A. Lynn Abbott


This paper concerns the assessment of peanut pod maturity through automated visual analysis of the middle layer of the shell, known as the mesocarp. Moisture in the mesocarp decreases with age, resulting in significant changes in color and rigidity. As peanuts mature, the color of the mesocarp gradually changes from white to yellow to dark brown, with progressively darker intensity levels. A technique is presented here in which pod maturity is estimated based on a combination of mesocarp color (visible-light wavelengths) and pod size. For each peanut pod, a voting procedure and nearest-neighbor classification are used to assign 1 of 10 color classes. The color class of each pod, together with the pod's size, are then used to assign 1 of 7 different maturity classes. In the experimental results reported here, precision and recall values for maturity classification were both 66.5%, as compared to classification by a human expert. The proportion of pods in the combined brown-black class, relative to the total sample size, can be used to estimate overall crop maturity. For this task, the automated system identified the number of brown-black peanuts per sample with an accuracy of 92.5%. This approach has strong potential for automatically determining suitable harvest dates for peanut crops.

Ekta Bindlish, A. Lynn Abbott, Maria Balota: Assessment of Peanut Pod Maturity. WACV 2017: 688-696


A. Lynn Abbott

Publication Details

Date of publication:
May 15, 2017
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