https://doi.org/10.4081/jae.2024.1570
Real-time straw moisture content detection system for mobile straw granulator
Published: 26 March 2024
Abstract Views: 2059
PDF: 216
HTML: 10
HTML: 10
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.
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.
Authors
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.
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: https://doi.org/10.1088/1757-899X/705/1/012001
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: https://doi.org/10.3390/s22010405
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: https://doi.org/10.1016/j.matpr.2021.04.320
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: https://doi.org/10.1016/j.biortech.2019.121400
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: https://doi.org/10.3390/ma14081819
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: https://doi.org/10.1109/TIM.2020.2994603
Fan W., Chen Q., Chen M. 2022. Online capacitive detection method for moisture content of aggregate based on edge effect. Measurement. 203: 111962.
DOI: https://doi.org/10.1016/j.measurement.2022.111962
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: https://doi.org/10.1002/dac.5094
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: https://doi.org/10.3390/agriculture13101928
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: https://doi.org/10.1177/0278364919894385
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: https://doi.org/10.3390/app11167655
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: https://doi.org/10.1016/S2095-3119(20)63245-2
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: https://doi.org/10.13031/aea.11578
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: https://doi.org/10.1080/07373937.2017.1418751
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: https://doi.org/10.1016/j.matpr.2020.07.317
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: https://doi.org/10.1007/s11036-021-01835-0
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: https://doi.org/10.1109/TASC.2021.3107832
Sayari S., Mahdavi-Meymand A., Zounemat-Kermani M. 2021. Irrigation water infiltration modeling using machine learning. Comput. Electron. Agr. 180: 105921.
DOI: https://doi.org/10.1016/j.compag.2020.105921
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: https://doi.org/10.11591/ijece.v12i4.pp3410-3420
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: https://doi.org/10.1016/j.powtec.2021.06.020
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: https://doi.org/10.4067/S0718-58392022000100078
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: https://doi.org/10.25165/j.ijabe.20191202.4708
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: https://doi.org/10.1109/LGRS.2023.3291718
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: https://doi.org/10.4081/jae.2022.1359
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: https://doi.org/10.1109/TIM.2022.3165740
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: https://doi.org/10.3390/mi12050540
How to Cite
Wang, W. (2024) “Real-time straw moisture content detection system for mobile straw granulator”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1570.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Adilkhan Niyazbayev, Francesco Garbati Pegna, Kanat Khazimov, Erik Umbetov, Kulmuhanbet Akhmetov, Zhadyra Sagyndykova, Marat Khazimov, Power need of an implement for removing polymer residues from the soil surface in Kazakh horticulture , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Daniela Lovarelli, Elisabetta Riva, Gabriele Mattachini, Marcella Guarino, Giorgio Provolo, Assessing the effect of barns structures and environmental conditions in dairy cattle farms monitored in Northern Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Xin Yu, Ling Zhao, Zongbin Liu, Yiqing Zhang, Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Eliseo Roma, Pietro Catania, Mariangela Vallone, Santo Orlando, Unmanned aerial vehicle and proximal sensing of vegetation indices in olive tree (Olea europaea) , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Giuliano Vox, Pierfrancesco Losito, Fabio Valente, Rinaldo Consoletti, Giacomo Scarascia-Mugnozza, Evelia Schettini, Cristoforo Marzocca, Francesco Corsi, A wireless telecommunications network for real-time monitoring of greenhouse microclimate , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Angelo Fabbri, Chiara Cevoli, Giulia Tabanelli, Fausto Gardini, Adriano Guarnieri, Numerical models of mass transfer during ripening and storage of salami , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Davide M. Giordano, Davide Facchinetti, Domenico Pessina, The spading machine as an alternative to the plough for the primary tillage , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Siti Hanggita Rachmawati, Zakaria Hossain, Jim Shiau, Ground improvement using waste shell for farm roads and embankments , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
- Pankaj Tyagi, Rahul Semwal, Anju Sharma, Uma Shanker Tiwary, Pritish Varadwaj, E-nose: a low-cost fruit ripeness monitoring system , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Enrico Antonio Chiaradia, Daniele Ferrari, Gian Battista Bischetti, Arianna Facchi, Olfa Gharsallah, Marco Romani, Claudio Gandolfi, Monitoring water fluxes in rice plots under three different cultivation methods , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
<< < 13 14 15 16 17 18 19 20 21 22 > >>
You may also start an advanced similarity search for this article.
