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Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector
Open accessAbstract
The farmers’ welfare and its interlinkages to energy efficiency and farm sustainability has attracted global scientific interest within the last few decades. This study examines the contribution of Agriculture 5.0 to the prosperity of the farmers in the post-pandemic era and the gradual transition to an energy-smart farm. To obtain an insight into the attributes of Agriculture 5.0 and the emerging technologies in the field, Bibliometrix analysis with the use of an R package was conducted based on 2000 data consisting of peer-reviewed articles. The data were retrieved from the Scopus database. A bibliometric approach was employed to analyze the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of Agriculture 5.0 and energy-smart farming. Emerging technologies that are part of Agriculture 5.0 in combination with alternative energy sources can provide cost-effective access to finance, weather updates, remotely monitoring, and future energy solutions for the establishment of smart farms. Keywords such as “renewable energy,” “Internet of Things,” and “emission control” remain the trending keywords. Moreover, thematic analysis shows that “economic and social effects”, “energy efficiency”, “remote sensing”, and “Artificial Intelligence” with their associated components such as “anaerobic digestion”, “wireless sensor network,” “agricultural robots”, and “smart agriculture” are the niche themes of Agriculture 5.0 in combination with green energy sources, which can lead to the cut cost, energy-efficient, and sustainable energy-smart farms.
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Cites
bibliometrix: An R-tool for comprehensive science mapping analysis
Aria, M., & Cuccurullo, C. (2017) · Journal of Informetrics · doi:10.1016/j.joi.2017.08.007 · Cited by 16,064
Bwambale, E., Abagale, F. K., & Anornu, G. K. (2021) · Agricultural Water Management · doi:10.1016/j.agwat.2021.107324 · Cited by 715
Review the state-of-the-art technologies of semantic segmentation based on deep learning
Mo, Y., Wu, Y. X., Yang, X., Liu, F., & Liao, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.005 · Cited by 669
Digital twins in smart farming
Verdouw, C., Tekinerdoğan, B., Beulens, A., & Wolfert, S. (2021) · Agricultural Systems · doi:10.1016/j.agsy.2020.103046 · Cited by 634
Smart farming for improving agricultural management
Mohamed, E. S., Belal, A., Abd‐Elmabod, S. K., El-Shirbeny, M. A., Gad, A., & Zahran, M. (2021) · The Egyptian Journal of Remote Sensing and Space Science · doi:10.1016/j.ejrs.2021.08.007 · Cited by 495
Xie, L., Chen, Z., Wang, H., Zheng, C., & Jiang, J. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.01.171 · Cited by 284
How digital marketing evolved over time: A bibliometric analysis on scopus database
Faruk, M., Rahman, M., & Hasan, S. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e08603 · Cited by 214
Niknejad, N., Ismail, W., Bahari, M., Hendradi, R., & Salleh, A. Z. (2020) · Environmental Technology & Innovation · doi:10.1016/j.eti.2020.101272 · Cited by 209
A bibliometric analysis of sustainable oil and gas production research using VOSviewer
Tamala, J. K., Maramag, E. I., Simeon, K. A., & Ignacio, J. J. (2022) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2022.100437 · Cited by 206
Making sense in the cloud: Farm advisory services in a smart farming future
Eastwood, C., Ayre, M. L., Nettle, R. A., & Rue, B. D. (2019) · NJAS - Wageningen Journal of Life Sciences · doi:10.1016/j.njas.2019.04.004 · Cited by 183
Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., & Zhang, X. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127712 · Cited by 181
Lamprinopoulou, C., Renwick, A., Klerkx, L., Hermans, F., & Roep, D. (2014) · Agricultural Systems · doi:10.1016/j.agsy.2014.05.001 · Cited by 173
Research progress, trends, and updates on anaerobic digestion technology: A bibliometric analysis
Ampese, L. C., Sganzerla, W. G., Ziero, H. D. D., Mudhoo, A., Martins, G., & Forster‐Carneiro, T. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.130004 · Cited by 173
A survey on smart farming data, applications and techniques
Alwis, S. D., Hou, Z., Zhang, Y., Na, M. H., Ofoghi, B., & Sajjanhar, A. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103624 · Cited by 158
A systematic literature review on deep learning applications for precision cattle farming
Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M., & Khan, M. U. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106313 · Cited by 149
Agricultural productivity and the sectoral reallocation of labor in rural India
Emerick, K. (2018) · Journal of Development Economics · doi:10.1016/j.jdeveco.2018.08.013 · Cited by 146
Ramachandran, K. K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.11.544 · Cited by 144
AI applications of data sharing in agriculture 4.0: A framework for role-based data access control
Spanaki, K., Karafili, E., & Despoudi, S. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102350 · Cited by 134
Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil
Carrer, M. J., Filho, H. M. D. S., Vinholis, M. D. M. B., & Mozambani, C. I. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121510 · Cited by 117
Deep learning-based spacecraft relative navigation methods: A survey
Song, J., Rondao, D., & Aouf, N. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2021.10.025 · Cited by 106
Agriculture 5.0: Reconciling Production with Planetary Health
Fraser, E., & Campbell, M. (2019) · One Earth · doi:10.1016/j.oneear.2019.10.022 · Cited by 105
Deep learning based assistive technology on audio visual speech recognition for hearing impaired
Kumar, L. A., Renuka, D. K., Rose, S. L., Chinnan, S. P. M., & Wartana, I. M. (2022) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2022.01.003 · Cited by 100
India's COVID-19 social assistance package and its impact on the agriculture sector
Varshney, D., Kumar, A., Mishra, A. K., Rashid, S., & Joshi, P. K. (2021) · Agricultural Systems · doi:10.1016/j.agsy.2021.103049 · Cited by 99
Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks
Cordeiro, M., Markert, C. B., Araújo, S. S., Campos, N. G. S., Gondim, R. S., da Silva, T. L. C., & Rocha, A. R. D. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.013 · Cited by 94
‘You can't eat data’?: Moving beyond the misconfigured innovations of smart farming
Fraser, A. (2021) · Journal of Rural Studies · doi:10.1016/j.jrurstud.2021.06.010 · Cited by 86
Sustainability-oriented research and innovation in ‘farm to fork’ value chains
Riccaboni, A., Neri, E., Trovarelli, F., & Pulselli, R. M. (2021) · Current Opinion in Food Science · doi:10.1016/j.cofs.2021.04.006 · Cited by 81
Photovoltaic Agricultural Internet of Things Towards Realizing the Next Generation of Smart Farming
Huang, K., Shu, L., Li, K., Yang, F., Han, G., Wang, X., & Pearson, S. (2020) · IEEE Access · doi:10.1109/access.2020.2988663 · Cited by 79
IoT-Agro: A smart farming system to Colombian coffee farms
Rodríguez, J. P., Montoya‐Munoz, A. I., Rodriguez-Pabon, C., Hoyos, J., & Corrales, J. C. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106442 · Cited by 73
Van, J. C. F., Tham, P. E., Lim, H. R., Khoo, K. S., Chang, J., & Show, P. L. (2022) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2022.104235 · Cited by 65
Digital from farm to fork: Infrastructures of quality and control in food supply chains
Donaldson, D. A. (2021) · Journal of Rural Studies · doi:10.1016/j.jrurstud.2021.10.004 · Cited by 63
Gavurová, B., Келемен, М., & Polishchuk, V. (2022) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2022.101253 · Cited by 60
Sustainable renewable energy planning and wind farming optimization from a biodiversity perspective
Dhunny, A. Z., Allam, Z., Lobine, D., & Lollchund, M. R. (2019) · Energy · doi:10.1016/j.energy.2019.07.147 · Cited by 53
You, H. (2016) · Land Use Policy · doi:10.1016/j.landusepol.2016.11.025 · Cited by 46
Billing, S., Charalambides, G., Tett, P., Giordano, M., Ruzzo, C., Arena, F., Santoro, A., Lagasco, F., Brizzi, G., & Collu, M. (2021) · Energy Research & Social Science · doi:10.1016/j.erss.2021.102421 · Cited by 43
Integrated environmental assessment of energy crops for biofuel and energy production in Greece
Fontaras, G., Skoulou, V. K., Zanakis, G., Zabaniotou, A. A., & Samaras, Z. C. (2012) · Renewable Energy · doi:10.1016/j.renene.2011.12.010 · Cited by 41
Impact of artificial intelligence on civilization: Future perspectives
Rajendra, P., Kumari, M., Rani, S., Dogra, N., Boadh, R., Kumar, A., & Dahiya, M. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.01.113 · Cited by 40
Cortignani, R., Buttinelli, R., & Dono, G. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152259 · Cited by 39
Balancing biodiversity and agriculture: Conservation scenarios for the Dutch dairy sector
Kok, A., Oostvogels, V. J., de Olde, E. M., & Ripoll‐Bosch, R. (2020) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2020.107103 · Cited by 38
Smart agriculture using renewable energy and AI-powered IoT
Doshi, M., & Varghese, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823694-9.00028-1 · Cited by 37
Behera, B. S., Panda, B., Behera, R. A., Nayak, N., Behera, A. C., & Jena, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.04.148 · Cited by 31
Emergent urban agricultural practices and attitudes in the residential area in China
Yuan, W., & Jones, P. R. (2022) · Urban forestry & urban greening · doi:10.1016/j.ufug.2022.127491 · Cited by 28
Selim, T., & Eltarabily, M. G. (2021) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2021.11.018 · Cited by 26
The digital asset value and currency supervision under deep learning and blockchain technology
Fan, H. (2022) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2021.114061 · Cited by 25
Sinha, A., Priyadarshi, P., Bhushan, M., & Debbarma, D. (2021) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2021.11.002 · Cited by 24
Pisanó, I., Gottumukkala, L. D., Hayes, D. J. M., & Leahy, J. J. (2021) · Industrial Crops and Products · doi:10.1016/j.indcrop.2021.113857 · Cited by 23
Implementation of the PaperRank and AuthorRank indices in the Scopus database
Amodio, P., Luigi Brugnano, A., & Scarselli, F. (2021) · Journal of Informetrics · doi:10.1016/j.joi.2021.101206 · Cited by 21
Machine learning and control theory
Bensoussan, A., Li, Y., Nguyen, D. P. C., Tran, M.-B., Yam, S. C. P., & Zhou, X. (2022) · Handbook of numerical analysis · doi:10.1016/bs.hna.2021.12.016 · Cited by 17
Smart farming in pig production and greenhouse horticulture : An inventory in the Netherlands
Hoste, R., Suh, H. K., & Kortstee, H. J. M. (2017) · doi:10.18174/425037 · Cited by 17
Manlove, J. L., Shew, A. M., & Obembe, O. S. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106432 · Cited by 15
Business Strategies in HR in Times of Crisis: The Case of Agri-Food Industry in Central Greece
Ragazou, K. (2021) · Businesses · doi:10.3390/businesses1010004 · Cited by 12
Self-citationFeasibility of using different renewable energy sources in Brazilian swine farmings
de Oliveira, A. C. L., Fernandes, O. R., Renato, N. D. S., & Borges, A. C. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.130040 · Cited by 12
Engels, H., & Jonker, J. B. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819817-9.00019-3 · Cited by 4
Research works printed but not linked: 2
- Majid, A.A.M., Alshaibi, A.J., Kostyuchenko, E., and Shelupanov, A. (Mater. Today Proc., 2021). A review of artificial intelligence based malware detection using deep learning, Mater. Today Proc., epub ahead of print.
- Shrivastava, A., Nayak, C.K., Dilip, R., Samal, S.R., Rout, S., and Ashfaque, S.M. (Mater. Today Proc., 2021). Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis, Mater. Today Proc., epub ahead of print.
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Miller, T. I., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025) · Sensors · doi:10.3390/s25123583 · Cited by 382
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Balaska, V. E., Adamidou, Z., Vryzas, Z., & Gasteratos, A. C. (2023) · Machines · doi:10.3390/machines11080774 · Cited by 181
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Metadata
- Authors
- Ragazou, K., Garefalakis, A., Zafeiriou, E., & Passas, I.
- Affiliations
- Hellenic Mediterranean University (GR)
- Democritus University of Thrace (GR)
- Corresponding author
- Garefalakis, Alexandros
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2022-04-24
- Volume
- 15
- Issue
- 9
- Pages
- 3113
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W4224313986
- Cited by
- 125
- References
- 53
- Citations by year
- 2026: 20 · 2025: 34 · 2024: 44 · 2023: 19 · 2022: 8
- FWCI
- 14.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, Agriculture Analysis
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digitalization and Economic Development in Agriculture
- Keywords
- Agriculture 5.0, energy efficiency in agriculture, smart farm, cost reduction, farm sustainability, farmer welfare, Internet of Things, renewable energy, remote sensing, artificial intelligence in agriculture, anaerobic digestion, wireless sensor networks, agricultural robotics, emission control
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