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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM.

Chengquan Zhou1,2, Dong Liang1, Xiaodong Yang2,3

  • 1School of Electronics and Information Engineering, Anhui University, Hefei, China.

Frontiers in Plant Science
|July 31, 2018
PubMed
Summary

This study introduces a computer vision algorithm for accurately counting wheat ears in images, crucial for crop yield prediction. The method achieves high precision and computational efficiency, offering valuable phenotypic data.

Keywords:
multi-feature optimizationsuperpixel theorysupport-vector-machine segmentationwheat ear countingyield estimation

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Image Processing

Background:

  • Accurate wheat ear counts are vital for crop growth prediction and yield estimation.
  • Existing methods for wheat ear counting can be labor-intensive and time-consuming.
  • Developing automated, precise methods for plant phenotyping is an ongoing research focus.

Purpose of the Study:

  • To develop and validate a novel computer vision algorithm for accurate wheat ear recognition and counting in digital images.
  • To improve the efficiency and accuracy of plant phenotyping for agricultural applications.
  • To provide a robust method for extracting wheat ear data from field images.

Main Methods:

  • Utilized red-green-blue images acquired from a ground vehicle, selecting images based on light intensity.
  • Employed Simple Linear Iterative Clustering (SLIC) for superpixel segmentation and patch generation.
  • Extracted features including Color Coherence Vectors, Gray Level Co-Occurrence Matrix, and Edge Histogram Descriptor.
  • Applied feature-weighting fusion using kernel principal component analysis (KPCA).
  • Trained a Twin-Support-Vector-Machine segmentation (TWSVM-Seg) model for pixel classification and image segmentation.
  • Used MATLAB statistical functions for counting segmented wheat ears.

Main Results:

  • The proposed algorithm achieved a precision of 0.79-0.82 when compared to manual field measurements.
  • The method demonstrated computational efficiency with an average running time of 0.1 seconds per image.
  • The TWSVM-Seg model successfully segmented wheat ears from background pixels.
  • The algorithm provided accurate phenotypic data for wheat seedlings.

Conclusions:

  • The developed computer vision algorithm offers an accurate and efficient solution for counting wheat ears.
  • This method can significantly aid in crop growth monitoring and yield prediction.
  • The approach provides a foundation for automated plant phenotyping in agriculture.