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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms.

Naveed Iqbal1, Rafia Mumtaz1, Uferah Shafi1

  • 1National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science (SEECS), Islamabad, Pakistan.

Peerj. Computer Science
|June 18, 2021
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Summary
This summary is machine-generated.

Accurate crop classification in early growth stages is challenging. This study shows that using drone imagery with Machine Learning (ML) and Gray Level Co-occurrence Matrix (GLCM) features significantly improves classification accuracy.

Keywords:
ClassificationFeature extractionGLCMMachine learningRemote sensingTexture analysisUnmanned aerial vehicles

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Remote Sensing

Background:

  • Crop classification in early phenological stages is difficult due to spectral similarities between different crop types.
  • High-resolution imagery from low-altitude platforms like drones offers a potential solution.
  • Machine Learning (ML) techniques can be applied to drone imagery for crop identification.

Purpose of the Study:

  • To classify different crop types at various phenological stages using drone-based optical imagery.
  • To evaluate the effectiveness of Gray Level Co-occurrence Matrix (GLCM) features for crop classification.
  • To compare the performance of different ML algorithms in crop classification using GLCM features.

Main Methods:

  • Acquisition of high-resolution optical images using drones at different crop phenological stages.
  • Extraction of Gray Level Co-occurrence Matrix (GLCM) based features from grayscale drone images.
  • Application and comparison of various Machine Learning algorithms, including Random Forest (RF), Naive Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM), for crop classification.

Main Results:

  • Machine Learning algorithms demonstrated significantly improved performance when applied to GLCM features compared to raw grayscale images.
  • An overall accuracy increase of 13.65% was observed when using GLCM features for crop classification.
  • All tested ML algorithms, particularly Random Forest, showed enhanced classification capabilities with GLCM features.

Conclusions:

  • GLCM features extracted from drone imagery are highly effective for improving crop classification accuracy, especially in early phenological stages.
  • Machine Learning algorithms, when combined with GLCM features, provide a robust approach for differentiating crops with similar spectral characteristics.
  • This methodology offers a valuable tool for precision agriculture and crop monitoring applications.