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Lightweight rice leaf spot segmentation model based on improved DeepLabv3.

Jianian Li1, Long Gao1, Xiaocheng Wang1

  • 1Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.

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|September 8, 2025
PubMed
Summary
This summary is machine-generated.

A new lightweight rice leaf spot segmentation model (MMPC-DeepLabv3+) significantly improves disease detection accuracy while reducing computational costs. This advancement enables efficient field deployment for precision agriculture and better rice crop management.

Keywords:
DeepLabV3 + 3feature fusion 5light-weight model 4rice leaf diseases 1segmentation 2

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Rice is a vital food crop, but its susceptibility to diseases necessitates effective monitoring.
  • Existing rice leaf spot segmentation models suffer from high computational overhead, hindering practical field application.
  • Accurate segmentation is crucial for diagnosing diseases like rice blast, brown spot, and bacterial leaf blight.

Purpose of the Study:

  • To develop a lightweight and efficient rice leaf spot segmentation model for field deployment.
  • To improve segmentation accuracy, particularly in transitional regions and at lesion boundaries.
  • To reduce computational complexity and model parameters for enhanced usability in resource-constrained environments.

Main Methods:

  • Developed MMPC-DeepLabv3+, a lightweight model using MobileNetV3_Large (MV3L) as the backbone.
  • Incorporated a multi-scale detail enhancement (MSDE) module with Haar wavelet downsampling for improved boundary and gap segmentation.
  • Utilized a PagFm-Ghostconv Feature Fusion (PGFF) module with coordinate attention (CA) to reduce computational overhead and enhance robustness.
  • Employed a hybrid loss function (Focal Loss + Dice Loss) to address class imbalance in disease imagery.

Main Results:

  • MMPC-DeepLabv3+ achieved 81.23% mean intersection over union (MIoU) and 89.79% mean pixel accuracy (MPA) on natural illumination images.
  • Significantly reduced computational resources: 9.695 G Flops and 3.556 M parameters.
  • Outperformed the baseline DeepLabv3+ by 1.89% in MIoU and 0.83% in MPA, with 93.1% and 91.6% reductions in Flops and Params, respectively.

Conclusions:

  • MMPC-DeepLabv3+ offers a superior balance of accuracy and computational efficiency compared to existing models like DeepLabv3+, U-Net, PSPNet, HRNetV2, and SegFormer.
  • The model's lightweight design and high performance establish a new standard for rice lesion segmentation in precision agriculture.
  • This research facilitates the practical application of AI for disease management in rice cultivation.