  A common requirement of plant breeding programs across the country is
companion planting -- growing different species of plants in close proximity so
they can mutually benefit each other. However, the determination of companion
plants requires meticulous monitoring of plant growth. The technique of ocular
monitoring is often laborious and error prone. The availability of image
processing techniques can be used to address the challenge of plant growth
monitoring and provide robust solutions that assist plant scientists to
identify companion plants. This paper presents a new image processing algorithm
to determine the amount of vegetation cover present in a given area, called
fractional vegetation cover. The proposed technique draws inspiration from the
trusted Daubenmire method for vegetation cover estimation and expands upon it.
Briefly, the idea is to estimate vegetation cover from images containing
multiple rows of plant species growing in close proximity separated by a
multi-segment PVC frame of known size. The proposed algorithm applies a Hough
Transform and Simple Linear Iterative Clustering (SLIC) to estimate the amount
of vegetation cover within each segment of the PVC frame. The analysis when
repeated over images captured at regular intervals of time provides crucial
insights into plant growth. As a means of comparison, the proposed algorithm is
compared with SamplePoint and Canopeo, two trusted applications used for
vegetation cover estimation. The comparison shows a 99% similarity with both
SamplePoint and Canopeo demonstrating the accuracy and feasibility of the
algorithm for fractional vegetation cover estimation.
