Peanut maturity classification using hyperspectral imagery

Authors: ZOU, SHENG - University Of Florida; TSENG, YU-CHIEN - University Of Florida; ZARE, ALINA - University Of Florida; ROWLAND, DIANE L. - University Of Florida; TILLMAN, BARRY L. - University Of Florida; Yoon, Seung-Chul

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/14/2019
Publication Date: 11/1/2019
Citation: Zou, S., Tseng, Y., Zare, A., Rowland, D., Tillman, B., Yoon, S.C. 2019. Peanut maturity classification using hyperspectral imagery. Biosystems Engineering. https://doi.org/10.1016/j.biosystemseng.2019.10.019.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.10.019

Interpretive Summary: It is critical for peanut growers to accurately determine pod maturity to be able to maximize seed weight and quality, thus leading to optimal economic returns. The most commonly accepted method for growers to predict and optimize harvest time is to remove the exocarp from the pericarp (hull) and categorize the inner mesocarp color. The researchers at the University of Florida and the USDA-ARS collaborated to explore a hyperspectral imaging process capable of non-destructively detecting hyperspectral signatures of pericarp that were indicative of each of the major inner mesocarp color classes. The study found a consistent high classification accuracy using samples from different years and cultivars. 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 has potential to solve issues of labor intensity and subjective error that all current methods of peanut maturity determination have.

Technical Abstract: 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 vigour and other quality characteristics. During seed development, the inner mesocarp layer of the pericarp (hull) transitions in colour 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 colours into varying colour classes from immature (white, yellow, orange) to mature (brown, and black). This visual colour classification is time consuming because the exocarp must be manually removed. In addition, the visual classification process involves human assessment of colours, which leads to large variability of colour 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. Therefore, there is no need to remove the exocarp nor is there a need for subjective colour assessment in the proposed process. The results showed a consistent high classification accuracy 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 labour intensity and subjective error that all current methods of peanut maturity determination have.