Multiview Stereo Plant Point Cloud Analysis: Leaf Segmentation and Phenotype Feature Extraction

This paper introduces a framework for leaf segmentation and phenotypic feature extraction from multiview stereo plant point clouds. Here's a detailed breakdown of the experimental steps and methods employed:

1. Dataset Preparation:

   • Acquire multiview stereo plant point cloud datasets from the plant domain.
   • Preprocess the datasets, including steps like point cloud denoising and point cloud registration.

2. Leaf Segmentation:

   • Utilize traditional methods, such as the watershed algorithm, for initial leaf segmentation within the point cloud.
   • Leverage point cloud normal vector information to refine and extract leaf regions.

3. Leaf Morphological Feature Extraction:

   • Extract morphological features for each segmented leaf region, such as area, perimeter, and convex hull.
   • Employ morphological methods to calculate shape indices like aspect ratio and circularity for individual leaves.

4. Leaf Texture Feature Extraction:

   • Extract leaf texture features using traditional methods like gray-level co-occurrence matrices (GLCM).
   • Utilize leaf color information to calculate texture features like color histograms and color moments.

5. Leaf Surface Feature Extraction:

   • Calculate leaf surface features using point cloud curvature information, such as height differences and normal differences.

6. Feature Fusion:

   • Fuse the extracted morphological, texture, and surface features to generate a comprehensive leaf phenotypic feature vector.

7. Experimental Evaluation:

   • Evaluate the framework's performance using metrics relevant to leaf segmentation and phenotypic feature extraction.
   • Compare the framework's results with other approaches to demonstrate its effectiveness and superiority.

Overall, the framework first segments the point cloud into individual leaves. It then extracts morphological, texture, and surface features of each leaf and integrates them to obtain a final phenotypic feature vector. Experimental evaluations validate the framework's effectiveness and superiority for leaf segmentation and phenotypic feature extraction from multiview stereo plant point clouds.