Using Vegetation Indices to Determine Peanut Maturity

This research project was conducted by Maria Seidl, a M.S. student at the Technische Universität München in Germany during her internship at the University of Georgia between July and December, 2010.


The goal of this project was to evaluate the potential of using vegetation indices (Vis) to determine peanut maturity.  VIs are mathematical ratios of the amount of light reflected by plant canopies at specific wavebands (green, red, NIR, etc.)  Reflectance is a measure of the percentage of sunlight reflected by plants at those wavelengths and can be measured with tractor-mounted sensors like the Crop Circle®.

We conducted the 2010 study at the University of Georgia’s Gibbs Farm on 4 peanut varieties.  The peanuts were planted in a replicated random block design intended for a planting date/peanut maturity experiment being conducted by Dr. John Beasley and Dr. Wilson Faircloth (see Figure 1).  Seven different planting dates were used beginning with April 20 and ending on June 01.

Figure 1. The design of the planting date/peanut maturity experiment with four peanut cultivars and seven planting dates used for this study.

Figure 1. The design of the planting date/peanut maturity experiment with four peanut cultivars and seven planting dates used for this study.

We monitored the reflectance response of all 4 varieties weekly from August 17 until the peanuts were inverted prior to harvest.  This resulted in several data sets for each planting date.  Peanut samples were collected and maturity measured using the hull-scrape method immediately following the collection of each reflectance data set.

The reflectance data were used to calculate six different VIs with potential for predicting peanut maturity.  The response of the VIs over time was graphed with the goal of identifying a pattern which indicates maturity.  For example, Figure 2 shows the results of one index for all four varieties and all seven planting dates.  The varieties generally produced similar curves although the magnitudes were different.  The magnitude of the vegetation index is likely to change from year to year so we are interested in patterns within the response curve rather than the magnitude of the measured VI at any particular time in the season.  The change in slope described in the caption for Figure 2 is a pattern also observed in 2009.  If the pattern is observed for a third consecutive year during the 2011 growing season, than we can have confidence that this may be a valuable technique for predicting peanut maturity.

Figure 2. Ratio vegetation index (RVI) response to peanut maturity. Each grouping of lines represents the response of that variety across seven planting dates. The big dip in RVI observed on 09/07/2010 is a result of drought stress and not related to maturity. Of interest for maturity measurement is the change in slope at the right end of each response curve. This change in slope has been observed for a second year although the response this year was more muted than 2009.

Figure 2. Ratio vegetation index (RVI) response to peanut maturity. Each grouping of lines represents the response of that variety across seven planting dates. The big dip in RVI observed on 09/07/2010 is a result of drought stress and not related to maturity. Of interest for maturity measurement is the change in slope at the right end of each response curve. This change in slope has been observed for a second year although the response this year was more muted than 2009.

Conclusions and Future Work:
Several indices appear to have great potential for serving as indicators of peanut maturity for many varieties. We would like to repeat our experiment during 2011.  Data from a third year will allow us to determine with confidence whether VIs can be used to predict peanut maturity.  We feel that this approach can be a very useful, reliable, and easy to use technique for determining optimal peanut maturity.  We will also expand our study to the field scale.  We will plant the NESPAL field which has known variability in soils to peanut and use the vegetation indexes to identify different maturity patterns in the field.  Our ultimate goal is to provide peanut growers with a tool to do exactly what we propose.