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Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine.

Yajun Chen¹, Zhangnan Wu¹, Bo Zhao²

¹Department of Information Science, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)

|January 5, 2021

Summary

This study introduces a new method for detecting weeds and corn seedlings using multi-feature fusion and Support Vector Machine (SVM) classification. The optimal combination accurately identifies weeds and crops for precision agriculture applications.

Keywords:

Gabor feature co-occurrence matrix multi-feature precise fertilization precision spraying rotation invariant LBP support vector machine weed and corn seedling detection

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Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Precision agriculture relies on accurate weed and crop detection for efficient herbicide application and fertilization.
Existing methods may lack the accuracy needed for real-time robotic applications in diverse field conditions.

Purpose of the Study:

To develop and validate a robust method for identifying and locating corn seedlings and weeds.
To enhance the capabilities of agricultural robots for precise weeding and fertilization.

Main Methods:

Established an image dataset of corn seedlings and weeds.
Extracted and reduced dimensionality of multiple features (HOG, LBP, Hu moments, Gabor, GLCM, GLCM-G).
Utilized Support Vector Machine (SVM) classification with optimal feature fusion.
Employed K-means clustering for image segmentation and connected component analysis.

Main Results:

The fusion of rotation invariant Local Binary Pattern (LBP) and gray level-gradient co-occurrence matrix features with SVM achieved the highest classification accuracy.
The method accurately detected and located various weed species and corn seedlings in field images.
Successfully provided positional data for herbicide spraying robots and fertilization machines.

Conclusions:

The proposed multi-feature fusion approach significantly improves weed and crop detection accuracy in precision agriculture.
This technique enables more effective automated weed management and targeted fertilization, reducing crop damage and resource waste.