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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Peanut and Small Grains Research Unit » Research » Publications at this Location » Publication #388094
Research Project: Genetic Improvement of Peanut for Production in the Southwest United States Region

Location: Peanut and Small Grains Research Unit

Title: Non-destructive method for measuring kernel weights from intact peanut pods using soft X-ray imaging

Author
item QIU, GUANGJUN - Guangdong Academy Of Agricultural Sciences
item LIU, YUANYUAN - Jilin Agricultural University
item WANG, NING - Oklahoma State University
item Bennett, Rebecca
item WECKLER, PAUL - Oklahoma State University

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/13/2023
Publication Date: 4/15/2023
Citation: Qiu, G., Liu, Y., Wang, N., Bennett, R., Weckler, P.R. 2023. Non-destructive method for measuring kernel weights from intact peanut pods using soft X-ray imaging. Agronomy. 13(4). Article 1127. https://doi.org/10.3390/agronomy13041127.
DOI: https://doi.org/10.3390/agronomy13041127

Interpretive Summary: In the U.S., peanut farmers receive premium prices for crops with high kernel quality. One component of kernel quality is the proportion of kernel weight to that of pod hulls and other matter. Kernel weight and size are also important traits for food processors. Current methods for evaluating proportion of peanut kernel weights are destructive, time-consuming, and labor-intensive. In this study, a non-destructive and efficient method was investigated to determine peanut kernel weights using soft X-ray imaging techniques. X-ray images of a total of 513 peanut pods from three commercial cultivars were taken using an X-ray imaging system. Kernel weights were estimated from the images using a computer program, and the estimated weights were compared to actual kernel weights. Results showed that X-ray images analyzed with the computer program can be used to efficiently and non-destructively estimate kernel weights from intact peanut pods. This approach may be useful for the peanut industry by saving time and labor when determining the proportion of kernel weights in farmers’ harvests.

Technical Abstract: In the U.S., peanut farmers receive premium prices for crops with high kernel grades. One component of kernel grade is the proportion of kernel weight to that of pod hulls and other matter. Kernel weight and size are also important traits for food processors. Current methods for evaluating peanut kernel grade are destructive, time-consuming and labor-intensive. In this study, a non-destructive and efficient method was investigated to determine peanut kernel weights using soft X-ray imaging techniques. X-ray images of a total of 513 peanut pods from three commercial cultivars were taken using a soft X-ray imaging system. The region of interest (ROI) from kernel images was extracted manually and also with a differential evolution (DE) segmentation algorithm. The comprehensive attenuation index (CAI) value was calculated from the segmented ROIs. Lastly, linear regression models were established between peanut kernel weights and CAI. The results demonstrated that the X-ray imaging technology, coupled with the differential evolution segmentation algorithm can be used to efficiently and non-destructively estimate kernel weights from intact peanut pods.