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Sunflower seeds classification based on sparse convolutional neural networks in multi-objective scene.

Xiaowei Jin1, Yuhong Zhao2, Hao Wu3

  • 1School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.

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This study proposes a sparse convolutional neural network for sunflower seed classification, reducing computational costs. The Iterative Magnitude Pruning algorithm effectively compressed the model with minimal accuracy loss, making it cost-effective for production.

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Machine learning

Background:

  • Traditional sunflower seed classification methods are costly.
  • Neural network methods have high computational demands.

Purpose of the Study:

  • To develop a cost-effective sunflower seed classification method.
  • To address high equipment and computational costs in seed classification.

Main Methods:

  • Utilized YOLOv5 for object detection and ResNet for classification.
  • Applied Iterative Magnitude Pruning (IMP) based on the Lottery Ticket Hypothesis for model compression.
  • Compared global and layer-wise pruning effects on model performance.

Main Results:

  • Achieved a 92% reduction in parameters using global pruning with IMP.
  • The pruned model showed a 0.56% accuracy improvement over the non-pruned model.
  • Global pruning minimized performance loss compared to layer-wise pruning.

Conclusions:

  • IMP effectively creates a lightweight sunflower seed classification model with minimal performance degradation.
  • This approach offers a cost-effective alternative for practical sunflower seed classification in production.
  • Reduced computational resources enhance the applicability of machine vision in agriculture.