Development of Image Processing and AI Model for Drone Based Environmental Monitoring System

Abstract

Data from environmental monitoring can be used to identify possible risks or adjustments to ecological patterns. Early detection reduces risks and lessens the effects on the environment and public health by allowing for prompt responses to ecological imbalances, pollution incidents, and natural disasters. Decision-making and analysis can be done in real time when Artificial Intelligence (AI) is integrated with Unmanned Aerial Vehicles (UAV) technology. With the help of these technologies, environmental monitoring is made possible with a more complete and effective set of tools for assessment, analysis, and reaction to changing environmental conditions. Multiple studies have shown that forest fires in India have been happening more often recently. Lightning, extremely hot weather, and dry conditions are the three main elements that might spontaneously ignite a forest fire. Both natural and man-made ecosystems are affected by forest fires. Forest fire photos are pre-processed using the Sobel and Canny filter. A Convolutional Neural Network (CNN)–based Forest Fire Image Classification Network (DFNet) using the publicly accessible Kaggle dataset is proposed in this study. The suggested DFNet classifier's hyperparameters are fine-tuned with the help of Spotted Hyena Optimizer (SHO). With a performance level of 99.4 percent, the suggested DFNet model outperformed the state-of-the-art models, providing substantial backing for environmental monitoring.