A 3D point cloud instance segmentation method for strawberry based on SGC

Abstract

With effective protective covering and microclimate control, greenhouse crops offer significant advantages, such as high yield and quality, remaining unaffected by seasonal variations and meeting the demand for diverse agricultural products. Leaf area is a critical growth parameter influencing the indoor microclimate and the transport of nutrients within plants. This study introduces a strawberry 3D point cloud instance segmentation method based on SGC to address the challenge of stem and leaf instance segmentation in calculating plant leaf area using 3D point cloud data. High-quality point cloud data were obtained using a 3D scanner, and feature enhancement was achieved through the Leaf Vein and Boundary Preserving Sampling (LVBPS) method. The SGC network achieved an average precision of 90.41% (AP25) and 89.47% (AP50) for instance segmentation, with the precision of leaf segmentation reaching 93.63% (AP25) and 92.80% (AP50). These findings provide valuable technical support and references for greenhouse cultivation and smart agriculture applications. The source code and dataset can be accessed at https://github.com/suyangsuluo/SGC.

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