Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

Hang Yin, Fangyan Ma, Dongwei Wang, Xiaoning He, Yuanyuan Yin, Chao Song and Liqing Zhao ()
Additional contact information
Hang Yin: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Fangyan Ma: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Dongwei Wang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Xiaoning He: 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
Liqing Zhao: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2023, vol. 13, issue 6, 1-16

Abstract: The rapid and nondestructive detection of tea leaf moisture content (MC) is of great significance to processing tea with an automatic assembly line. This study proposes an MC detection method based on microwave scattering parameters (SPs). Through the established free-space electromagnetic measurement device, 901 different frequency points are taken between 2.45 and 6 GHz using a vector network analyzer (VNA). The SPs of tea leaves with different moisture contents (5.72–55.26%) at different bulk density and different sample thicknesses were measured. The relationship between frequency, S 21 amplitude and moisture content, thickness, and bulk density of tea was analyzed using correlation coefficients, significance analysis, and model construction. Back propagation (BP) neural network, decision tree (DT), and random forest (RF) MC prediction models were established with the frequency, amplitude, and phase of the SPs, thickness, and bulk density of the samples as inputs. The results showed that the RF-based model had the best performance, with determination coefficient (R 2 ) = 0.998, mean absolute error (MAE) = 0.242, and root mean square error (RMSE) = 0.614. Compared to other nondestructive testing processes for tea, this method is simpler and more accurate. This study provides a new method for the detection of tea MC, which may have potential applications in tea processing.

Keywords: Moisture content; microwave measurement; tea; free-space method (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/6/1136/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/6/1136/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:6:p:1136-:d:1157982

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().