{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T07:56:19Z","timestamp":1721894179202},"reference-count":76,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011929","name":"Foundation for Food and Agriculture Research","doi-asserted-by":"publisher","award":["534662"],"id":[{"id":"10.13039\/100011929","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"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.<\/jats:p>","DOI":"10.3390\/s20205768","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T01:24:39Z","timestamp":1602725079000},"page":"5768","source":"Crossref","is-referenced-by-count":3,"title":["ISOBlue HD: An Open-Source Platform for Collecting Context-Rich Agricultural Machinery Datasets"],"prefix":"10.3390","volume":"20","author":[{"given":"Yang","family":"Wang","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"He","family":"Liu","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"James","family":"Krogmeier","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Amy","family":"Reibman","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Dennis","family":"Buckmaster","sequence":"additional","affiliation":[{"name":"Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1002\/rob.20300","article-title":"Coverage path planning algorithms for agricultural field machines","volume":"26","author":"Oksanen","year":"2009","journal-title":"J. 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