Anomaly Detection on Data Streams for Smart Agriculture

Juliet Chebet Moso, Stéphane Cormier, Cyril de Runz, Hacène Fouchal and John Mwangi Wandeto
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Juliet Chebet Moso: CReSTIC EA 3804, Université de Reims Champagne-Ardenne, 51097 Reims, France
Stéphane Cormier: CReSTIC EA 3804, Université de Reims Champagne-Ardenne, 51097 Reims, France
Cyril de Runz: BDTLN, LIFAT, University of Tours, Place Jean Jaurès, 41000 Blois, France
Hacène Fouchal: CReSTIC EA 3804, Université de Reims Champagne-Ardenne, 51097 Reims, France
John Mwangi Wandeto: Computer Science, Dedan Kimathi University of Technology, Private Bag-10143, Dedan Kimathi, Nyeri 10143, Kenya

Agriculture, 2021, vol. 11, issue 11, 1-17

Abstract: 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 58.7 % better than that of the second-best approach. In the crop dataset, our analysis showed that 30 % of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health.

Keywords: anomaly detection; data streams; precision farming; unsupervised learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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