The platform uses advanced AI to create meaningful visualizations of conservation progress through different tree growth stages:
class AITreeGrowthEngine:
def __init__(self):
self.growth_predictor = TreeGrowthPredictor()
self.environment_simulator = EnvironmentSimulator()
self.impact_analyzer = ConservationImpactAnalyzer()
async def simulate_tree_growth(self, tree_data, environmental_data):
# AI determines optimal growth patterns
growth_pattern = await self.growth_predictor.predict_next_stage(
tree_data,
environmental_data
)
# Simulate environmental conditions
env_conditions = self.environment_simulator.generate_conditions(
growth_pattern
)
# Update tree visualization
return self.update_tree_stage(growth_pattern, env_conditions)
def update_tree_stage(self, growth_pattern, conditions):
stages = {
'sapling': self.generate_sapling_visuals,
'medium': self.generate_medium_tree_visuals,
'mature': self.generate_large_tree_visuals
}
return stages[growth_pattern.stage](conditions)
