Design and implementation of a smart forest environment monitoring system

Abstract

Understanding the forestry health and environment—air and water quality, and the density of trees—is the primary objective of our project. From the numerous ways of analyzing the forest, our aim was to find the most effective, but accurate method—the use of a UAV (Unmanned Aerial Vehicle)—to monitor the forest. Given the size, accessibility, and unpredictability of a forest, the UAV has made it possible for us to precisely and efficiently gather the necessary data. Additionally, the health of a forest can be determined by a number of factors—but our project focuses primarily on the three: Air Quality, Water Quality, and the forest's canopy. These factors help us determine the rate of deforestation and the gradual environmental deterioration of a specific forest—over a period of time. In terms of the project's goals, a device was made with sensors for data collection, later the data was analyzed with Standard Data for a comparative idea; and a machine learning model was created to predict the future condition of the forest. Along with this, a camera was used in the device to periodically collect images for processing and calculating the area of the forest. We received fairly accurate test results for our project, and these findings let us comprehend the general state of the forest according to our criteria.