Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2023, 6(9); doi: 10.25236/AJETS.2023.060910.

Application of Mechanical Electronic Engineering in Agricultural Machinery Integration Control Engineering


Shaomin Lu, Yiqing Huang, Siyu Hou, Wei Li

Corresponding Author:
Shaomin Lu

College of Mechanical and Transportation, Southwest Forestry University, Kunming, 650000, Yunnan, China


In recent years, with the rapid development of new information technology, western countries have begun to implement precision agriculture, combining agricultural production with information technology, and agricultural informatization has entered a new era of development. China has also introduced a set of policies to promote the development of agricultural production and agricultural machinery service management in the direction of intelligence, digitization, and scientificity. This article explores the construction of an intelligent agricultural machinery comprehensive service system based on the 3S architecture. This article aims to study the establishment of a smart agricultural machinery integrated service system based on the 3S framework. The experimental results show that when the concurrency is 100, the average response time is 236ms, the maximum response time is 698ms, and the minimum response time is 178ms. Therefore, this system has improved the agricultural machinery information service management and decision support system, and strengthened the macro regulation and micro guidance of modern agricultural production.


Control Engineering, Agricultural Machinery Integration, Mechanical and Electronic Engineering, 3S Integration Framework

Cite This Paper

Shaomin Lu, Yiqing Huang, Siyu Hou, Wei Li. Application of Mechanical Electronic Engineering in Agricultural Machinery Integration Control Engineering. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 9: 65-72. https://doi.org/10.25236/AJETS.2023.060910.


[1] Sun Wei. "Design of condenser air filter integrated device for self-cleaning agricultural air conditioning." Journal of Engineering Mechanics and Machinery 6.1 (2021): 94-97.

[2] Pimonratanakan Sudarat. "The causal factors that influence the organization performance of the agricultural machinery industry." AgBioForum 24.1 (2022): 72-82.

[3] Abuselidze George, and Anna Slobodianyk. "Marketing Aspects of the Key Issues of Agricultural Machinery in the Industrial Enterprises." Journal of Optimization in Industrial Engineering 15.1 (2022): 311-320.

[4] Tian Hongkun. "Computer vision technology in agricultural automation—A review." Information Processing in Agriculture 7.1 (2020): 1-19.

[5] Tripathi Mukesh Kumar, and Dhananjay D. Maktedar. "A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey." Information Processing in Agriculture 7.2 (2020): 183-203.

[6] Bolandnazar, Elham, Abbas Rohani, and Morteza Taki. "Energy consumption forecasting in agriculture by artificial intelligence and mathematical models." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42.13 (2020): 1618-1632.

[7] Rotz Sarah. "The politics of digital agricultural technologies: a preliminary review." Sociologia ruralis 59.2 (2019): 203-229.

[8] Sambasivam G. Opiyo GD. "A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks." Egyptian informatics journal 22.1 (2021): 27-34.

[9] Suchithra M. S., and Maya L. Pai. "Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters." Information processing in Agriculture 7.1 (2020): 72-82.

[10] Ma Ling, Mohammad Ikbal, and Korhan Cengiz. "Realization of agricultural machinery equipment management information system based on network." International Journal of Agricultural and Environmental Information Systems (IJAEIS) 12.3 (2021): 13-25.

[11] Alimova Z. "Ways to improve the performance of hydraulic oils for agricultural machinery." Industrial Technology and Engineering 3.36 (2020): 17-22.

[12] Sobirjonov Abutolib, et al. "Prevention of corrosion and accelerated wear of agricultural machinery." Ilkogretim Online-Elementary Education Online 20.5 (2021): 7482-7486.

[13] Han Jialin. "A multi-objective districting problem applied to agricultural machinery maintenance service network." European Journal of Operational Research 287.3 (2020): 1120-1130.

[14] Romek Dawid. "The impact of padding weld shape of agricultural machinery tools on their abrasive wear." Tribologia 290.2 (2020): 55-62.

[15] Pajurek Marek, Szczepan Mikolajczyk, and Malgorzata Warenik-Bany. "Engine oil from agricultural machinery as a source of PCDD/Fs and PCBs in free-range hens." Environmental Science and Pollution Research 30.11 (2023): 29834-29843.

[16] Muller Malte. "Leadership in agricultural machinery circles: experimental evidence from Tajikistan." Australian Journal of Agricultural and Resource Economics 64.2 (2020): 533-554.

[17] Liu Ye. "Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm." Tehnički vjesnik 29.2 (2022): 456-463.

[18] Hui Xianghui. "Trend prediction of agricultural machinery power in china coastal areas based on grey relational analysis." Journal of Coastal Research 103.SI (2020): 299-304.

[19] Li Huaxia. "Application of a multi-disciplinary design approach in a mechatronic engineering toolchain." at-Automatisierungstechnik 67.3 (2019): 246-269.

[20] Luque-Vega, Luis F. "Educational methodology based on active learning for mechatronics engineering students: towards educational mechatronics." Computacióny Sistemas 23.2 (2019): 325-333.

[21] Frey Cheolgi. Internal Combustion Engine in Agricultural Machinery Field Relying on Artificial Fish Swarm Algorithm. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 1: 18-27. 

[22] Wu J., Tao R., Zhao P., Martin N. F., & Hovakimyan N. (2022). Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1712-1720).