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Anomaly Detection on Data Streams for Smart Agriculture
| Content Provider | MDPI |
|---|---|
| Author | Moso, Juliet Chebet Cormier, Stéphane de Runz, Cyril Fouchal, Hacène Wandeto, John Mwangi |
| Copyright Year | 2021 |
| Description | Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is |
| Starting Page | 1083 |
| e-ISSN | 20770472 |
| DOI | 10.3390/agriculture11111083 |
| Journal | Agriculture |
| Issue Number | 11 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-02 |
| Access Restriction | Open |
| Subject Keyword | Agriculture Computer Science Industrial Engineering Anomaly Detection Data Streams Precision Farming Unsupervised Learning |
| Content Type | Text |
| Resource Type | Article |
