International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Endangered Species Conservation Rishita Shekapure, Shreya Sawant, Shrutika Kadam, Sakshi Chaudhari Prof. S.N Chaudhari Computer Engineering Department, Datta Meghe College of Engineering, Navi Mumbai ,Maharashtra , India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - In recent years, the biodiversity of the planet has In recent years, there has been a growing interest in using been disappearing at an unprecedented rate. Several species are close to going extinct, thus it's important to protect the populations that still exist. It is possible to consistently observe animals in their natural settings and determine the effect of temperature in animal conservation. The use of automatic hidden cameras for wildlife monitoring has increased dramatically in the modern world due to its efficiency and dependability in capturing data on animals in large volumes, more effectively, and without operator interference. Yet, it can be time-consuming and exhausting to manually analyze and extract information from such enormous datasets obtained by camera traps. challenging procedure. For ecologists and biologists who want to watch wildlife in its native habitat, and make conclusions about the future state of animals. This presents a substantial obstacle. We have reviewed all recent works on deep learning-based animal detection and identification in the field. By examining numerous publications, authors have discovered a method of compiling all the data on all the endangered species and how temperature affects them, and then making predictions to determine the count of animals with change in temperature over future years.
machine learning algorithms to monitor and protect endangered species. Machine learning is a subfield of artificial intelligence that allows computers to learn from data and make predictions or decisions without being explicitly programmed.
Keywords: Automatic hidden cameras , Natural settings,
In this analysis of endangered species using machine learning, we will explore the potential of this technology to improve our understanding of these species and their habitats. We will look at case studies of successful machine learning applications, examine the challenges and limitations of this approach, and discuss future directions for research and conservation efforts.
Machine learning algorithms can help overcome some of the challenges of monitoring endangered species. Machine learning algorithms can analyze large amounts of data from various sources, including satellite imagery, acoustic recordings, and camera traps, to identify, track and monitor endangered species. Machine learning algorithms can also be trained to recognize patterns and anomalies in data, which can be useful for detecting changes in species populations or habitats. Also the importance of Temperature change in Animal disappearance cannot be ignored. By building this project we are also emphasizing the effect of temperature change on animal counts in the future years. Using the CNN approach we are also providing the much needed User interface for recognition and classification of these animals.
Temperature, Future count.
1.INTRODUCTION As our planet continues to face the challenges of climate change, habitat loss, and human activity, many animal species are struggling to survive. Endangered species are those that are at risk of extinction, and their conservation is a critical priority for scientists, policymakers, and conservationists around the world. Machine learning is a powerful toolthat can help us better understand the threats facing endangered species and develop effective strategies for their protection. By analyzing large datasets of species populations, habitat characteristics, and environmental variables, machine learning algorithms can identify patterns and relationships that may not be apparent to human analysts.
Ultimately, we hope to demonstrate how machine learning can be a valuable tool in the fight to protect endangered species and preserve biodiversity for future generations.
2. LITERATURE SURVEY Michela Pacifici et al.[1] proposed a paper where summarization of different currencies used for assessing species’ climate change vulnerability is done. They describe three main approaches used to derive these currencies (correlative, mechanistic and trait- based), and their associated data requirements, spatial and temporal scales of application and modelling methods. Identified strengths and weaknesses of the approaches and highlight the sources of uncertainty inherent in each method that limit projection reliability. Also provided guidance for conservation practitioners in selecting the most
However, monitoring and protecting endangered species can be a daunting task, especially for species that are rare, elusive, or inhabit remote areas. Traditional methods of monitoring, such as field surveys, can be time-consuming, expensive, and may not provide accurate or real-time data.
© 2023, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 857