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Related Experiment Video

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Wheat ear counting using K-means clustering segmentation and convolutional neural network.

Xin Xu1,2, Haiyang Li1, Fei Yin1,2

  • 1Henan Agricultural University, Zhengzhou, 450002 China.

Plant Methods
|August 13, 2020
PubMed
Summary

This study developed an automated wheat ear counting method using K-means clustering and a convolutional neural network (CNN). The approach significantly improves the accuracy and efficiency of wheat yield estimation in field conditions.

Keywords:
CNNCrop yieldDeep learningK-meansRecognitionsSegmentationWheat ear counting

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Wheat yield estimation traditionally relies on manual ear counting, which is labor-intensive and prone to errors.
  • Automating wheat ear counting is crucial for rapid and accurate yield assessment.

Purpose of the Study:

  • To develop and validate an automated system for wheat ear counting using image analysis.
  • To improve the efficiency and accuracy of wheat yield estimation.

Main Methods:

  • K-means clustering was employed for automatic segmentation of wheat ear images captured by hand-held devices.
  • A convolutional neural network (CNN) model was trained on segmented images categorized into non-wheat, one, two, or three wheat ears.
  • The model's performance was evaluated using metrics like R², RMSE, and F1-score.

Main Results:

  • High recognition accuracies were achieved for different ear counts (97.5-99.8%).
  • The model demonstrated strong overall performance with R² of 0.96 and macro/micro F1-scores of 98.47%.
  • Optimal performance was noted during the late grain-filling stage (R²=0.99, RMSE=3.24 ears) and on UAV platforms (R²=0.97, RMSE=9.47 ears).

Conclusions:

  • Image classification of segmented wheat ears, rather than direct target recognition, simplifies the process.
  • This automated method significantly reduces manual annotation workload and enhances counting efficiency and accuracy.
  • The developed system meets the demands for field-based wheat yield estimation.