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Soil Region Segmentation and Visual Whiteness Analysis in Cold-Region Rice Seedbeds Based on Improved DAC-UNet.

Jiaxin Gao1, Feng Tan2, Fangming Tian2

  • 1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

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|June 12, 2026
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This summary is machine-generated.

This study introduces an advanced AI method for monitoring cold-region rice seedbeds. The technique accurately detects soil whitening, aiding in efficient seedbed management and improved rice cultivation.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Soil Science

Background:

  • Soil whitening in cold-region rice seedbeds indicates drying and moisture variations, crucial for effective seedbed management.
  • Manual scouting and traditional methods are inefficient and limited by illumination and soil complexity.

Purpose of the Study:

  • To develop an objective and efficient method for analyzing soil whitening in rice seedbeds.
  • To improve seedbed management through automated soil surface condition monitoring.

Main Methods:

  • Developed an enhanced DAC-UNet model incorporating deformable convolution, ASPP++ multi-scale feature aggregation, and CBAM attention for semantic segmentation of soil images.
  • Created a binary segmentation dataset using RGB images of rice seedbeds.
  • Formulated a Whiteness Index (WI) for quantitative analysis and spatial visualization of soil whitening.
Keywords:
DAC-UNetWhiteness Indexdeformable convolutionimage processingrice seedbed

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Main Results:

  • The enhanced DAC-UNet model achieved high performance in soil segmentation with 90.63% MIoU, 94.82% mPA, and 97.52% accuracy.
  • The Whiteness Index effectively characterized different soil whitening states.
  • Generated whitening heatmaps provided intuitive visual references for soil surface analysis.

Conclusions:

  • The proposed AI-driven method offers an effective solution for objective soil whitening analysis in cold-region rice seedbeds.
  • This technology enhances the ability to monitor and manage seedbed conditions, potentially improving rice yield.
  • The combination of semantic segmentation and color analysis provides valuable insights for agricultural practices.