Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering

TL;DR

This paper introduces a novel image processing algorithm that combines Hough Transform and SLIC clustering to accurately estimate fractional vegetation cover in images, aiding plant growth monitoring in breeding programs.

Contribution

The paper presents a new algorithm that improves vegetation cover estimation accuracy by integrating Hough Lines and Linear Iterative Clustering, expanding on traditional methods.

Findings

Achieves 99% similarity with SamplePoint and Canopeo in vegetation cover estimation.

Effectively monitors plant growth over time through image analysis.

Provides a robust, automated alternative to manual plant monitoring methods.

Abstract

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.