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An improved Deeplab V3+ network based coconut CT image segmentation method.

Qianfan Liu1, Yu Zhang1, Jing Chen2

  • 1School of Computer Science and Technology, Hainan University, Haikou, China.

Frontiers in Plant Science
|December 27, 2023
PubMed
Summary

This study introduces an improved Deeplab V3+ model for segmenting internal coconut organs from CT scans. The enhanced model offers higher accuracy, aiding coconut breeding and research.

Keywords:
CBAMCT imagesDASPPRRMcoconutsemantic segmentation

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Medical Imaging

Background:

  • Coconut cultivation relies on manual experience, hindering internal characteristic observation and breeding optimization.
  • Accurate segmentation of internal coconut organs from CT images is challenging due to data complexity.

Purpose of the Study:

  • To develop an advanced image segmentation model for accurately identifying internal coconut organs.
  • To improve the efficiency and accuracy of coconut breeding programs through better internal characteristic analysis.

Main Methods:

  • An improved Deeplab V3+ architecture was developed, incorporating a dense atrous spatial pyramid pooling module and a Convolutional Block Attention Module (CBAM).
  • A residual refinement module (RRM) was integrated to refine boundary information between organs.
  • Model performance was validated through comparisons and ablation experiments on diverse coconut organ CT images.

Main Results:

  • The proposed model demonstrated significantly higher accuracy in segmenting internal coconut organs compared to existing methods.
  • The dense atrous spatial pyramid pooling module effectively addressed information loss from sparse sampling and captured global features.
  • The CBAM and RRM modules improved the model's ability to capture detailed boundary information.

Conclusions:

  • The enhanced segmentation algorithm provides a robust solution for accurate internal coconut organ segmentation from CT images.
  • This technology facilitates data-driven decision-making for coconut researchers, supporting breeding and growth analysis.
  • The developed model has the potential to advance precision agriculture in coconut cultivation.