Towards an alert system for coffee diseases and pests in a smart farming approach based on semi-supervised learning and graph similarity
Advances in Intelligent Systems and Computing, ISSN: 2194-5357, Vol: 687, Page: 111-123
2018
- 5Citations
- 49Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Smart Farming represents a new approach based on management of observation, measurement and response to internal and external variations in crops. This approach is closely related to a current trend area in Information and Communication Technologies such as Big Data. The application of machine learning techniques to agriculture data allows to assist in decision making and predict what will happen in the future (predictive analysis). From predictive models, the inexact graph matching would allow to establish the probability of occurrence of one or another disease or in such case the presence of a pest, based on the analysis of the crop conditions. This paper presents a review of some areas involved in the definition of an alert system for diseases and pests in a Smart Farming approach, based on machine learning and graph similarity. Finally, the integration of the mentioned areas for their application in coffee crops is proposed.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85034585401&origin=inward; http://dx.doi.org/10.1007/978-3-319-70187-5_9; http://link.springer.com/10.1007/978-3-319-70187-5_9; https://doi.org/10.1007%2F978-3-319-70187-5_9; https://dx.doi.org/10.1007/978-3-319-70187-5_9; https://link.springer.com/chapter/10.1007/978-3-319-70187-5_9
Springer Science and Business Media LLC
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