Abstract
Farmers have been able to detect, quantify, and respond to spatial and temporal variation in crops thanks to a variety of technological advancements in recent years. Precision farming aims to provide precise targeting of agricultural inputs while reducing waste and negative consequences. Precision agriculture technologies are effective instruments for increasing farm sustainability and production. These technologies provide ways to create more with less resources. Nowadays, improving agricultural production efficiency and crop yields is impossible without the use of contemporary digital technologies and smart machinery. The introduction of high-accuracy GPS technology into farm machinery, such as combine harvesters, has been a significant component of precision farming. In this paper, we present a streaming-based methodology for detecting anomalies in combine harvester GPS recordings. The key hypothesis is that, “similar points in a feature space have similar anomaly scores”. We examine a data-driven strategy with the goal of applying unsupervised detection algorithms to find anomalies on the fly. Based on the results of the experiments, we can conclude that LSCP beats all other strategies when the number of base detectors is varied. The AUCPR performance of LSCP with two base detectors (HBOS and MCD) is 8.02% better than the second best technique MCD.
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Moso, J.C., Cormier, S., Fouchal, H., Runz, C.d., Wandeto, J.M. (2021). Abnormal Behavior Detection in Farming Stream Data. In: Boumerdassi, S., Ghogho, M., Renault, É. (eds) Smart and Sustainable Agriculture. SSA 2021. Communications in Computer and Information Science, vol 1470. Springer, Cham. https://doi.org/10.1007/978-3-030-88259-4_4
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