CALDS-RTDETR: a robust forestry pest detection model for small targets in complex environments

Key Points

• The detection model successfully identifies small targets with high accuracy, addressing a significant challenge in pest management.
• Key performance metrics indicate over 90% accuracy in detecting pests within complex forest settings.
• Using advanced machine learning algorithms, the model analyzes data from various forestry environments to enhance detection capabilities.
• This approach highlights the importance of robust tools in sustainable forestry practices, enabling better pest control.