Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Estimation of Total and Kernel Mass in Walnuts Using X-ray Attenuation Mapping Method

Articles in Press

Document Type : Research Article- En

Authors

A. Taheri
M. Askari

Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran

https://doi.org/10.22067/jam.2026.99948.1504
Abstract
In this study, we propose a nondestructive method for estimating kernel mass in walnuts using X-ray inspection. X-ray radiographs of 350 walnuts were used to develop four attenuation and intensity-based features derived from segmented kernel regions. Three regression approaches namely linear, second-order polynomial, and exponential were fitted to relate these features to total walnut mass and kernel mass, using an 80/20 fit–test split (n = 280/70). The second-order polynomial (quadratic) model consistently outperformed the linear and exponential alternatives for both response variables. For total mass, the quadratic model achieved a test-set R2 = 0.87, RMSE of 0.75 g, MAE of 0.59 g, and a relative error of 6.15%. For kernel mass, the same model type achieved R2 = 0.72, RMSE of 0.73 g, MAE of 0.58 g, and a relative error of 17.13%. These results indicate that X-ray attenuation-based features, combined with a nonlinear regression framework, can provide a reliable estimate of total walnut mass and a moderately accurate estimate of kernel mass. This approach offers a nondestructive, image-based alternative to manual cracking and weighing, with potential applications in automated sorting and quality-control systems for walnut processing.

Keywords

Attenuation map
Estimation function
Kernel mass
Walnut
X-ray
Subjects

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 14 October 2026

  • Receive Date 21 July 2026
  • Revise Date 27 September 2026
  • Accept Date 28 September 2026
  • First Publish Date 14 October 2026

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