Özet:

In recent years, the application of machine learning and remote sensing technologies in wildlife conservation has demonstrated tremendous promise. This article provides a comprehensive overview of the advancements in these fields and the impact they have had on various aspects of wildlife conservation. These technologies contribute to more efficient and effective conservation strategies by automating species identification, mapping and monitoring habitats, tracking population dynamics, detecting wildlife crime, and analysing animal vocalisations. This article talks about the development of machine learning algorithms capable of classifying bird and amphibian calls, differentiating fish species, and identifying plant species such as orchids and cacti has made automated species identification possible. Incorporating machine learning algorithms with remote sensing techniquesprovides significant advantages for mapping and monitoring habitats. This article discusses that images captured by camera trapswhen combined with acoustic analysis, can help in monitoring population by enabling automated detection and tracking. These technologies provide efficient and non-invasive approaches to monitor and manage animal populations, from estimating animal density using passive acoustics to identifying and tracking endangered species such as tigers, cheetahs, and sea turtles using camera trap images. The analysis of animal vocalisations using machine learning techniques has revealed information about species behaviour, population dynamics, and habitat quality. Examining the acoustic communication of species such as black rhinoceros and Yangtze finless porpoise has been the focus of research. Acoustic analysis provides a non-invasive method for comprehending the communication patterns of animals and their implications for conservation efforts. By utilising artificial intelligence and remote sensing data, conservationists can make informed decisions and take targeted actions to protect and preserve endangered species and their habitats. These technologies continued development and application hold great promise for the future of wildlife conservation.

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