Abstract
Grain cleaning loss rate is an important performance index of combine harvesters which needs to be measured in real time during the harvesting operation. To improve the measurement accuracy and range, a grain loss sensor based on piezoelectric effect and adaptive neuro-fuzzy inference system (ANFIS) was proposed. A piezoelectric ceramic was fixed on the bottom of a thin sensitive plate to detect grain impact, and the sensitive plate was fixed to a support plate with a piece of shock-absorbing rubber between them to increase the attenuation rate of the vibration generated by grain impact. Based on the analysis of the reasons that restrict the improvement of measurement performance of traditional measurement methods, a novel signal processing circuit was designed. The circuit could simultaneously measure the number and energy of grain impacts, and output the results in the form of square wave voltage and analog voltage, respectively. Variation characteristics of the two output signals under different grain impact frequencies were analyzed. Then, a grain impact frequency prediction method based on ANFIS fusion of the two signals was proposed, and the established ANFIS model was trained through the calibration tests. Finally, measurement tests were carried out, and the results indicated that the measurement errors of grain impact were less than 2.5, 3.9, 4.4, 6.5 and 9.2% with measurement ranges of 100, 200, 600, 1000 and 1500 grain/s, respectively. With increase of MOG/grain mass ratio, the measurement error of the sensor was increased gradually due to the collision interference between MOG and grain. Compared with traditional sensors, the measurement accuracy and range were both improved significantly.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Badretdinov, I., Mudarisov, S., Lukmanov, R., Permyakov, V., Ibragimov, R., & Nasyrov, R. (2019). Mathematical modeling and research of the work of the grain combine harvester cleaning system. Computers and Electronics in Agriculture, 165, 104966. https://doi.org/10.1016/j.compag.2019.104966
Chai, X. Y., Xu, L. Z., Li, Y. M., Qiu, J., Li, Y., Lv, L. Y., & Zhu, Y. H. (2020a). Development and experimental analysis of a fuzzy grey control system on rapeseed cleaning loss. Electronics, 9(11), 1764. https://doi.org/10.3390/electronics9111764
Chai, X. Y., Zhou, Y., Xu, L. Z., Li, Y. M., Li, Y., & Lv, L. Y. (2020b). Effect of guide strips on the distribution of threshed outputs and cleaning losses for a tangential-longitudinal flow rice combine harvester. Biosystems Engineering, 198, 223–234. https://doi.org/10.1016/j.biosystemseng.2020.08.009
Civicioglu, P. (2007). Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS. IEEE Transactions on Image Processing, 16(3), 759–773. https://doi.org/10.1109/TIP.2007.891067
Craessaerts, G., De Baerdemaeker, J., Missotten, B., & Saeys, W. (2010). Fuzzy control of the cleaning process on a combine harvester. Biosystems Engineering, 106(2), 103–111. https://doi.org/10.1016/j.biosystemseng.2009.12.012
Craessaerts, G., Saeys, W., Missotten, B., & De Baerdemaeker, J. (2007). A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part I: Selection of relevant input variables for identification of the sieve losses. Biosystems Engineering, 98(2), 166–175. https://doi.org/10.1016/j.biosystemseng.2007.07.008
Himeur, Y., & Boukabou, A. (2017). An efficient impulsive noise cancellation scheme for power-line communication systems using ANFIS and chaotic interleaver. Digital Signal Processing, 66, 42–55. https://doi.org/10.1016/j.dsp.2017.04.005
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transaction on Systems, Man, and Cybernetics, 23(3), 665–685.
Kaveh, M., Rasooli Sharabiani, V., Amiri Chayjan, R., Taghinezhad, E., Abbaspour-Gilandeh, Y., & Golpour, I. (2018). ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture, 5(3), 372–387. https://doi.org/10.1016/j.inpa.2018.05.003
Khan, M. S., Semwal, M., Sharma, A., & Verma, R. K. (2020). An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI. Precision Agriculture, 21(1), 18–33. https://doi.org/10.1007/s11119-019-09655-9
Li, B., & Li, T. T. (2020). Prediction of cleaning loss of combine harvester based on neural network. International Journal of Pattern Recognition and Artificial Intelligence, 34(7), 2059021. https://doi.org/10.1142/S0218001420590211
Lian, Y., Chen, J., Guan, Z., & Song, J. (2021). Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm. International Journal of Agricultural and Biological Engineering, 14(1), 224–229. https://doi.org/10.25165/j.ijabe.20211401.5731
Liang, Z. W. (2021). Selecting the proper material for a grain loss sensor based on DEM simulation and structure optimization to improve monitoring ability. Precision Agriculture, 22, 1120–1133. https://doi.org/10.1007/s11119-020-09772-w
Liang, Z., Li, Y., Xu, L., & Zhao, Z. (2016). Sensor for monitoring rice grain sieve losses in combine harvesters. Biosystems Engineering, 147, 51–66. https://doi.org/10.1016/j.biosystemseng.2016.03.008
Liang, Z. W., Li, Y. M., Zhao, Z., Xu, L. Z., & Li, Y. (2015). Optimum design of grain sieve losses monitoring sensor utilizing partial constrained viscoelastic layer damping (PCLD) treatment. Sensors and Actuators a: Physical, 233, 71–78. https://doi.org/10.1016/j.sna.2015.06.010
Liu, C., & Leonard, J. (1993). Monitoring actual grain loss from an axial flow combine in real time. Computers and Electronics in Agriculture, 9(3), 231–242. https://doi.org/10.1016/0168-1699(93)90041-X
Li, Y., Xu, L. Z., Zhou, Y., Li, B. J., Liang, Z. W., & Li, Y. M. (2018). Effects of throughput and operating parameters on cleaning performance in air-and-screen cleaning unit: A computational and experimental study. Computers and Electronics in Agriculture, 152, 141–148. https://doi.org/10.1016/j.compag.2018.07.019
Ni, J., Mao, H. P., & Pang, R. R. (2015). Design and experimentation of piezoelectric crystal sensor array for grain cleaning loss. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2015/754278
Omid, M., Lashgari, M., Mobli, H., Alimardani, R., Mohtasebi, S., & Hesamifard, R. (2010). Design of fuzzy logic control system incorporating human expert knowledge for combine harvester. Expert Systems with Applications, 37(10), 7080–7085. https://doi.org/10.1016/j.eswa.2010.03.010
Reyns, P., Missotten, B., Ramon, H., & De Baerdemaeker, J. (2002). A review of combine sensors for precision farming. Precision Agriculture, 3(2), 169–182. https://doi.org/10.1023/A:1013823603735
Reinke, R., Dankowicz, H., Phelan, J., & Kang, W. (2011). A dynamic grain flow model for a mass flow yield sensor on a combine. Precision Agriculture, 12(5), 732–749. https://doi.org/10.1007/s11119-010-9215-0
Shoji, K., & Miyamoto, M. (2014). Improving the accuracy of estimating grain weight by discriminating each grain impact on the yield sensor. Precision Agriculture, 15(1), 31–43. https://doi.org/10.1007/s11119-013-9327-4
Sparham, M., Sarhan, A. A. D., Mardi, N. A., Hamdi, M., & Dahari, M. (2017). ANFIS modeling to predict the friction forces in CNC guideways and servomotor currents in the feed drive system to be employed in lubrication control system. Journal of Manufacturing Processes, 28, 168–185. https://doi.org/10.1016/j.jmapro.2017.05.020
Tagarakis, A., Koundouras, S., Papageorgiou, E. I., Dikopoulou, Z., Fountas, S., & Gemtos, T. A. (2014). A fuzzy inference system to model grape quality in vineyards. Precision Agriculture, 15(5), 555–578. https://doi.org/10.1007/s11119-014-9354-9
Wu, Y. H., Li, X. Y., Mao, E. R., Du, Y. F., & Yang, F. (2020). Design and development of monitoring device for corn grain cleaning loss based on piezoelectric effect. Computers and Electronics in Agriculture, 179, 105793. https://doi.org/10.1016/j.compag.2020.105793
Xu, L. Z., Wei, C. C., Liang, Z. W., Chai, X. Y., Li, Y. M., & Liu, Q. (2019). Development of rapeseed cleaning loss monitoring system and experiments in a combine harvester. Biosystems Engineering, 178, 118–130. https://doi.org/10.1016/j.biosystemseng.2018.11.001
Yilmaz, D., & Sagiroglu, H. C. (2015). Development of measurement system for grain loss of some chickpea varieties. Measurement, 66, 73–79. https://doi.org/10.1016/j.measurement.2015.01.025
Zhao, Z., Li, Y. M., Chen, J., & Xu, J. J. (2011). Grain separation loss monitoring system in combine harvester. Computers and Electronics in Agriculture, 76(2), 183–188. https://doi.org/10.1016/j.compag.2011.01.016
Zhao, Z., Li, Y. M., Liang, Z. W., & Chen, Y. (2012). Optimum design of grain impact sensor utilising polyvinylidene fluoride films and a floating raft damping structure. Biosystems Engineering, 112(3), 227–235. https://doi.org/10.1016/j.biosystemseng.2012.04.005
Zhao, Z., Li, Y. M., Liang, Z. W., & Gong, Z. Q. (2013). DEM simulation and physical testing of rice seed impact against a grain loss sensor. Biosystems Engineering, 116(4), 410–419. https://doi.org/10.1016/j.biosystemseng.2013.10.002
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 51775246), Natural Science Foundation of Jiangsu Province (No. BK20201421), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX21-3380) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADP-2018-87).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jin, M., Zhao, Z., Chen, S. et al. Improved piezoelectric grain cleaning loss sensor based on adaptive neuro-fuzzy inference system. Precision Agric 23, 1174–1188 (2022). https://doi.org/10.1007/s11119-022-09879-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-022-09879-2