Anomaly Detection on Data Streams for Smart Agriculture

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 anomali...

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Autores principales:	Juliet Chebet Moso, Stéphane Cormier, Cyril de Runz, Hacène Fouchal, John Mwangi Wandeto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	anomaly detection data streams precision farming unsupervised learning Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/4b34c692a13f496883234f176f9590fc
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spelling	oai:doaj.org-article:4b34c692a13f496883234f176f9590fc2021-11-25T15:58:54ZAnomaly Detection on Data Streams for Smart Agriculture10.3390/agriculture111110832077-0472https://doaj.org/article/4b34c692a13f496883234f176f9590fc2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1083https://doaj.org/toc/2077-0472Smart 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>58.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> better than that of the second-best approach. In the crop dataset, our analysis showed that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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.Juliet Chebet MosoStéphane CormierCyril de RunzHacène FouchalJohn Mwangi WandetoMDPI AGarticleanomaly detectiondata streamsprecision farmingunsupervised learningAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1083, p 1083 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anomaly detection data streams precision farming unsupervised learning Agriculture (General) S1-972
spellingShingle	anomaly detection data streams precision farming unsupervised learning Agriculture (General) S1-972 Juliet Chebet Moso Stéphane Cormier Cyril de Runz Hacène Fouchal John Mwangi Wandeto Anomaly Detection on Data Streams for Smart Agriculture
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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>58.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> better than that of the second-best approach. In the crop dataset, our analysis showed that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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.
format	article
author	Juliet Chebet Moso Stéphane Cormier Cyril de Runz Hacène Fouchal John Mwangi Wandeto
author_facet	Juliet Chebet Moso Stéphane Cormier Cyril de Runz Hacène Fouchal John Mwangi Wandeto
author_sort	Juliet Chebet Moso
title	Anomaly Detection on Data Streams for Smart Agriculture
title_short	Anomaly Detection on Data Streams for Smart Agriculture
title_full	Anomaly Detection on Data Streams for Smart Agriculture
title_fullStr	Anomaly Detection on Data Streams for Smart Agriculture
title_full_unstemmed	Anomaly Detection on Data Streams for Smart Agriculture
title_sort	anomaly detection on data streams for smart agriculture
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4b34c692a13f496883234f176f9590fc
work_keys_str_mv	AT julietchebetmoso anomalydetectionondatastreamsforsmartagriculture AT stephanecormier anomalydetectionondatastreamsforsmartagriculture AT cyrilderunz anomalydetectionondatastreamsforsmartagriculture AT hacenefouchal anomalydetectionondatastreamsforsmartagriculture AT johnmwangiwandeto anomalydetectionondatastreamsforsmartagriculture
_version_	1718413377911390208

Anomaly Detection on Data Streams for Smart Agriculture

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