A fast and accurate segmentation model of lychee tree canopy from UAV remote sensing image based on big data and deep learning

Abstract

Abstract Litchi chinensis Sonn, an evergreen tree within the Sapindaceae family and the Litchi Sonn genus, plays a significant role in agricultural management. The canopy information of lychee trees offers a comprehensive and detailed perspective for agricultural experts and orchard managers to monitor and assess the health and growth status of lychee trees in orchards. However, existing segmentation models for lychee tree canopies derived from UAV remote sensing images suffer from low segmentation accuracy and long construction times, making it challenging to meet the demands for rapid feedback and precise decision-making in lychee orchard management. To address this issue, this paper proposes a fast and accurate segmentation model for lychee tree canopies from UAV remote sensing images based on big data and deep learning technologies. Specifically, in our proposed method, first, the Res34CA_UNet segmentation model architecture was designed to improve the segmentation accuracy rate, where residual network technology was adopted to improve the ability to extract input data features, while coordinate attention mechanisms was used to enhance the perception ability of structural diversity for our model; Second, a parallel Res34CA_UNet segmentation model based on Hadoop and Spark was developed to accelerate the construction process based on the improved Res34CA_UNet segmentation model for lychee tree canopy segmentation respectively. Experimental results demonstrate that our proposed segmentation model, which integrates improved residual networks and attention mechanisms, achieves a 3.88% increase in segmentation accuracy. Furthermore, our proposed Hadoop and Spark-based parallel Res34CA_UNet segmentation models reduce 86.9% and 88.2% construction times respectively. These improvements significantly enhance the overall segmentation efficiency for lychee tree canopies from UAV remote sensing images, providing a robust solution for fast and accurate orchard management.

Keywords

• Lychee tree canopy segmentation
• Big data
• Deep learning
• Residual network
• CA attention mechanism