Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover

Fractional green canopy cover (FGCC) is a key diagnostic variable that can be used to estimate canopy development, light interception, and evapotranspiration partitioning. Available image analysis tools for quantifying FGCC are time-consuming or expensive and cannot analyze video. Our objective was to develop a simple, accurate, and rapid tool to analyze FGCC from images and videos. This tool, called Canopeo, was developed using Matlab and is based on color ratios of red to green (R/G) and blue to green (B/G) and an excess green index (2G–R–B). The output from this tool was compared to that from two software packages widely used to analyze FGCC, SamplePoint, and SigmaScan Pro. Canopeo’s image processing speed was 20 to 130 times faster than SigmaScan and 75 to 2500 times faster than SamplePoint. Canopeo correctly classified 90% of pixels when compared to SamplePoint. Root mean squared difference (RMSD) values for Canopeo FGCC vs. FGCC determined by SamplePoint and SigmaScan ranged from 0.04 to 0.12, with an average RMSD of 0.073 across several sets of images of corn (Zea mays L.), forage sorghum [Sorghum bicolor (L.) Moench], bermuda grass [Cynodon dactylon (L.) Pers.], and switchgrass (Panicum virgatum L.). Analysis of video recordings of transects over crop canopies proved to be useful to minimize sampling error and to quantify FGCC spatial variability. This analysis was simple and rapid with Canopeo but not possible with SamplePoint or SigmaScan. The Canopeo app for Matlab and for iOS and Android mobile devices can be downloaded at www.canopeoapp.com.

• Publication year

  2015

• Venue

  Agronomy Journal

• Publication date

  2015-11-01

• Fields of study

  Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.2134/AGRONJ15.0150
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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