Inline 3D volumetric measurement of moisture content in rice using regression-based ML of RF tomographic imaging

Abd Alazeez Almaleeh, Ammar Zakaria*, Latifah Munirah Kamarudin, Mohd Hafiz Fazalul Rahiman, David Lorater Ndzi, Ismahadi Ismail

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
45 Downloads (Pure)


The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.
Original languageEnglish
Article number405
Number of pages12
Issue number1
Publication statusPublished - 5 Jan 2022


  • 3D volumetric
  • moisture content
  • machine learning
  • tomographic imaging


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