Abstract
The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values.
Highlights
Fractional vegetation cover (FVC), which is defined as the vertical projection of foliage onto a horizontal surface, is an important measure of crop development [1]
An advantage that FVC holds over other measures of crop development, such as Leaf Area Index (LAI), is that it can be estimated from the analysis of digital photographs
The objective of this study was to evaluate the performance of a novel, unsupervised, threshold-based, segmentation technique, the Automated Canopy Estimator (ACE) that overcomes many of the aforementioned challenges to produce accurate estimates of fractional vegetation cover for both terrestrial and remotely sensed photographs
Summary
Fractional vegetation cover (FVC), which is defined as the vertical projection of foliage onto a horizontal surface, is an important measure of crop development [1]. FVC can be used as direct input to crop models or as a predictor of crop yield, above-ground biomass and plant nutritional status [2,3,4,5,6,7]. An advantage that FVC holds over other measures of crop development, such as Leaf Area Index (LAI), is that it can be estimated from the analysis of digital photographs. This holds the potential for a simple, low cost, approach to measuring crop development. Applications include vegetation monitoring [8,9], estimation of LAI [10,11,12], plant nutritional status [6,7,13], fractional vegetation cover measurement [1,14,15,16,17], growth characteristics [18], weed detection [19]
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