Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone
Keywords:
smartphone, kinematic sequence, operating behavior, fuel consumption forecast, tractorAbstract
In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions. Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window. Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized. Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns. When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively. In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing. Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption. Keywords: smartphone, kinematic sequence, operating behavior, fuel consumption forecast, tractor DOI: 10.25165/j.ijabe.20221504.7454 Citation: Yang L L, Tian W Z, Zhai W X, Wang X X, Chen Z B, Wen L, et al. Behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. Int J Agric & Biol Eng, 2022; 15(4): 154–162.References
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[3] Barth M, An F, Younglove T, Levine C, Scora G. Development of a comprehensive modal emissions model. Transportation Rsearch Board, 2000; 435p.
[4] Cheng Y, Zhang J L, Zhang S J, Guo J F, Zhang D. Evaluation of eco-driving behavior and fuel-saving potential of large freight vehicles. Journal of Transportation Systems Engineering and Information Technology, 2020; 20(6): 253–258. (in Chinese)
[5] Gu Q H, Wang Q, Jiang S, Ma P P. Research on fuel consumption prediction of truck in open-pit mine based on PSOGA-SVM. Mining Research and Development, 2021; 41(8): 161–166. (in Chinese)
[6] Sun R X, Chen Y H, Dubey A, Pugliese P. Hybrid electric buses fuel consumption prediction based on real-world driving data. Transportation Research: Part D: Transport and Environment, 2020; 91: 102637. doi: 10.1016/j.trd.2020.102637.
[7] Perrotta F, Parry T, Neves L. Application of machine learning for fuel consumption modelling of trucks. 2017 IEEE International Conference on Big Data (Big Data), Boston, USA, 2017; pp.3810–3815. doi: 10.1109/BigData.2017.8258382.
[8] Yao Y, Zhao X H, Zhang Y L, Chen C, Rong J. Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions. Transportation Research Part D: Transport and Environment, 2020; 79: 102224. doi: 10.1016/j.trd.2020.102224.
[9] Li X. Effective measures for prevention and control of agricultural machinery pollution. Fujian Agricultural Machinery, 2020; 2: 6–9. (in Chinese)
[10] Zhao B D, Feng L L, Deng Y, Cao L H. Real-time online compression method for vehicle trajectory based on smart phone sensors. Journal of Southwest Jiaotong University, 2022; 57(1): 1–10. (in Chinese)
[11] Hu S, Wu Z C, Zhang J. Driving behavior recognition based on data depth features of vehicle mobile phone. Computer Applications and Software, 2019; 36(1): 59–66, 87. (in Chinese)
[12] Yang J D, Cao Y C, Lin Q, Man Z X, Liu X S. Research on traffic status recognition based on mobile phone sensors. Journal of Northwest Minzu University (Natural Science), 2019; 40(4): 1–8. (in Chinese)
[13] Kou Z H. A method of agricultural machinery operation perception and behavior modeling based on smartphone sensors. Master’s dissertation. Beijing: China Agricultural University, 2018; 44p. (in Chinese)
[14] Kong Q H, Maimaiti T, Zhao M J. Recognition of tractor working condition based on convolutional neural network. Journal of Chinese Agricultural Mechanization, 2021; 42(11): 144–150. (in Chinese)
[15] Zou B, Shi Z X, Du S H. Gases emissions estimation and analysis by using carbon dioxide balance method in natural-ventilated dairy cow barns. Int J Agric & Biol Eng, 2020; 13(2): 41–47.
[16] GB/T 38146.2-2019. Vehicle driving conditions in China - Part II: Heavy commercial vehicles. (in Chines)
[17] Castano F, Rossi A, Sevaux M, Velasco N. A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints. Computers & Operations Research, 2014; 52(Part B): 220–230.
[18] Huang Z F, Xue J, Ming B, Wang K R, Xie R Z, Hou P, et al. Analysis of factors affecting the impurity rate of mechanically-harvested maize grain in China, Int J Agric & Biol Eng, 2020; 13(5): 17–22.
[19] Jia T L. AF-Verifying theorem deduced from two factors distribution. Journal of Southwest University of Science and Technology, 2004; 19(3): 89–91, 97. (in Chinese)
[20] Chen Y, Zhang X Q, Wu C C, Li G Y. Field-road trajectory segmentation for agricultural machinery based on direction distribution. Computers and Electronics in Agriculture, 2021; 186: 106180. doi: 10.1016/ j.compag.2021.106180.
[21] Li C Y. Analysis of technical requirements and implementation misunderstandings of maize mechanized sowing operations. Agricultural Machinery Using & Maintenance, 2021; 12: 26–27. (in Chinese)
[22] Ye Q P, Yu S C, Liu J L, Zhao Q X, Zhao Z. Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms. Ecological Indicators, 2021; 129: 107948. doi: 10.1016/j.ecolind.2021.107948.
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[28] Zhou L B. Internal combustion engine. Beijing: Machinery Industry Press, 1990; 352p. (in Chinese)
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Published
2022-09-04
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Yang, L., Tian, W., Zhai, W., Wang, X., Chen, Z., Wen, L., … Wu, C. (2022). Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone. International Journal of Agricultural and Biological Engineering, 15(4), 154–162. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7454
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Information Technology, Sensors and Control Systems
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