Forest Auditory Surveillance System
Keywords: Climate Change, Machine Learning, sound data
Abstract: Deforestation is a serious problem worldwide, with an estimated 10 million hectares
of forests lost each year [1]. The loss of forests can have a significant impact on
the environment, including climate change, loss of biodiversity, and soil erosion
[2]. Illegal logging often carried out using chainsaws, is a major contributor to
deforestation in many areas. FASS takes in sound data as input and produces
notifications and analytics as output. The development of FASS involved the
utilization of various tools and technologies like Arduino Nano RP2040 Connect
for the edge device, Angular and TypeScript for the web portal, Tensorflow for
the Machine Learning model, and React Native Expo framework for the Mobile
Application. The system was able to undergo different types of testing to make sure
it works as intended including functional testing, performance testing, integration
testing, and regression testing. In terms of service and maintenance for FASS, a
comprehensive user manual to guide users in effectively utilizing the system will be
provided. The responsibility for maintaining the system rests on the development
team to ensure that the system is operating smoothly and that any issues that may
arise, are addressed. The development of the Forest Auditory Surveillance System
for detecting illegal logging activities but the successful implementation of such a
system requires collaboration among various stakeholders, including the National
Forestry Authority (NFA), researchers, forest management organizations, and local
communities.
Submission Category: Machine learning algorithms
Submission Number: 74
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