AI in forestry leverages various techniques to analyze and interpret data from diverse sources. For instance, computer vision algorithms are employed to process satellite imagery and identify changes in forest cover, such as deforestation or forest degradation.

Natural language processing (NLP) is used to analyze textual data, such as forest inventory reports or scientific literature, extracting valuable insights. Machine learning models are trained on historical data to predict future trends, such as the likelihood of forest fires or the growth rate of trees.

Additionally, AI-powered drones and sensors collect real-time data on forest health, weather conditions, and soil composition, enabling more accurate monitoring and management.