Working Zone Identification for Specialized Micro Transportation Systems Using GPS Tracks

Zhang, Yaguang; Balmos, Andrew; Krogmeier, James V.; Buckmaster, Dennis R.

doi:10.1109/itsc.2015.289

Cited by 5 publications

(7 citation statements)

References 11 publications

(9 reference statements)

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“…Coordinate-based techniques involve the use of geolocation and/or operational data from ground-based machinery in motion to detect field boundaries. Field segmentation may be performed using coordinate-based trajectory segmentation methods [34], [35], [36], [37], [38]. An advantage of segmentation by this method is that it offers a job-specific context to machinery operational data.…”

Section: B Coordinate-based Methodsmentioning

confidence: 99%

Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements

Harkin,

Crotty,

Warren

et al. 2024

IEEE Trans. Agri. Elect.

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Establishing and maintaining farmland geometric boundaries is crucial to increasing agricultural productivity. Accurate field boundaries enable farm machinery contractors and other farm stakeholders to calculate charges, costs and to examine machinery performance. Field segmentation is the process by which agricultural field plots are geofenced into their individual field geometric boundaries. This paper presents a novel coordinate-based method to perform trajectory segmentation and field boundary detection from a tractor towing an implement. The main contribution of this research is a practical, robust algorithm which can solve for challenging field-to-field segmentation cases where the operator engages the towed implement continuously across several fields. The algorithm first isolates raw machinery trajectory data into unique job sites by using a coarse filter on geolocation data and implement power-take off activation. Next, the coordinate data is plotted and image processing techniques are applied to erode any pathway(s) that may present in job sites with adjacent working fields. Georeferenced time series tractor and implement data were aggregated from a five-month-long measurement campaign of a silage baling season in Galway, Ireland. The algorithm was validated against two unique machinery implement datasets, which combined, contain a mixture of 296 road-to-field and 31 field-tofield cases. The results demonstrate that the algorithm achieves an accuracy of 100% on a baler implement dataset and 98.84% on a mower implement dataset. The proposed algorithm is deterministic and does not require any additional labor, land traversal or aerial surveillance to produce results with accuracy metrics registering above 98%.

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Section: B Coordinate-based Methodsmentioning

confidence: 99%

Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements

Harkin,

Crotty,

Warren

et al. 2024

IEEE Trans. Agri. Elect.

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“…The second step was to extract only those points within a specific field using a bounding box. The extracted data exhibited normal harvesting behaviour with all data points below 4 mph, which is the maximal harvesting speed [29]. The data were further processed by removing all data points with zero speed, since our interest was on GPS points associated to actual grain harvest.…”

Section: Scenario A: Combine Harvester Gps Logsmentioning

confidence: 99%

Anomaly Detection on Data Streams for Smart Agriculture

et al. 2021

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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.

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“…Intelligent Transportation Systems (ITS) offer substantial advances in transportation system safety, mobility, productivity and environmental conservation [7]. Specialized Micro Transportation Systems (SMTS) are a subset of ITS which comprise of a limited number of vehicles cooperating on a specialized job [23]. SMTS vehicles frequently follow predictable and recognized patterns of activity, which vary depending on the operation at hand.…”

Section: Specialized Micro Transportation Systemsmentioning

confidence: 99%

“…Changes in combine harvester speed, on the other hand, usually signal in-field or on-road actions such as turning and accelerating. During harvesting a combine harvester should operate at a maximum of 4mph for yield efficiency reasons and a maximum of 20mph on the road or when moving between fields [23]. Moving at high speed can result in grain being missed thus lowering the yield.…”

Section: Problem Statementmentioning

confidence: 99%

Abnormal Behavior Detection in Farming Stream Data

Moso

Cormier

Fouchal

et al. 2021

Smart and Sustainable Agriculture

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Working Zone Identification for Specialized Micro Transportation Systems Using GPS Tracks

Cited by 5 publications

References 11 publications

Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements

Field-to-Field Coordinate-Based Segmentation Algorithm on Agricultural Harvest Implements

Anomaly Detection on Data Streams for Smart Agriculture

Abnormal Behavior Detection in Farming Stream Data

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