The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics
Open accessAbstract
Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.
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Cites
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Metadata
- Authors
- Buja, I., Sabella, E., Monteduro, A. G., Chiriacò, M. S., De Bellis, L., Luvisi, A., & Maruccio, G.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-03-18
- Volume
- 21
- Issue
- 6
- Pages
- 2129
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3136389514
- Cited by
- 284
- References
- 90
- FWCI
- 19.95
- Type
- journal article
- Language
- en
- Open access
- gold
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- Buja, I., Sabella, E., Monteduro, A. G., Chiriacò, M. S., De Bellis, L., Luvisi, A., & Maruccio, G. (2021). Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics. Sensors. https://doi.org/10.3390/s21062129
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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.