The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Internet of Things and smart sensors in agriculture: Scopes and challenges
Open accessAbstract
Agriculture is an essential sector needed for survival of the human community. Several measures have been taken to enhance the crop production. However harsh environmental conditions and frequent pest infestation lead to the agricultural loss. In such scenario, integration of advanced technologies such as advanced sensors coupled with internet of things could escalate the agricultural production and minimize the economic loss. Studies have been conducted across the world that satisfactorily demonstrated the implication of integrated IoT-smart sensors in monitoring environmental factors such as moisture, humidity, temperature and soil composition that are critical for crop growth. Green house gases such as carbon dioxide, methane etc are also measured through automated sensors. Smart farming also enables measurement of nitrogen contents in soil that helps farmers to determine the amount of fertilizers to be used in farm lands. Some IoT enabled equipments and UAVs are useful in accurate surveillance of pest attack and associated diseases in farm vegetation. Though the smart farming has great scopes in future, it faces certain limitations related to implementation cost, data security, and lack of sufficient digital knowledge in farmers. Special economic policies, data encryption and digital literacy could ease IoT enabled smart farming in future.
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Cites
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Metadata
- Authors
- Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S.
- Venue
- Journal of Agriculture and Food Research
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 14
- Pages
- 100776
- ISSN
- 2666-1543 (ISSN-L)
- OpenAlex
- W4386495749
- Cited by
- 355
- References
- 63
- FWCI
- 50.11
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
