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A common requirement of plant breeding programs across the country iscompanion planting -- growing different species of plants in close proximity sothey can mutually benefit each other. However, the determination of companionplants requires meticulous monitoring of plant growth. The technique of ocularmonitoring is often laborious and error prone. The availability of imageprocessing techniques can be used to address the challenge of plant growthmonitoring and provide robust solutions that assist plant scientists toidentify companion plants. This paper presents a new image processing algorithmto determine the amount of vegetation cover present in a given area, calledfractional vegetation cover. The proposed technique draws inspiration from thetrusted Daubenmire method for vegetation cover estimation and expands upon it.Briefly, the idea is to estimate vegetation cover from images containingmultiple rows of plant species growing in close proximity separated by amulti-segment PVC frame of known size. The proposed algorithm applies a HoughTransform and Simple Linear Iterative Clustering (SLIC) to estimate the amountof vegetation cover within each segment of the PVC frame. The analysis whenrepeated over images captured at regular intervals of time provides crucialinsights into plant growth. As a means of comparison, the proposed algorithm iscompared with SamplePoint and Canopeo, two trusted applications used forvegetation cover estimation. The comparison shows a 99% similarity with bothSamplePoint and Canopeo demonstrating the accuracy and feasibility of thealgorithm for fractional vegetation cover estimation.

Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering