Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm
Keywords:
monitoring system, combine harvester, paddy rice, grain loss, sensor, data mining, decision tree, developmentAbstract
China has the world’s largest planting area of paddy rice, but large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Reducing grain loss is an effective way to increase production and revenue. In this study, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for verifying the precision. The development of the monitoring system for grain loss included two stages: the first stage was to collect impact signals using a piezoelectric film, extract the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component), and identify the kernel impact signals using the J48 (C4.5) Decision Tree algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to the results, the highest monitoring accuracy was 99.3% (moisture content 30.8% and grain/impurity ratio 1/2.5), the average accuracy of the monitoring tests was 92.6%, and monitoring of grain/impurity ratios between 1/1 and 1/1.5 (>95.4%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 88.2%, 75.7% and 78.8% at moisture contents of 10.4%, 19.6% and 30.4%. Keywords: monitoring system, combine harvester, paddy rice, grain loss, sensor, data mining, decision tree, development DOI: 10.25165/j.ijabe.20211401.5731 Citation: Lian Y, Chen J, Guan Z H, Song J. Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm. Int J Agric & Biol Eng, 2021; 14(1): 224–229.References
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[2] Liang Z, Li Y, Zhao Z, Chen Y. Structure optimization and performance experiment of grain loss monitoring sensor in combine harvester in using modal analysis. Transactions of the CSAE, 2013; 29(4): 22–29. (in Chinese)
[3] Liang Z, Li Y, Zhao Z, Xu L, Li Y. Optimum design of grain sieve losses monitoring sensor utilizing partial constrained viscoelastic layer damping (pcld) treatment. Sensors and Actuators A: Physical, 2015; 233: 71–82.
[4] Liang Z, Li Y, Zhao Z, Xu L. Structure optimization of a grain impact piezoelectric sensor and its application for monitoring separation losses on tangential-axial combine harvesters. Sensors, 2015; 15(1): 1496–1517.
[5] Liang Z, Li Y, Xu L, Zhao Z, Tang Z. Optimum design of an array structure for the grain loss sensor to upgrade its resolution for harvesting rice in a combine harvester. Biosystems Engineering, 2017; 157: 24–34.
[6] Liu C L, Lin H Z, Li Y M, Gong L, Miao Z H. Analysis on status and development trend of intelligent control technology for agricultural equipment. Transactions of the CSAM, 2020; 51(1): 1–18. (in Chinese)
[7] Zhao Z, Li Y, Liang Z, Chen Y. Optimum design of grain impact sensor utilising polyvinylidene fluoride films and a floating raft damping structure. Biosystems Engineering, 2012; 112(3): 227–235.
[8] Zhao Z, Li Y, Chen J, Xu J. Grain separation loss monitoring system in combine harvester. Computers and Electronics in Agriculture, 2011; 76(2): 183–188.
[9] Chen J, Ning X, Li Y, Yang G, Wu P, Chen S. A fuzzy control strategy for the forward speed of a combine harvester based on KDD. Applied Engineering in Agriculture, 2017; 33(1): 15–22.
[10] Maertens K, Ramon H, De Baerdemaeker J. An on-the-go monitoring algorithm for separation processes in combine harvesters. Computers and Electronics in Agriculture, 2004; 43(3): 197–207.
[11] Craessaerts G, Saeys W, Missotten B, De Baerdemaeker J. Identification of the cleaning process on combine harvesters. Part I: A fuzzy model for prediction of the material other than grain (MOG) content in the grain bin. Biosystems Engineering, 2008; 101(1): 42–49.
[12] Craessaerts G, Saeys W, Missotten B, De Baerdemaeker J. Identification of the cleaning process on combine harvesters, Part II: A fuzzy model for prediction of the sieve losses. Biosystems Engineering, 2010; 106(2): 97–102.
[13] Craessaerts G, De Baerdemaeker J, Missotten B, Saeys W. Fuzzy control of the cleaning process on a combine harvester. Biosystems Engineering, 2010; 106(2): 103–111.
[14] Hiregoudar S, Udhaykumar R, Ramappa K T, Shreshta B, Meda V, Anantachar M. In: Artificial neural network for assessment of grain losses for paddy combine harvester a novel approach. Control, Computation and Information Systems. ICLICC 2011. Communications in Computer and Information Science, Berlin, Heidelberg: Springer, 2011; 140: 221–231.
[15] Madhusudana C K, Kumar H, Narendranath S. Fault diagnosis of face milling tool using Decision Tree and sound signal. Materials Today: Proceedings, 2018; 5(5): 12035–12044.
[16] Mu Y S, Liu X D, Wang L. A Pearson’s correlation coefficient based decision tree and its parallel implementation. Information Sciences, 2018; 435: 40–58.
[17] Chen J, Lian Y, Li Y M. Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm. Computers and Electronics in Agriculture, 2020; 175: 105591. doi: 10.1016/j.compag.2020.105591.
[18] Jönsson M, Olsson B, Mörtberg U, Sjögren J, Larsolle A, Hannrup B, et al. A spatially explicit decision support system for assessment of tree stump harvest using biodiversity and economic criteria. Sustainability, 2020; 12(21): 8900. doi: 10.3390/su12218900.
[19] Han J, Kim J, Park S, Son S, Ryu M. Seismic vulnerability assessment and mapping of Gyeongju, South Korea using frequency ratio, decision tree, and random forest. Sustainability, 2020; 12(8): 1–22.
[20] Arabameri A, Sanchini E K, Pal S C, Saha A, Chowdhuri I, Lee S, et al. Novel credal decision tree-based ensemble approaches for predicting the landslide susceptibility. Remote Sensing, 2020; 12: 3389. doi: 10.3390/rs12203389.
[21] Chen J, Lian Y, Zou R, Zhang S, Ning X B, Han M N. Real-time grain breakage sensing for rice combine harvesters using machine vision technology. Int J Agric & Biol Eng, 2020; 13(3): 194–199.
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Published
2021-02-10
How to Cite
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. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5731
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Information Technology, Sensors and Control Systems
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