Toward Understanding of the Peanut Maturity Classification Process

Abstract

HighlightsThe manual mesocarp color-based classification process for runner peanuts was investigated to inform the development of an automatic method for peanut maturity classification.The results show that the maturity classification process is not solely dependent on mesocarp color. Other factors, such as the shape of the sample distribution, are considered during the manual process.Color detection and distribution shape should be considered in the future development of automatic peanut maturity classification system.Abstract. Optimizing the harvest date is essential for maximizing peanut yields and ensuring top-quality peanuts. Currently, the most popular and acceptable method for peanut maturity classification is a manual method called the peanut profile board, which is based on the color of the peanut’s mesocarp layer. An automated system could greatly improve the efficiency of the current manual method. However, little research has been done on the manual method, so there is not enough insight to guide the development of an automatic system. In this study, the aim was to analyze the process of using the peanut profile board and gather relevant information for developing an automatic method for peanut maturity classification. Specifically, a GoPro camera was used to record on-site sample classification by trained human classifiers. Classification times were analyzed through the recorded videos, and the classification results were compared with a solely color-based classification method to see if trained classifiers consider factors other than color. With an average classification time of 11.98 s per peanut, the Black 1 color category required more classification time than the other color categories, which indicates that Black 1 is most important for maturity classification. The results from the solely color-based classification method had an average color matching rate of 69.3% with the results of the trained classifiers, which indicates that peanut maturity evaluation is not based only on color. The maturity prediction, defined as the ratio of the number of peanuts in the combined brown and black categories to the total sample size, differed by an average of only 13.4% between the two classification methods. This indicates that mesocarp color is the major factor for peanut maturity classification. Overall, the results of this study prove the feasibility of automated color detection and classification for peanut maturity evaluation. Future development of such a system will benefit from the insights found in this study. Keywords: Color classification, Peanut classification time, Peanut maturity classification procedure.