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Related Experiment Video

Updated: Sep 17, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Corn variety identification based on improved EfficientNet lightweight neural network.

Jinpu Xu1,2, Jinhao Lan3, Guangjie Lv4

  • 1College of Animation and Communication, Qingdao Agricultural University, Qingdao, China.

Frontiers in Plant Science
|July 4, 2025
PubMed
Summary

This study introduces SCD_EFTNet, an AI model for identifying corn ear varieties. This deep learning approach enhances seed authenticity screening, improving crop yields and market value.

Keywords:
CBAMEfficientNetB0classificationcorn eardilated convolutionvariety identification

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Corn seed authenticity is vital for crop yield and economic value.
  • Effective screening of corn ears is crucial for seed processing and intellectual property protection.
  • Current methods for corn ear identification may lack efficiency and accuracy.

Purpose of the Study:

  • To develop an intelligent system for classifying and identifying corn ear varieties using deep learning.
  • To propose an improved EfficientNet lightweight model for enhanced corn ear image analysis.
  • To protect intellectual property rights of corn varieties through accurate identification.

Main Methods:

  • Collected a dataset of 6529 RGB images of corn ears from five varieties.
  • Developed the SCD_EFTNet model by modifying EfficientNetB0, incorporating CBAM attention and dilation convolution.
  • Utilized the Swish activation function to improve gradient transfer stability.

Main Results:

  • The SCD_EFTNet model demonstrated superior performance over mainstream models in Recall, Precision, and mAP.
  • Achieved a mean Average Precision (mAP) of 98.11% in corn ear classification.
  • Showcased significant improvements in inference time compared to existing models.

Conclusions:

  • Phenotypic characteristics of corn ears are effective for classifying and identifying different varieties.
  • The proposed SCD_EFTNet model offers a reliable and efficient solution for intelligent corn ear sorting.
  • This research provides a valuable reference for automated agricultural processes and quality control.