The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Precision Agriculture for Crop and Livestock Farming—Brief Review
Open accessAbstract
In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.
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
Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022) · Internet of Things · doi:10.1016/j.iot.2020.100187 · Cited by 885
Computer vision technology in agricultural automation —A review
Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020) · Information Processing in Agriculture · doi:10.1016/j.inpa.2019.09.006 · Cited by 684
An IoT based smart irrigation management system using Machine learning and open source technologies
Goap, A., Sharma, D., Shukla, A. K., & Rama Krishna, C. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.09.040 · Cited by 676
General introduction to precision livestock farming
Berckmans, D. (2017) · Animal Frontiers · doi:10.2527/af.2017.0102 · Cited by 565
BERCKMANS, D. (2014) · Revue Scientifique et Technique de l'OIE · doi:10.20506/rst.33.1.2273 · Cited by 431
Remote sensing applications for precision agriculture: A learning community approach
Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003) · Remote Sensing of Environment · doi:10.1016/j.rse.2003.04.007 · Cited by 428
Martiskainen, P., Järvinen, M., Skön, J.-P., Tiirikainen, J., Kolehmainen, M., & Mononen, J. (2009) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2009.03.005 · Cited by 403
The role of sensors, big data and machine learning in modern animal farming
Neethirajan, S. (2020) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2020.100367 · Cited by 375
Lovarelli, D., Bacenetti, J., & Guarino, M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.121409 · Cited by 304
Controlled release micronutrient fertilizers for precision agriculture – A review
Mikula, K., Izydorczyk, G., Skrzypczak, D., Mironiuk, M., Moustakas, K., Witek-Krowiak, A., & Chojnacka, K. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.136365 · Cited by 299
Cited by
Sustainable farming practices and soil health: a pathway to achieving SDGs and future prospects
Sharma, P., Sharma, P., & Thakur, N. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00447-4 · Cited by 128
Patil, N. D., Kumar, A., Bains, A., Fareed, M., Dhull, S. B., Nagraik, R., & Chawla, P. (2025) · Food Chemistry: X · doi:10.1016/j.fochx.2025.102608 · Cited by 15
Retracted
Metadata
- Authors
- Monteiro, A., Santos, S., & Gonçalves, P.
- Venue
- Animals
- Publisher
- MDPI AG
- Published
- 2021-08-09
- Volume
- 11
- Issue
- 8
- Pages
- 2345
- ISSN
- 2076-2615 (ISSN-L)
- OpenAlex
- W3192862018
- Cited by
- 396
- References
- 40
- FWCI
- 23.54
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Precision Agriculture for Crop and Livestock Farming—Brief Review. https://app.cochat.ai/papers/10.3390/ani11082345
- The paper
- Monteiro, A., Santos, S., & Gonçalves, P. (2021). Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals. https://doi.org/10.3390/ani11082345
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.