Tree-Cutting Detecting System Using Residual Neural Networks

Abstract

Trees are significant in that there are products that originate from them. Wood is the greatest product of trees together with other products like timber and paper. Carbonized wood produces charcoal that is energy used for cooking. Trees, as indicated above, have quite a range of importance and usage to the human beings, hence they are not usable while still intact. Due to this reason, people end up cutting down many trees to meet their needs like timber and charcoal. The problem is finding the culprits who cut down trees for their own selfish needs. This paper discusses a solution to this challenge. This is the Tree-cutting Detection System designed to alert the forest authorities when there is tree-cutting going on in the forest. There is detecting device placed in the forest to listen to whether there are chainsaw sounds or not. When it detects chainsaw sounds, meaning there is tree-cutting going on, the forest authorities are alerted through an alarm and the location of the device that signal them is given so as to access the scene easily and faster. The development methodology adopted in the implementation stage was an agile approach. The solution was the designed and developed through several iteration phases of feedback and improvements of functionalities. Finally, testing of the system done severally during development and after the system completed.