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Implementing a Compression Technique on the Progressive Contextual Excitation Network for Smart Farming Applications.

Setya Widyawan Prakosa1, Jenq-Shiou Leu1, He-Yen Hsieh1

  • 1Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

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Summary
This summary is machine-generated.

This study compresses the Progressive Contextual Excitation Network (PCENet) model for smart farming using filter pruning. The accelerated model achieves faster processing speeds on edge devices while maintaining high accuracy for image classification tasks.

Keywords:
deep learningmodel compressionprogressive contextual excitationpruning filters

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Agricultural Automation

Background:

  • Smart farming increasingly utilizes computer vision for automation.
  • Deep learning models excel in image classification and object detection.
  • The Progressive Contextual Excitation Network (PCENet) shows promise for agricultural image analysis.

Purpose of the Study:

  • To accelerate the PCENet model for edge computing in smart farming.
  • To investigate the effectiveness of pruning filters for model compression.
  • To evaluate the trade-off between speed and accuracy.

Main Methods:

  • Applied filter pruning technique to compress the PCENet model.
  • Evaluated the compressed PCENet model on cocoa bean image classification.
  • Assessed performance on the Jetson Nano edge platform.
  • Tested the compressed model on a corn leaf disease dataset.

Main Results:

  • Achieved a processing speed of 16.7 FPS on Jetson Nano, an improvement from 9.9 FPS.
  • Maintained high accuracy for cocoa bean classification (86.1% compressed vs. 86.8% original).
  • Outperformed ResNet18 (82.7%) in accuracy for cocoa bean classification.
  • Achieved 97.5% accuracy on the corn leaf disease dataset post-compression.

Conclusions:

  • Filter pruning effectively accelerates PCENet for real-time smart farming applications.
  • The compressed PCENet model offers a favorable balance between computational efficiency and classification accuracy.
  • The approach demonstrates potential for deploying advanced deep learning models on resource-constrained edge devices.