3D localization of moisture distribution in rice silo using RTI based on Wi-Fi signal

Rice is a staple food which conveys a sign of local culture throughout Asia, particularly in South Asia, and it is consumed daily, either as cooked rice or indirectly as rice flour. Several measures are aimed at raising national rice production for the next few years as expected to see rising demand and falling supply. Researchers discovered that, in order to ensure an adequate supply of domestic output, appropriate silo facilities together with good agriculture practices should be addressed to resolve this prolonged issue in the agricultural industry. A silo's main purpose is to protect grain crops from the environment, especially moisture build-up, pest infestation, and fungal load. Therefore, grain storage is part of an important aspect of quality assurance in post-harvest activities. One of the main issues is the moisture content of the rice during storage. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or very much relies on single rod sensors placed at random locations during measurement. The moisture content of stored rice is directly dependent on the surrounding and environmental factors which in turn affects the quality and economic value of the rice. In addition, the moisture content needs to be measured frequently for prompt action. Until today, the current sensor is very localized and the continuous measurement microwave sensor is very costly. There is also no commercially available 3D volumetric measurement of rice moisture content in the silo. This research reported preliminary work using a off-the-shelf wireless device i.e., esp8266 which can be placed around the silo to measure the change of moisture. A new technique has been proposed in this research, which uses a more accurate reconstruction of the image generated by radio tomography based on Wi-Fi signals. The technique is based on the Hybrid Tikhonov-LASSO (HTL) combines the advantage of Tikhonov and the LASSO method, which achieved the reconstructed image is cleaner. Also used Regression-based machine learning (ML) on RF Tomographic Imaging which can provide 3D moisture content measurements to localize the moisture distribution in storage. This proposed technique can detect multiple levels of localized moisture distributions in the silo with high accuracies, 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.