The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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.
Cited by
Sorted by citations
Lakhiar, I. A., Yan, H., Zhang, C., Wang, G., He, B., Hao, B., Han, Y., Wang, B., Bao, R., Syed, T. N., et al. (2024) · Agriculture · doi:10.3390/agriculture14071141 · Cited by 501
The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies
Miller, T. I., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025) · Sensors · doi:10.3390/s25123583 · Cited by 382
A Review of CNN Applications in Smart Agriculture Using Multimodal Data
Sakka, M. E., Ivanovici, M., Chaâri, L., & Mothe, J. (2025) · Sensors · doi:10.3390/s25020472 · Cited by 215
Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions
Balaska, V. E., Adamidou, Z., Vryzas, Z., & Gasteratos, A. C. (2023) · Machines · doi:10.3390/machines11080774 · Cited by 181
Taha, M. F., Mao, H., Zhang, Z., ElMasry, G. M., Awad, M. A., Abdalla, A., Mousa, S., Elwakeel, A. E., & Elsherbiny, O. (2025) · Agriculture · doi:10.3390/agriculture15060582 · Cited by 180
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review