Vegetation Measurement Along the Line Corridor Using Satellite Imagery

The project is an initiative to monitor trees using machine learning and smart technology. It works by analyzing images of vegetation to detect trees and sends alerts when the need arises. A large dataset of images of vegetation is used to train the system, which takes a powerful deep learning model known for its accuracy in recognizing objects in images, known as ResNet. In this way, it can accurately identify trees by distinguishing them from other plants.

Besides identifying trees, the system employs Internet of Things technology and monitors parameters such as height and exact location with latitude and longitude coordinates. The synergistic treatment of artificial intelligence with real-time monitoring helps the associated system efficiently track and map trees as they grow and change over time.
If the system detects something significant-like a tree that is becoming unsafe with a height that's gone high beyond a certain threshold-it automatically sends an email alert to the appropriate authorities. The alerts describe exact location details so that action can be taken without delay.

By integrating advanced image recognition and smart monitoring together, this system helps in tree tracking, promoting environmental safety and further supporting better forest management.

Measurement techniques, red edge, Vegetation Indices, ResNet Algorithm, Machine Learning, Satellite images, Artificial Intelligence, Line Corridor.

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