Real-time straw moisture content detection system for mobile straw granulator

Published: 26 March 2024
Abstract Views: 1960
PDF: 70
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


In order to improve the molding rate of biomass particles extruded by ring mold of the mobile straw granulator, a real-time straw moisture content detection system based on frequency was designed in this paper. The detection system comprised the frequency based acquisition devices and the supporting circuits, and support vector regression based calculation method. The acquisition device contained a soil separation cylinder and a signal detection chamfer. The soil separation cylinder was used to remove the soil from the straw. The moisture of the straw was transformed into the relatively stable frequency for detection, but the temperature can affect the Brownian movement of free water. Hence, the designed signal detection chamfer mainly contained a frequency sensor and a temperature sensor. The proposed calculation method blended the frequency and temperature to acquire the accurate moisture of the straw. A water replenishment module was also designed to verify the effectiveness of the detection system, and it was used to supply water to the straw when it becomes too dry. The system was verified in the experimental plots and field. The actual moisture content was obtained by 105°C drying method. The results obtained in the experiment plots showed that the detectable moisture content range was between 9.09% to 46.68%, the maximum detection error was less than 0.44%, and the average absolute error was less than 0.33%, and the molding rate could reach approximately 94%. The results obtained in the fieldd showed that the average molding rate achieved was 93.57% and 89.76% for straws with moisture content of about 20% and 15%, respectively. The detection system comprehensively takes into account the influence of temperature and soil on moisture content and can effectively improve the working efficiency of the mobile straw granulator.



PlumX Metrics


Download data is not yet available.


Abdullah M.S.M., Rahiman M.H.F., Zakaria A., Kamarudin L.M., Mohamed L. 2019. A review on moisture measurement technique in agricultural silos. Proc. IOP Materials Science and Engineering. 705: 012001. Bristol: Temple Circus. DOI:
Almaleeh A., Zakaria A., Kamarudin L.M., Rahiman M.H.F., Ndzi D.L. Ismail I. 2022. Inline 3D volumetric measurement of moisture content in rice using regression-based ML of RF tomographic imaging. Sensors-BASEL. 22(1): 405. DOI:
Agrawal N., Thakur O.P., Anjani K.S. 2021. Analysis of electromechanical properties of electrode for enhancing electrostrictive capacitive sensor response. Mater. Today. Proc. 47(8): 1621-1626. DOI:
Amer M., Nour M., Ahmed M., Ookawara S., Nada S., Elwardany A. 2019. The effect of microwave drying pretreatment on dry torrefaction of agricultural biomasses. Bioresource. Technol. 286: 121400. DOI:
Basok B., Davydenko B., Pavlenko A.M. 2021. Numerical network modeling of heat and moisture transfer through capillary-porous building materials. Materials. 14(8): 1819. DOI:
Fan L., Chai Z., Wang Y., Wang Z., Zhao Q., Qin X. 2020. A novel handheld device for intact corn ear moisture content measurement. IEEE. T. Instrum. Meas. 69: 9157-9169. DOI:
Fan W., Chen Q., Chen M. 2022. Online capacitive detection method for moisture content of aggregate based on edge effect. Measurement. 203: 111962. DOI:
Gurol I.E., Basar E., Kucukyavuz D., Onat F.A. 2022. A novel orthogonal frequency division multiplexing with index modulation waveform with carrier frequency offset resistance and low peak-to-average power ratio. Int. J. Commun. Syst. 35(7): e5094. DOI:
Han J., Guo J., Zhang Z., Yang X., Shi Y., Zhou J. 2023. The rapid detection of trash content in seed cotton using near-infrared spectroscopy combined with characteristic wavelength selection. Agriculture. 13:1928. DOI:
Hartley R., Ghaffari M., Eustice R.M., Grizzle J.W. 2020. Contact-aided invariant extended Kalman filtering for robot state estimation. Int. J. Robot. Res. 39(4): 402-430. DOI:
Li C., Zhang X., Meng M., Li B., Li C. 2021. Capacitive Online Corn Moisture Content Sensor Considering Porosity Distributions: Modeling, Design, and Experiments. Appl. Sci. 11(16): 7655. DOI:
Li L., Bo M., Xue J., Shang G., Li S. 2021. Difference in corn kernel moisture content between pre-and post-harvest. J. Integr. Agr. 20(7): 1775-1782. DOI:
Li T., Ji Y.H., Zhang M., Li M.Z. 2017. Determining optimal CO2 concentration of greenhouse tomato based on PSO-SVM. Appl. Eng. Agric. 33(2): 157-166. DOI:
Lv W., Zhang M., Wang Y., Adhikari B. 2017. Online measurement of moisture content, moisture distribution, and state of water in corn kernels during microwave vacuum drying using novel smart NMR/MRI detection system. Dry. Technol. 36: 1592-1602. DOI:
Jain S., Mishra P. K., Mishra J., Thakare V. 2020. Design and analysis of H-Shape patch sensor for rice quality detection. Mater. Today. 29: 581-586. DOI:
Jafarpisheh N., Zaferani E.J., Teshnehlab M., Karimipour H., Parizi R.M., Srivastava G. 2021. A deep neural network combined with radial basis function for abnormality classification. Mobile. Netw. Appl. 26(6): 2318-2328. DOI:
NAMSTC, 2013. Machinery industry standard of the People’s Republic of China. JB/T 5161-2013. National Agricultural Machinery Standardization Technical Committee, Beijing, China.
Park S., Choi H. 2021. Characteristics of a superconducting DC circuit breaker according to L and C elements of LC divergent oscillation circuit. IEEE T. Appl. Supercon. 31(8), 5604604. DOI:
Sayari S., Mahdavi-Meymand A., Zounemat-Kermani M. 2021. Irrigation water infiltration modeling using machine learning. Comput. Electron. Agr. 180: 105921. DOI:
Sivtsov D.P., Khandetskyi V.S. 2015. Device to determine of fluorine concentration in fluorinated carbon powders. Syst. Technol. 1: 3-9.
Tran T.N., Lam B.M., Nguyen A.T., Le Q.B. 2022. Load forecasting with support vector regression: influence of data normalization on grid search algorithm. Int. J. Electr. Comput. 12(4): 3410-3420. DOI:
Thais L.C., Giovani A., Carlos H., Duarte C.R. 2021. Biomass feeding in a dilute pneumatic conveying system. Powder. Technol. 391: 321-333. DOI:
Vidal A.K.F., Daher R.F., Freitas R.S., Stida W.F., Lédo F.J.D.S., Silva V.B.D., Farias J.E. 2022. Growth curve in elephant grass genotypes based on morpho-agronomic traits for energy production. Chil. J. Agr. Res. 82(1): 78-87. DOI:
Wang J., Tang T., Tang H., Xu W., Zhou W., Wang Q. 2021. Design and experiment of on-line detection device for capacitive paddy rice moisture content of combine harvester. Tran. CSAE. 52(03): 143-152.
Wang X., Ma T., Yang T., Song P., Chen Z., Xie H. 2019. Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size. Int. J. Agric. Biol. Eng. 12(2): 132–140. DOI:
Wang R., Xu T., Zhao J., Wang Y., Xing J., Lyu T., et al. 2020. Effects of harvest date on maize grain moisture content and grain mechanical harvesting quality. J. Agr. Scitech-Iran. 22(11): 35-41.
Wang W., Gong Y., Bai X., Tan R., Huang W. 2021. Investigation on operating speed regulation system of mobile straw granulator. Trans. CSAE. 52(10): 186-195.
Wang W., Quan X. 2023. Estimation of live fuel moisture content from multiple sources of remotely sensed data. IEEE. Geosci. Remote. S. 20: 1-5. DOI:
Wang W., Zhang S., Li J., Zhang P., Chen Y. 2022. Effects of the twin-row planter with subsoiling on soybean growth and yield in northern China. J. Agric. Eng-ITALY. LIII: 1359. DOI:
Yigit E., Duysak H. 2022. Determination of flowing grain moisture contents by machine learning algorithms using free space measurement Data. IEEE. T. Instrum. Meas. 71:1-8. DOI:
Zhou S., Bilal M., Khan M.A., Muhammad T. 2021. Numerical analysis of thermal radiative maxwell nanofluid flow over-stretching porous rotating disk. Micromachines. 12(5): 540. DOI:

How to Cite

Wang, W., Gong, Y., Bai, X. and Tan, R. (2024) “Real-time straw moisture content detection system for mobile straw granulator”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1570.