A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

Area of Science:

• Agricultural Science
• Remote Sensing
• Computer Vision

Background:

• Unmanned Aerial Vehicle (UAV) technology is crucial for precision agriculture (PA), offering combined terrestrial and aerial remote sensing benefits.
• PA applications like crop health monitoring and weed management rely on vegetation segmentation from field images.
• Traditional methods often use multispectral imagery, but cost reduction necessitates techniques for low-cost RGB cameras.

Purpose of the Study:

• To develop a novel vegetation segmentation methodology specifically for low-cost UAV RGB images.
• To address the challenge of accurate vegetation detection using affordable RGB camera systems in PA.

Main Methods:

• The proposed methodology utilizes the Hue color channel from RGB images.
• It assumes distinct color distributions for vegetation and non-vegetation.
• Five threshold values are determined using hue histograms to differentiate colors, enabling vegetation segmentation.

Main Results:

• The new methodology achieved a mean accuracy of 87.29% for vegetation segmentation.
• The system demonstrated stable performance with a standard deviation of 12.5%.
• Accurate binary images distinguishing vegetation from other objects were generated.

Conclusions:

• The developed method provides an effective and accurate approach for vegetation segmentation using low-cost UAV RGB imagery.
• This technique supports the expansion of UAV applications in precision agriculture by reducing system costs.
• The Hue channel analysis offers a promising solution for automated vegetation detection in agricultural fields.