Using a distributed network of microwave moisture sensors to monitor in-shell kernel moisture content in real-time during drying and storage

Authors: Lewis, Micah; Trabelsi, Samir

Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 4/2/2024
Publication Date: 11/22/2024
Citation: Lewis, M.A., Trabelsi, S. 2024. Using a distributed network of microwave moisture sensors to monitor in-shell kernel moisture content in real-time during drying and storage. ASABE Annual International Meeting. 10.13031/aim.202400852.
DOI: https://doi.org/10.13031/aim.202400852

Interpretive Summary:

Technical Abstract: After peanuts are harvested, they remain in-shell as they go through postharvest processes. A representative sample has to be cleaned of all foreign material and shelled for kernel moisture content determination with the official moisture meter. Such limitations prohibit real-time determination of kernel moisture content during drying and while in storage. Peanuts must be dried down to = 10.49% kernel moisture content for grading purposes, and that kernel moisture content must be maintained for safe storage. Presently, there is no commercially available moisture sensor for in-shell kernel moisture content determination, and it would be extremely helpful to monitor moisture content at different locations during drying and storage. A microwave moisture sensor, developed within USDA ARS, has demonstrated the utility of nondestructive, instantaneous kernel moisture content determination without having to shell the peanut, both in static applications as a benchtop sensor and in dynamic applications as a real-time monitoring sensor during drying. Real-time, in-shell kernel moisture content sensing applications in storage are also feasible as changes in moisture content occur more gradually than during drying. The microwave sensor operates at 5.8 GHz and uses low-intensity microwaves in a free-space transmission measurement of the attenuation and phase shift caused by the in-shell peanuts between its transmitting and receiving antennas. From these parameters, the dielectric properties were calculated and correlated to kernel moisture content, providing a method of real-time determination of in-shell kernel moisture content from 6 – 18% moisture content and 10 – 40 °C. Several microwave sensors, equipped with a microprocessor, were implemented to communicate over Wi-Fi using ThingSpeak, an open-source IoT platform. A quarter-scale peanut drying trailer was filled with peanuts, and the microwave sensors were placed at different locations for each drying trial. Many drying trials were run with varying initial moisture contents. During each trial, samples were extracted and shelled to conduct moisture tests using the reference oven drying method. In-shell kernel moisture content was determined with a standard error of performance (SEP) of = 0.5% kernel moisture content. Data from all sensors were available for monitoring in real-time through ThingSpeak, and they were helpful in evaluating drying uniformity and efficiency.