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ISOBlue HD: An Open-Source Platform for Collecting Context-Rich Agricultural Machinery Datasets

This paper introduces an open-source platform called ISOBlue HD for acquisition of context-rich data from agricultural machinery. We call these datasets context-rich, because they enable the identification of machine status and farming logistics by properly labeling, fusing, and processing the data....

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Detalles Bibliográficos
Autores principales: Wang, Yang, Liu, He, Krogmeier, James, Reibman, Amy, Buckmaster, Dennis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600794/
https://www.ncbi.nlm.nih.gov/pubmed/33053819
http://dx.doi.org/10.3390/s20205768
Descripción
Sumario:This paper introduces an open-source platform called ISOBlue HD for acquisition of context-rich data from agricultural machinery. We call these datasets context-rich, because they enable the identification of machine status and farming logistics by properly labeling, fusing, and processing the data. The system includes a single board computer, a cellular modem, local storage, and power-over-ethernet switch to sensors. The system allows remote diagnostics and access, automatic startup/shut down with vehicle operations, and uses Apache Kafka to enable robust data exchange. ISOBlue HD was deployed in a combine harvester during a 2019 wheat harvest for simultaneously capturing 69 million CAN frames, 230,000 GPS points, and 437 GB of video data, focusing on header status and operator actions over 84 h of harvest time. Analyses of the collected data demonstrate that contextual knowledge can be inferred on harvest logistics (paths, speeds, header status, material transfer) and sensor data semantics.