MATURITY DETECTION IN PEANUTS (ARACHIS-HYPOGAEA L) USING MACHINE VISION

The flavor of shelled peanuts (Arachis hypogaea L.) is dependent on se veral factors including variety, growing conditions, post harvest oper ations, and maturity of the crop at harvest. At harvest, the peanut cr op will have kernels in different stages of development due to the pla nt's indeterminate fruiting nature. The maturity of individual peanut kernels can be determined by surface texture characteristics. However, at present, there is no automated method for separating peanut kernel s by maturity. To facilitate development of a method, peanuts were han d classified into three maturity groupings (mature, mid-mature, and im mature). A machine vision algorithm was developed to find useful surfa ce texture descriptors for detecting each peanut maturity group. A des criptor derived from the gradient image of a peanut was found to be mo st useful in identifying peanuts of the mid-mature and immature groups in three market size classifications (jumbo, medium, and No. 1) when either white background or black background and red filter conditions were used. The gray level histogram characterization of peanut provide d some information but additional experimentation will be necessary to derive texture descriptors useful for maturity sorting. Vision analys is of the gradient image appears as a promising technique for the deve lopment of an automated peanut maturity detection system.