-wildlife conflicts, establishing safer passages for animals across transportation infrastructures, and protecting vital agricultural resources from wildlife intrusion. |
| Keywords: | CNN,RNN,Machine learning,Deep Learning,Animal Intrusion Detection |
| Cite Article: | "Detection Of Animal Intrusion In Agricultural Field Using Recurrent Neural Networks.", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.952 - 960, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404135.pdf |
| Downloads: | 000338175 |
| Publication Details: | Published Paper ID: IJSDR2404135 Registration ID:210899 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 952 - 960 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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