Evaluation of stereovision for extracting plant features

Evaluation of stereovision for extracting plant features

Visual sensors produce an image that can be analysed by computational algorithms to extract useful
information about image features. Plants have complex object structures in which images can help
extract, however, not all plant features can be recognised through a single image. Stereovision
enhances the single image features by adding the third dimension, depth, to obtain more accurate
localisation of the plant’s structures. Stereovision is a technique that produces a disparity map of a
scene through the use of two or more images taken from different points of view. Depth information
can be used to enhance the detection of fruit and plant parts; however research in using Stereovision
for extracting plant structures is sparse.

In this paper, Stereovision is analysed in its ability to extract important features from two types of
nursery plants taken in indoor and outdoor lighting conditions. From the colour images, colour and
shape segmentation are evaluated on their ability to extract certain plant features, such as stems,
branches and leaf. Depth images are also evaluated on their accuracy, coverage, and ability to improve
image segmentation for colour images. The depth images have some gaps and missing data. The new
algorithm develops the depth images by interpolating the gap data and smoothing depth images.
Preliminary results show good plant feature can be extracted from depth images at indoor
environment, while depth data from an outdoor environment contains more noise due to the variation
in lighting conditions.

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