DEFORESTATION DETECTION AND CONTROL SYSTEM

Abstract

Global warming will not only be felt many decades from now, it is already happening and its impacts are clearly visible. The multitude of global changes provide clear evidence of the immediate and growing danger that global warming poses to the economy, human health, and the ecosystems upon which humans and other species depend. However, deforestation being one of the major contributors to this menace has attracted the attention of government both in the developing and developed nations for a lasting solution. Different approaches have attracted different limitations including false alarm notification, network degradation due to wide network coverage areas and so on. This research provided a real time technique for deforestation detection and control using machine learning based image detector and flex sensors. Image detector was used to detect intruders into the forest area, while flex sensors were used to monitor tree position changes. The proposed model was implemented using Unity 3D incorporated with C-Sharp programming language as the front end, and MySQL as the backend on a Windows 10 platform. Simulation using Unity 3D was used to test the functionality of the network framework. The results obtained showed that longer flex sensor produces high resistance thereby yielding better sensor sensitivity to tree position change.