Abstract
Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%.
Highlights
The agriculture industry is a major source of livelihood in the whole world
These thresholds can be used to generate the vegetation binary images as the main output for the vegetation segmentation process, so it is important to generate ground truth vegetation binary images to be used for accuracy evaluation of the binarization and threshold detection
The proposed paper introduced a new vegetation segmentation approach which aims to generate vegetation binary images from RGB images acquired by a low-cost unmanned aerial vehicle (UAV) imagery system
Summary
The agriculture industry is a major source of livelihood in the whole world. Its importance has been enhanced by its role to supply food, clothing, medicine, and employment opportunities to all human beings. Agriculture has an important ability to enhance the environment condition through providing different biological products [1]. Due to such importance and roles, it was vital to transform the agriculture industry into a knowledge-based industry to enhance the benefits of its outputs. A major step is to apply a management system to the agriculture industry which can achieve an economically and an environmentally development for the agriculture process. It is important to avoid the side effects of using the different agricultural inputs such
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