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.
翻译:全国植物育种方案的共同要求是伴生栽培 -- -- 种植近距离种植不同种类的植物,以便相互受益。然而,确定伴生植物需要仔细监测植物生长情况。视觉监测技术往往很费力而且容易出错。图像处理技术的可用性可以用来应对植物生长监测的挑战,并提供强有力的解决办法,帮助植物科学家确定伴生植物。本文件提出了一种新的图像处理算法,以确定某一地区植被覆盖面积,称为分层植被覆盖。拟议技术从可信赖的Daubenmire植被覆盖估计和扩大植被的方法中得到灵感。短期而言,设想是从含有多行植物物种在近距离生长的图像中估计植被覆盖情况,由已知规模的多层聚氯乙烯框架加以分离。拟议的算法采用了一种人工变换和简单线性线性分类法,用以估计PVC框架每一部分的植被覆盖面积。在定期摄取图像时进行反复分析,为植物生长和扩展提供了至关重要的洞察力。作为比较手段,用于比较植被生长率的比较,同时比较使用的样本和样本分析,用以比较。比较使用的样本分析范围,用以比较。用于比较。