The significance of agriculture lies in its role in ensuring the sustenance of the human population through the production of essential resources such as food, feed, and fiber. Precision agriculture is employed to effectively administer appropriate treatments at the correct location and time in order to attain agricultural output that is characterized by low input, high efficiency, and long-term sustainability. The primary objective of precision agriculture is to enhance agricultural productivity while minimizing adverse environmental impacts. Precision agriculture, an agricultural approach that leverages advanced technologies such as robotics and automation, is predominantly employed to enhance the efficiency and precision of farm management practices. The utilization of mobile robots in agricultural activities, such as harvesting, spraying, inspection, and planting, has been extensively investigated and researched in the past few decades. This study investigates the rapid increase in the utilization of automation and robots in the agricultural sector over the past five years. In this study, we categorize the latest applications into four distinct groups, each representing a specific range of activities conducted during the entire process of planting management, starting from the initial sowing stage and concluding with the final harvest. In the final section of the paper, an analysis of various challenges and suggestions is provided to underscore potential opportunities and enhancements in the advancement of an effective robotic and autonomous system for agricultural purposes.
T. Mizik, “How can precision farming work on a small scale? A systematic literature review,” Precis. Agric., vol. 24, no. 1, pp. 384–406, 2023.
S. Shanthi, S. Saradha, J. A. Smitha, N. Prasath, and H. Anandakumar, “An efficient automatic brain tumor classification using optimized hybrid deep neural network,” International Journal of Intelligent Networks, vol. 3, pp. 188–196, 2022, doi: 10.1016/j.ijin.2022.11.003.
V. L. Narla, R. Kachhoria, M. Arun, A. Haldorai, D. Vijendra Babu, and B. M. Jos, “IoT based energy efficient multipath power control for underwater sensor network,” International Journal of System Assurance Engineering and Management, Apr. 2022, doi: 10.1007/s13198-021-01560-7.
K. Ashok, M. Ashraf, J. Thimmia Raja, M. Z. Hussain, D. K. Singh, and A. Haldorai, “Collaborative analysis of audio-visual speech synthesis with sensor measurements for regulating human–robot interaction,” International Journal of System Assurance Engineering and Management, Aug. 2022, doi: 10.1007/s13198-022-01709-y.
S. R and A. H, “Improved EPOA clustering protocol for lifetime longevity in wireless sensor network,” Sensors International, vol. 3, p. 100199, 2022, doi: 10.1016/j.sintl.2022.100199.
G. Tuncel and S. Topaloglu, “Assembly line balancing with positional constraints, task assignment restrictions and station paralleling: A case in an electronics company,” Comput. Ind. Eng., vol. 64, no. 2, pp. 602–609, 2013.
S. Kreuzfeld, C. Felsing, and R. Seibt, “Teachers’ working time as a risk factor for their mental health - findings from a cross-sectional study at German upper-level secondary schools,” BMC Public Health, vol. 22, no. 1, p. 307, 2022.
C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, “Integrating machine learning with human knowledge,” iScience, vol. 23, no. 11, p. 101656, 2020.
A. Terada, Y. Ando, and M. Mizukawa, “2A1-B13 autonomous agricultural robot with a seeding function : Self localization using Omni-directional vision,” Proc. JSME Annu. Conf. Robot. Mechatron. (Robomec), vol. 2006, no. 0, p. _2A1-B13_1-_2A1-B13_2, 2006.
J. Choi, Z. Ma, K. Kim, and H. Sohn, “Continuous structural displacement monitoring using accelerometer, vision, and infrared (IR) cameras,” Sensors (Basel), vol. 23, no. 11, 2023.
S. Munikoti, D. Agarwal, L. Das, M. Halappanavar, and B. Natarajan, “Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, 2023.
D. J. Mandal, M. Pedersen, S. George, H. Deborah, and C. Boust, “An experiment-based comparative analysis of pigment classification algorithms using hyperspectral imaging,” J. Imaging Sci. Technol., vol. 67, no. 3, pp. 030403-1-030403–18, 2023.
C. Alexander, “Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST),” Int. J. Appl. Earth Obs. Geoinf., vol. 86, no. 102013, p. 102013, 2020.
M. Pietroń, D. Żurek, and B. Śnieżyński, “Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction,” J. Comput. Sci., vol. 67, no. 101971, p. 101971, 2023.
Y. Yeboah, Department of Electrical and Computer Engineering, South China University of Technology, Y. Zhuliang, W. Wei, and School of Automation Technology, “Robust and persistent visual tracking-by-detection for robotic vision systems,” Int. J. Mach. Learn. Comput., vol. 6, no. 3, pp. 196–204, 2016.
K. Hayashi, “Exploring unexplored tensor network decompositions for convolutional neural networks,” Brain Neural Netw., vol. 29, no. 4, pp. 193–201, 2022.
S. Yin, H. Li, A. Laghari, S. Karim, and A. Jumani, “A bagging strategy-based kernel extreme learning machine for complex network intrusion detection,” ICST Trans. Scalable Inf. Syst., vol. 8, no. 33, p. 171247, 2021.
K. D. Gibson, R. Dirks, C. R. Medlin, and L. Johnston, “Detection of weed species in soybean using multispectral digital images,” Weed Technol., vol. 18, no. 3, pp. 742–749, 2004.
C. Guo, B. Wei, and K. Yu, “Deep transfer learning for biology cross-domain image classification,” J. Control Sci. Eng., vol. 2021, pp. 1–19, 2021.
E. Mohammed Abdelkader, “On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures,” Smart Sustain. Built Environ., vol. 11, no. 3, pp. 740–764, 2022.
X. Wang, J. Tu, and Y. Qiu, “A research on family flexible load scheduling based on improved non-dominated sorting genetic algorithm,” J. Build. Eng., vol. 71, no. 106541, p. 106541, 2023.
L. N. Truc, N. Van Quyen, and N. P. Quang, “Dynamic model with a new formulation of Coriolis/centrifugal matrix for robot manipulators,” J. Comput. Sci. Cybern., vol. 36, no. 1, pp. 89–104, 2020.
P. Grabusts, J. Musatovs, and V. Golenkov, “The application of simulated annealing method for optimal route detection between objects,” Procedia Comput. Sci., vol. 149, pp. 95–101, 2019.
T. Bak and H. Jakobsen, “Agricultural robotic platform with four wheel steering for weed detection,” Biosyst. Eng., vol. 87, no. 2, pp. 125–136, 2004.
C. Zou, L. Li, G. Cai, and R. Lin, “Fixed-point landing method for unmanned aerial vehicles using multi-sensor pattern detection,” Unmanned Syst., pp. 1–10, 2023.
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Cite this article
Ali-Кhusein and Urquhart, “Present and Future Applications of Robotics and Automations in Agriculture”, Journal of Robotics Spectrum, vol.1, pp. 047-056, 2023. doi: 10.53759/9852/JRS202301005.
