Deforestation Detection Through Deep Learning using Yolo

The amount of forest cover has a direct correlation to the variety of ecosystems. One of the most important places to get a lot of food and nutrition is from plants. As they take up carbon from the air and release it as heat, trees are essentially the world’s largest carbon sinks. However, deforestation has become more common as a result of the world’s fast urbanization and industrialization. The degradation of living conditions is exacerbated by the destruction of forest cover, which has a devastating impact on the environment. Deforestation is outlawed in most nations, but it’s hard to keep tabs on exactly how much forest cover is being lost. Hence, an efficient framework for the identification of deforestation using image processing methodologies is defined in this research study. The suggested method employs decision-making for classification, channel-boosted convolutional neural networks for training, and a dataset of deforestation images for image normalization. Results from the experimental evaluation of the method were positive.