The complex agricultural environment, together with the need for high levels of productivity, necessitates establishing robust systems that can be efficiently and economically developed. The absence of order and structure in the external environment heightens the probability of experiencing failures. Furthermore, it is often seen that equipment management is entrusted to those with little proficiency in technology. Therefore, the significance of intrinsic safety and reliability becomes a pivotal attribute. The issue of ensuring food safety requires using automated technologies that are both sterilized and reliable to minimize the risk of contamination leakage. This article examines the progress and prospects of automation in the agricultural sector, with a specific emphasis on the use of autonomous equipment, robotics, and artificial intelligence. The article examines the advantages of automation in enhancing the management of agricultural production, minimizing expenses, and achieving objectives related to environmental sustainability. Nevertheless, it is important to acknowledge the complexities associated with automation, as it brings to light several obstacles such as the repercussions on agricultural workers, possible disparities in social and environmental aspects, and the need for more investigation and advancement.
Keywords
Variable Rate Technology (VRT), Geographic Information System (GIS), Artificial Intelligence (AI), Global Navigation Satellite Systems (GNSS), Unmanned Aerial Vehicles (UAVs).
A. Rodríguez‐Pose and D. Hardy, “Addressing poverty and inequality in the rural economy from a global perspective,” Applied Geography, vol. 61, pp. 11–23, Jul. 2015, doi: 10.1016/j.apgeog.2015.02.005.
T. W. Kim, F. Maimone, K. Pattit, A. J. G. Sisón, and B. L. Teehankee, “Master and Slave: the Dialectic of Human-Artificial Intelligence Engagement,” Humanistic Management Journal, vol. 6, no. 3, pp. 355–371, Dec. 2021, doi: 10.1007/s41463-021-00118-w.
K. H. John and M. Tiegelkamp, IEC 61131-3: Programming Industrial Automation Systems. 2010. doi: 10.1007/978-3-642-12015-2.
C. Driessen and L. F. M. Heutinck, “Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms,” Agriculture and Human Values, vol. 32, no. 1, pp. 3–20, Jun. 2014, doi: 10.1007/s10460-014-9515-5.
T. A. Shaikh, T. Rasool, and F. R. Lone, “Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,” Computers and Electronics in Agriculture, vol. 198, p. 107119, Jul. 2022, doi: 10.1016/j.compag.2022.107119.
K. E. Giller et al., “The future of farming: Who will produce our food?,” Food Security, vol. 13, no. 5, pp. 1073–1099, Sep. 2021, doi: 10.1007/s12571-021-01184-6.
A. Bechar and C. Vigneault, “Agricultural robots for field operations: Concepts and components,” Biosystems Engineering, vol. 149, pp. 94–111, Sep. 2016, doi: 10.1016/j.biosystemseng.2016.06.014.
C. Johnson et al., “The Bio-Based Industries Joint Undertaking as a catalyst for a green transition in Europe under the European Green Deal,” EFB Bioeconomy Journal, vol. 1, p. 100014, Nov. 2021, doi: 10.1016/j.bioeco.2021.100014.
E. Vinco, N. Morrison, J. Bourassa, and G. Lhermie, “Climate policy and Canadian crop production: A qualitative study of farmers’ attitudes and perceptions towards nitrous oxide reductions,” Journal of Cleaner Production, vol. 418, p. 138108, Sep. 2023, doi: 10.1016/j.jclepro.2023.138108.
A. Bechar and C. Vigneault, “Agricultural robots for field operations. Part 2: Operations and systems,” Biosystems Engineering, vol. 153, pp. 110–128, Jan. 2017, doi: 10.1016/j.biosystemseng.2016.11.004.
M. Stefanini et al., “Effects of optical sensing based variable rate nitrogen management on yields, nitrogen use and profitability for cotton,” Precision Agriculture, vol. 20, no. 3, pp. 591–610, Sep. 2018, doi: 10.1007/s11119-018-9599-9.
S. Fabiani et al., “Assessment of the economic and environmental sustainability of Variable Rate Technology (VRT) application in different wheat intensive European agricultural areas. A Water energy food nexus approach,” Environmental Science & Policy, vol. 114, pp. 366–376, Dec. 2020, doi: 10.1016/j.envsci.2020.08.019.
J. Lowenberg‐DeBoer, K. Franklin, K. Behrendt, and R. J. Godwin, “Economics of autonomous equipment for arable farms,” Precision Agriculture, vol. 22, no. 6, pp. 1992–2006, May 2021, doi: 10.1007/s11119-021-09822-x.
D. C. Slaughter, D. K. Giles, and D. Downey, “Autonomous robotic weed control systems: A review,” Computers and Electronics in Agriculture, vol. 61, no. 1, pp. 63–78, Apr. 2008, doi: 10.1016/j.compag.2007.05.008.
B. Darwin, P. Dharmaraj, S. Prince, D. Popescu, and D. J. Hemanth, “Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A review,” Agronomy, vol. 11, no. 4, p. 646, Mar. 2021, doi: 10.3390/agronomy11040646.
H. Mousazadeh, “A technical review on navigation systems of agricultural autonomous off-road vehicles,” Journal of Terramechanics, vol. 50, no. 3, pp. 211–232, Jun. 2013, doi: 10.1016/j.jterra.2013.03.004.
S. R. Edulakanti and S. Ganguly, “Review article: The emerging drone technology and the advancement of the Indian drone business industry,” The Journal of High Technology Management Research, vol. 34, no. 2, p. 100464, Nov. 2023, doi: 10.1016/j.hitech.2023.100464.
S. Cubero, E. Marco‐Noales, N. Aleixos, S. Barbé, and J. Blasco, “RobHortic: a field robot to detect pests and diseases in horticultural crops by proximal sensing,” Agriculture, vol. 10, no. 7, p. 276, Jul. 2020, doi: 10.3390/agriculture10070276.
K. Sangaiah, A. Javadpour, C.-C. Hsu, A. Haldorai, and A. Zeynivand, “Investigating Routing in the VANET Network: Review and Classification of Approaches,” Algorithms, vol. 16, no. 8, p. 381, Aug. 2023, doi: 10.3390/a16080381.
M. S. A. Mahmud, M. S. Z. Abidin, A. A. Emmanuel, and H. S. Hasan, “Robotics and Automation in agriculture: Present and future applications,” DOAJ (DOAJ: Directory of Open Access Journals), Apr. 2020, [Online]. Available: https://doaj.org/article/69ed706740c0400bba3bd20df0e69871
Y. Kuo, “Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem,” Computers & Industrial Engineering, vol. 59, no. 1, pp. 157–165, Aug. 2010, doi: 10.1016/j.cie.2010.03.012.
S. Ayub, N. Singh, Md. Z. Hussain, M. Ashraf, D. K. Singh, and A. Haldorai, “Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies,” Computational Intelligence, vol. 39, no. 4, pp. 592–606, Sep. 2022, doi: 10.1111/coin.12547.
S. Dogru and L. Marques, “Evaluation of an automotive short range radar sensor for mapping in orchards,” 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Apr. 2018, doi: 10.1109/icarsc.2018.8374164.
“SWEEPER, the sweet pepper harvesting robot,” WUR. https://www.wur.nl/en/project/sweeper-the-sweet-pepper-harvesting-robot.htm
B. Arad et al., “Development of a sweet pepper harvesting robot,” Journal of Field Robotics, vol. 37, no. 6, pp. 1027–1039, Jan. 2020, doi: 10.1002/rob.21937.
G. Kootstra, X. Wang, P. M. Blok, J. Hemming, and E. Van Henten, “Selective Harvesting Robotics: current research, trends, and future directions,” Current Robotics Reports, vol. 2, no. 1, pp. 95–104, Jan. 2021, doi: 10.1007/s43154-020-00034-1.
S. V. Ilyukhin, T. A. Haley, and R. K. Singh, “A survey of automation practices in the food industry,” Food Control, vol. 12, no. 5, pp. 285–296, Jul. 2001, doi: 10.1016/s0956-7135(01)00015-9.
M. Colla, T. Leidi, and M. Semo, “Design and implementation of industrial automation control systems: A survey,” 2009 7th IEEE International Conference on Industrial Informatics, Jun. 2009, doi: 10.1109/indin.2009.5195866.
G. Barrett, M. I. Caniggia, and L. Read, “‘There are More Vets than Doctors in Chiloé’: Social and Community Impact of the Globalization of Aquaculture in Chile,” World Development, vol. 30, no. 11, pp. 1951–1965, Nov. 2002, doi: 10.1016/s0305-750x(02)00112-2.
D. Bechtsis, N. Tsolakis, D. Vlachos, and E. Iakovou, “Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles,” Journal of Cleaner Production, vol. 142, pp. 3970–3984, Jan. 2017, doi: 10.1016/j.jclepro.2016.10.057.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Sophie Emilie
Sophie Emilie
Computer Science and Quantum Information, Sorbonne University, Jussieu, Paris, France.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Marie Pasteur and Sophie Emilie, “Revolutionizing Agriculture: The Impact of Automation on Productivity and Efficiency”, Journal of Robotics Spectrum, vol.2, pp. 023-033, 2024. doi: 10.53759/9852/JRS202402003.