Author
Listed:
- Shuhao Wang
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Songling Du
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Yuanyuan Yin
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Chao Song
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Chuang Liu
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Rui Qian
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
- Liqing Zhao
(College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)
Abstract
Detecting the moisture content of stored maize kernels is critical for minimizing post-harvest losses. To measure the moisture content of maize kernels under low-temperature conditions, a small-strip transmission line device was employed to construct a non-destructive measurement platform. The dielectric constant of maize kernels with varying moisture content was measured at temperatures ranging from −15 °C to 20 °C and frequencies between 1 and 200 MHz. By using the dielectric constant, frequency, and temperature as input variables, along with volume density and scattering parameter characteristics, three moisture content prediction models—SPO-SVM, XGBoost, and GA-BP—were established. The results show that temperature significantly affects the dielectric constant of maize kernels, especially when the moisture levels exceed 22.4%. The prediction model significantly improves the prediction accuracy under low-temperature conditions after introducing the volume density feature. Furthermore, incorporating the multi-phase and amplitude characteristics of scattering parameters further improves the model’s performance. This study verifies the mechanism and behavior of dielectric constant variations in maize kernels under low-temperature conditions. The proposed model effectively mitigates measurement errors caused by the icing of free water and is well suited for measuring maize moisture content under low-temperature conditions.
Suggested Citation
Shuhao Wang & Songling Du & Yuanyuan Yin & Chao Song & Chuang Liu & Rui Qian & Liqing Zhao, 2025.
"Establishing a Low-Temperature Maize Kernel Moisture Content Prediction Model Based on Dielectric Constant Measurement,"
Agriculture, MDPI, vol. 15(5), pages 1-22, February.
Handle:
RePEc:gam:jagris:v:15:y:2025:i:5:p:507-:d:1600401
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:507-:d:1600401. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.