  Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a
producer because of its impact on seed weight (yield), and critically
influences seed vigor and other quality characteristics. During seed
development, the inner mesocarp layer of the pericarp (hull) transitions in
color from white to black as the seed matures. The maturity assessment process
involves the removal of the exocarp of the hull and visually categorizing the
mesocarp color into varying color classes from immature (white, yellow, orange)
to mature (brown, and black). This visual color classification is time
consuming because the exocarp must be manually removed. In addition, the visual
classification process involves human assessment of colors, which leads to
large variability of color classification from observer to observer. A more
objective, digital imaging approach to peanut maturity is needed, optimally
without the requirement of removal of the hull's exocarp. This study examined
the use of a hyperspectral imaging (HSI) process to determine pod maturity with
intact pericarps. The HSI method leveraged spectral differences between mature
and immature pods within a classification algorithm to identify the mature and
immature pods. The results showed a high classification accuracy with
consistency using samples from different years and cultivars. In addition, the
proposed method was capable of estimating a continuous-valued, pixel-level
maturity value for individual peanut pods, allowing for a valuable tool that
can be utilized in seed quality research. This new method solves issues of
labor intensity and subjective error that all current methods of peanut
maturity determination have.
