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Tomato seedling stem and leaf segmentation method based on an improved ResNet architecture.

Lina Zhang1, Xinying Li1, Zhiyin Yang1

  • 1College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in Plant Science
|September 22, 2025
PubMed
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This study introduces an improved ResNet model for tomato plant phenotyping using 3D point clouds. The new model achieves higher accuracy and faster processing for extracting key plant traits, improving tomato production research.

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Biology

Background:

  • Tomato plant phenotyping is crucial for improving crop production.
  • Traditional deep learning models struggle with point cloud segmentation due to complexity and overfitting.
  • Accurate extraction of phenotypic traits is essential for monitoring plant growth and health.

Purpose of the Study:

  • To develop a lightweight and efficient deep learning model for tomato plant point cloud segmentation.
  • To enhance the accuracy and robustness of phenotypic trait extraction from 3D tomato plant data.
  • To address the limitations of traditional models in processing complex plant structures.

Main Methods:

  • Proposed a novel lightweight model based on the ResNet architecture, optimizing residual blocks with bottleneck modules and downsampling.
Keywords:
bottleneck blockdownsamplinglightweight networkplant phenotypestem and leaf segmentation in point cloud

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  • Integrated specialized convolutional layers using curvature and geometric features for improved segmentation of tomato stems and leaves.
  • Employed adaptive average pooling to enhance model generalization and robustness in point cloud analysis.

    • Main Results:

    • The optimized model achieved 95.11% training accuracy, surpassing ResNet18 by 3.26%.
    • Reduced testing time to 4.02 seconds, a 25% improvement over ResNet18.
    • High correlations (R² values up to 0.945) and low errors (APE < 6%) were observed for extracted phenotypic parameters (plant height, stem diameter, leaf area, leaf inclination angle) compared to manual measurements.

    Conclusions:

    • The proposed X-ResNet model demonstrates superior performance in segmenting tomato plant point clouds and extracting phenotypic traits.
    • 3D point cloud technology, powered by the optimized model, is validated as a feasible approach for accurate tomato phenotyping.
    • This research offers a valuable benchmark for plant phenotyping, with significant implications for agricultural research and practice.