Research Output
A new method of rice moisture content determination using voxel weighting-based from radio tomography images
Measurement of rice moisture content based on quantitative analysis from radio tomography images
1 - 2 of 2
Publication
This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.
Publication
Inefficient storage of paddy and rice grains can lead to grain deterioration, resulting in post-harvest losses ranging from 10% to 30%. The quality of grains cannot be improved throughout the storage period. Therefore, following the mechanisation of agricultural industries, air dryers have been developed to control the crops’ moisture level by blowing ambient or heated air into the silo to improve the aeration and allow the grains to be preserved with minimal loss of quality until the appropriate time for managing and marketing processes. However, the conventional sampling method used to measure the moisture level is inefficient because it is very localised and only represents part of the moisture distribution inside the bulk grains. Additionally, incorporating advanced technologies can be a significant cost limitation for small-scale industries. Thus, to address the issue, this research study developed a radio tomographic imaging (RTI) system in a silo-scale prototype using 20 sensor nodes operating at 2.4 GHz to localise and monitor the moisture level constructively. The RTI system reconstructs the cross-sectional images across the rice silo by measuring radio frequency attenuation, in terms of received signal strength (RSS) quality, caused by the rice moisture phantoms within the wireless sensor network (WSN) area. A total of five phantoms’ profiles having a percentage of moisture content (MC)of 15%, 20% and 25% were reconstructed using four image reconstruction algorithms,Linear Back Projection (LBP), Filtered Back Projection (FBP), Newton’s One-step ErrorReconstruction (NOSER) and Tikhonov Regularisation. Then, an image quality assessment,Mean Structural Similarity Index (MSSIM), was utilised to evaluate the performance of thereconstructed images. Lastly, a numerical method based on the first-order linear regressionmodel was introduced as a preliminary approach toward the method’s establishment. In summary, the experimental results demonstrated average image quality scores for all MClevels (15%, 20% and 25%), where the range scores are 0.2776 – 0.4755. Based on thenumerical analysis, the results support the possibility of engaging the proposed techniqueto monitor the moisture level inside a rice silo with the highest and lowest correlationcoefficients of 0.7218 and 0.5442, respectively.