Verification of color vegetation indices for automated crop imaging applications

An accurate vegetation index is required to identify plant biomass versus soil and residue backgrounds for automated remote sensing and machine vision applications, plant ecological assessments, precision crop management, and weed control. An improved vegetation index, Excess Green minus Excess Red (ExG - ExR) was compared to the commonly used Excess Green (ExG), and the normalized difference (NDI) indices. The latter two indices used an Otsu threshold value to convert the index near-binary to a full-binary image. The indices were tested with digital color image sets of single plants grown and taken in a greenhouse and field images of young soybean plants. Vegetative index accuracies using a separation quality factor algorithm were compared to hand-extracted plant regions of interest. A quality factor of one represented a near perfect binary match of the computer extracted plant target compared to the hand-extracted plant region. The ExG - ExR index had the highest quality factor of 0.88 +/- 10.12 for all three weeks and soil-residue backgrounds for the greenhouse set. The ExG + Otsu and NDI - Otsu indices had similar but lower quality factors of 0.53 +/- 0.39 and 0.54 +/- 0.33 for the same sets, respectively. Field images of young soybeans against bare soil gave quality factors for both ExG - ExR and ExG + Otsu around 0.88 +/- 10.07. The quality factor of NDI + Otsu using the same field images was 0.25 +/- 10.08. The ExG - ExR index has a fixed, built-in zero threshold, so it does not need Otsu or any user selected threshold value. The ExG - ExR index worked especially well for fresh wheat straw backgrounds, where it was generally 55% more accurate than the ExG + Otsu and NDI + Otsu indices. Once a binary plant region of interest is identified with a vegetation index, other advanced image processing operations may be applied, such as identification of plant species for strategic weed control. (C) 2008 Elsevier B.V. All rights reserved.