Komodo Dragon Mlipir Algorithm-based CNN Model for Detection of Illegal Tree Cutting in Smart IoT Forest Area

Abstract

Trees and woods are vital to preventing climate change and protecting our planet. Sadly, they are constantly being destroyed due to human activities like deforestation, fires, etc. This research presents and examines an outline for using audio event categorisation to automatically detect unlawful tree-cutting activity in forests. To monitor large swaths of forest, the research team proposes using ultra-low-power, minor devices incorporating edgecomputing microcontrollers and long-range wireless communication. An efficient and accurate audio classification solution based on multi-layer perceptron (MLP) and modified convolutional neural networks (M-CNN) is projected and tailored for cutting. The Komodo Dragon Mlipir Algorithm (KDMA) is used to pick the best weight for the CNN. Compared to earlier efforts, the suggested system uses a computing technique to recognise deforestation-related hazards. Various preprocessing methods have been evaluated, with special attention paid to the trade-off between classification precision and computer resources, memory, and power use. Additionally, there have been long-range communication trials performed in natural settings. The experimental consequences demonstrate that the suggested method can notice and apprise tree-cutting occurrences through smart IoT for efficient and lucrative forest nursing.