Satellite Based Forest stand Detection using Artificial Intelligence

The prototype is developed using a Raspberry Pi configured with a memory card containing the operating system, Python environment, and the YOLOv8 deep learning framework. A predefined dataset of satellite or drone images representing forested areas, individual trees, and empty fields is used to train the YOLOv8 model. The trained model is deployed on the Raspberry Pi to perform image-based classification. A USB web camera captures input images, which are processed by the AI model to detect forest stands. Detection results and system status are displayed on an LCD module for real-time visualization. The GPS module continuously tracks the geographical location of the detected areas, while the GSM module sends location-tagged detection data and alert messages to remote users. The entire system is powered using a 12V adapter with regulated power supply and connectors ensuring stable operation. Students can use a standard 5V Type-C adapter for Raspberry Pi power. This prototype demonstrates a compact, AI-enabled forest monitoring system suitable for environmental surveillance and land-use assessment applications.