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Recent developments in nanotechnology transforming the agricultural sector: a transition replete with opportunities
Abstract
The applications and benefits of nanotechnology in the agricultural sector have attracted considerable attention, particularly in the invention of unique nanopesticides and nanofertilisers. The contemporary developments in nanotechnology are acknowledged and the most significant opportunities awaiting the agriculture sector from the recent scientific and technical literature are addressed. This review discusses the significance of recent trends in nanomaterial‐based sensors available for the sustainable management of agricultural soil, as well as the role of nanotechnology in detection and protection against plant pathogens, and for food quality and safety. Novel nanosensors have been reported for primary applications in improving crop practices, food quality, and packaging methods, thus will change the agricultural sector for potentially better and healthier food products. Nanotechnology is well‐known to play a significant role in the effective management of phytopathogens, nutrient utilisation, controlled release of pesticides, and fertilisers. Research and scientific gaps to be overcome and fundamental questions have been addressed to fuel active development and application of nanotechnology. Together, nanoscience, nanoengineering, and nanotechnology offer a plethora of opportunities, proving a viable alternative in the agriculture and food processing sector, by providing a novel and advanced solutions. © 2017 Society of Chemical Industry
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Cited by
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Metadata
- Authors
- Kim, D., Kadam, A., Shinde, S., Saratale, R. G., Patra, J., & Ghodake, G.
- Venue
- Journal of the Science of Food and Agriculture
- Publisher
- Wiley
- Published
- 2017-11-27
- Volume
- 98
- Issue
- 3
- Pages
- 849-864
- ISSN
- 0022-5142 (ISSN-L)
- 1097-0010
- OpenAlex
- W2766743260
- Cited by
- 312
- References
- 179
- FWCI
- 7.33
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.
