Image assessment tool for plant health and growth tracking with structural similarity and vegetation index
Software supporting the measurements of leaf area using digital photographs requires a frame reference such as a manual ruler scale to derive the surface areas. Two wellknown image software are ImageJ and EasyLeafArea. Each software uses a thresholdbased approach in pixel count, coupled with calibr...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/158596 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | Software supporting the measurements of leaf area using digital photographs requires
a frame reference such as a manual ruler scale to derive the surface areas. Two wellknown image software are ImageJ and EasyLeafArea. Each software uses a thresholdbased approach in pixel count, coupled with calibration to avoid perspective distortion,
before deriving the surface areas. However, both are unable to provide an assessment
of the leaf’s health. Assessing the health of the leaves is done through the observation
of any discolouration. In this study, an assessment tool is created to calculate the
plant’s surface area as well as monitor the health of the leaves. Aruco markers are used
as reference to calculate leaf surface area. Perspective Distortion errors present in the
images are taken into consideration through the usage of a clear and consistent pattern.
Images are processed and transformed from Red, Green, Blue (RGB) to Hue,
Saturation and Value (HSV) colour space. The health of the leaves is monitored by
detecting discolouration using HSV. Finally, the assessment tool can track the growth
of the leaves through contour detection and provide information to users on the
conditions of the plant’s health. |
---|