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Estimation of Peanut Maturity Using Color Image Analysis
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1801669.(doi:10.13031/aim.201801669)
Authors: Wei-zhen Liang, Kendall R Kirk, James Thomas
Keywords: Peanut maturity; Mesocarp; Exocarp; Mahalanobis distance; Statistical regression
Abstract. Peanuts pods grow underground and mature unevenly, resulting that choosing the correct time to harvest is more complicated than other crops. Pod maturity can be determined by blasting with a pressure washer to remove outer skin of the pod (exocarp) to expose the color of the middle layer (mesocarp). The mesocarp color changes with maturity from white to yellow, orange, brown and finally black. The sum of percentage from orange, brown, and black mesocarp (OBB) color and black color (BL) represent the kernels are mature enough to harvest. The goal of this research is to identify methodologies to estimate OBB and BL of the pods using RGB images taken in the field and validate the proposed model using other pod images. The Mahalanobis distance classification method was used to process sets of images and calculate pods area (number of pixels) corresponding to two classes (mesocarp and background) with nine different color groups. The results showed a performance of 94% effectiveness for mesocarp using Mahalanobis distance classification. Statistical regression model for OBB and BL were developed based on 315 images of peanut pods taken from the field. The R2 and root mean square error of predicted and actual OBB were 0.93 and 4.1%, respectively. The R2 and root mean square error of predicted and actual BL were 0.88 and 1.8%, respectively. The validation of OBB using other images provided reasonable estimation (R2 = 0.98 and RMSE = 2.73%). This approach proposed a simple, inexpensive, and non-destructive method for peanut maturity estimation.
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