Instrumentation applied in agricultural machines: systematic literature review
Keywords:Agriculture; Precision agriculture; Agricultural instrumentation; Agricultural machinery.
In order to analyze the publications on the use of instrumentation in agriculture, the objective of this paper is to present a set of works published between 2017 and 2021 on the subject so that an analysis of the technologies developed during this period can be carried out. For this, a search was carried out in the IEEE, Science Direct and Scopus databases, where 1490 published articles were found using a search string to select papers considering theme, year of publication. In view of this result, the Start software was used to apply selection criteria to choose the articles to be used in the review. After performing all the steps of selection of works in the software, the result was 33 papers carrying out the Systematic Review. Of the 33 articles, the work methods and the result obtained by the author are presented, thus enabling an analysis of the technologies researched during the study period.
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Dusadeerungsikul, P. O. & NOF, S. Y. (2019). A collaborative control protocol for agricultural robot routing with online adaptation, Computers Industrial Engineering, 135, 456-466.
Espejo, G. B. Martinez, G. J. Pérez, R. M. Lopez, P. F.J. & Javier, Z. F. (2018). Machine learning for automatic rule classification of agricultural regulations: a case study in Spain. Comput Electron Agric. 150: 343–352.
Gupta, C. Tewari, V.K. Kumar, A. A. & Shrivastava, P. (2019). Automatic tractor slip-draft embedded control system, Computers and Electronics in Agriculture, 165.
Han, X. Kim, H. Jeon, C. W. Moon, H. C. Kim, J. H. & Yi, S. Y. (2019). Application of a 3D tractor-driving simulator for slip estimation-based path-tracking control of auto-guided tillage operation, Biosystems Engineering, 178, 70-85.
Inoue, K. Kaizu, Y. Igarashi, S. & Imou, K. (2019). The development of autonomous navigation and obstacle avoidance for a robotic mower using machine vision technique, IFAC-PapersOnLine, 52(30), 173-177.
İrsel, G. M.; & Altinbalik, T. (2018). Adaptation of tilt adjustment and tracking force automation system on a laser-controlled land leveling machine, Computers and Electronics in Agriculture, 150, 374-386.
Kim, J. Kim, S. Ju, C. & Son, H. I. (2019). Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications, IEEE Access, 7, 105100-105115.
Kim, Y Chung, S., & Choi, C.H. (2018). Development of automation technology for manual transmission of a 50 HP autonomous tractor. IFAC-PapersOnLine. 51. 20-22.
Kumar, A. A. Tewari, V.K. Nare, B. Chetan, C.R. Srivastava, P. & Kumar, S. P. (2017). Embedded Digital Drive Wheel Torque Indicator For Agricultural 2WD Tractors. Computers and electronics in agriculture, 139, 91-102.
Lee, J. Kim, H. Cho, B Choi, J., & Kim, Y. (2018). Road Bump Detection Using LiDAR sensor for Semi-Active Control of Front Axle Suspension in an Agricultural Tractor. IFAC-PapersOnLine. 51, 124-129.
Li, S. Xu, H. Ji, Y. Cao, R Zhang, M. & Li, H. (2019). Development of a following agricultural machinery automatic navigation system, Comput. Electron. Agricult., 158, 335-344.
Lu, W. Wei, Y. Yuan, J. Deng, Y. & Song, A. (2020). Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm, IEEE Access, 8, 163269-163279.
Mccool, C. S. Perez, T. & Upcroft, B. (2017). Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics, IEEE Robotics and Automation Letters. 2, 1344-1351.
Mattetti, M. Molari, G. & Sereni, E. (2017). Damage Evaluation Of Driving Events For Agricultural Tractors. Computers and Electronics in Agriculture, 135, 328–337.
Mattetti, M. Maraldi, M. Lenzini, N. Fiorati, S. Sereni. E. & Molari, G. (2021). Outlining the mission profile of agricultural tractors through CAN-BUS data analytics, Computers and Electronics in Agriculture, 184, 106078.
Mattetti, M Maraldi, M Sedoni, E., & Molari, G. (2019). Optimal criteria for durability test of stepped transmissions of agricultural tractors, Biosystems Engineering, 178, 145-155.
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Pasternak, G. Greenman, J. & Ieropoulos, I. (2017). Self-Powered, Autonomous Biological Oxygen Demand Biosensor For Online Water Quality Monitoring. Sensors Actuators B Chemical. 244, 815-822.
Perz, R. & Wronowski, K. (2018). UAV application for precision agriculture, Aircraft Engineering and Aerospace Technology, 91, 257-263.
Rajabi-Vandechali, M. Abbaspour-Fard, M. & Rohani, A. (2018). Development of a prediction model for estimating tractor engine torque based on soft computing and low cost sensors. Measurement. 121, 83-95.
Rajkumar, S. Arun M. Hirwani, J. & Sanjeev, S. (2018). Predictive analysis of crops cultivation for a smart green environment using azure services. International Journal of Recent Technology and Engineering (IJRTE). 7, 295-298.
Raikwar, S. Wani, L. J. Kumar, S. A. & Rao, M. S. (2019). Hardware-in-the-Loop test automation of embedded systems for agricultural tractors, Measurement, 133, 271-280.
Ranjbarian, S.; Askari, M.; & Jannatkhah, J. (2017) Performance of tractor and tillage implements in clay soil. Journal of the Saudi Society of Agricultural Sciences, 16(2), 154-162.
Shafaei, S.M. Loghavi, M. & Kamgar, S. (2019). A practical effort to equip tractor-implement with fuzzy depth and draft control system, Engineering in Agriculture, Environment and Food, 12(2), 191-203.
Shafaei, S.M. Loghavi, M. & Kamgar, S. (2020). Benchmark of an intelligent fuzzy calculator for admissible estimation of drawbar pull supplied by mechanical front wheel drive tractor, Artificial Intelligence in Agriculture, 4, 209-218.
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Spykman, O. Gabriel, A. Ptacek, M. & Gandorfer, M. (2021). Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany, Computers and Electronics in Agriculture, 186, 106176.
Sulistyo, S.B. Wu, D. Woo, W.L. Dlay, S. S. & Gao, B. (2017). Computational Deep Intelligence Vision Sensing For Nutrient Content Estimation In Agricultural Automation. IEEE Transactions on Automation Science and Engineering, 15, 1243-1257.
Tufail, M. Iqbal, J. Tiwana, M. I. Alam, M. S. Khan, Z. A. & Khan, M. T. (2021) Identification of Tobacco Crop Based on Machine Learning for a Precision Agricultural Sprayer, IEEE Access, 9, 23814-23825.
Yalei, W. Lijun, Q. & Hao, Z. (2018). Design and experiment of remote intelligent spray control system based on embedded internet. Trans. Chin. Soc. Agric. Eng. 34(20), 28–35.
Yang, Q. Huang, G. Shi, X. He, M. Ahmad, I. Zhao, X. & Addy, M. (2020) Design of a control system for a mini-automatic transplanting machine of plug seedling, Computers and Electronics in Agriculture, 169.
Zhang, S. Wang, Y. Zhu, Z. Li, Z. Du, Y. & Mao, E. (2018). Tractor Path Tracking Control Based on Binocular Vision. Information Processing in Agriculture, 5, 422-432.
Zhao, H. Zhou, S. Chen, W. Miao, Z. & Liu, Y. H. (2021). Modeling and Motion Control of Industrial Tractor–Trailers Vehicles Using Force Compensation, IEEE/ASME Transactions on Mechatronics, 26(2), 645-656.
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Copyright (c) 2021 Thiago Santana Aranha; Mario Mollo Neto; Mariana Matulovic da Silva Rodrigueiro; Flávio José de Oliveira Morais; Paulo Sérgio Barbosa dos Santos
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