# Accurate Segmentation of Vegetation in UAV Desert Imagery Using HSV-GLCM Features and SVM Classification

## TL;DR

This paper introduces a machine learning method to accurately identify vegetation in desert drone images, outperforming existing techniques in challenging conditions.

## Contribution

A novel approach combining HSV color features, GLCM texture analysis, and SVM classification for improved vegetation segmentation in desert environments.

## Key findings

- The proposed method achieved an accuracy of 0.91 and F1-score of 0.88 on desert drone imagery.
- It outperformed traditional spectral index methods and a deep learning baseline in handling shadows and sparse vegetation.
- The method processed images in 25 seconds per image with a training time of 28 minutes.

## Abstract

Segmentation of vegetation from images is an important task in precision agriculture applications, particularly in challenging desert environments where sparse vegetation, varying soil colors, and strong shadows pose significant difficulties. In this paper, we present a machine learning approach to robust green-vegetation segmentation in drone imagery captured over desert farmlands. The proposed method combines HSV color-space representation with Gray-Level Co-occurrence Matrix (GLCM) texture features and employs Support Vector Machine (SVM) as the learning algorithm. To enhance robustness, we incorporate comprehensive preprocessing, including Gaussian filtering, illumination normalization, and bilateral filtering, followed by morphological post-processing to improve segmentation quality. The method is evaluated against both traditional spectral index methods (ExG and CIVE) and a modern deep learning baseline using comprehensive metrics including accuracy, precision, recall, F1-score, and Intersection over Union (IoU). Experimental results on 120 high-resolution drone images from UAE desert farmlands demonstrate that the proposed method achieves superior performance with an accuracy of 0.91, F1-score of 0.88, and IoU of 0.82, showing significant improvement over baseline methods in handling challenging desert conditions, including shadows, varying soil colors, and sparse vegetation patterns. The method provides practical computational performance with a processing time of 25 s per image and a training time of 28 min, making it suitable for agricultural applications where accuracy is prioritized over processing speed.