Cropinfra research data collection platform for ISO 11783 compatible and retrofit farm equipment

Backman, Juha; Linkolehto, Raimo; Koistinen, Markku; Nikander, Jussi; Ronkainen, Ari; Kaivosoja, Jere; Suomi, Pasi; Pesonen, Liisa

doi:10.1016/j.compag.2019.105008

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…For this purpose, IoT solutions had to deal with the characteristics inherent to agricultural equipment, such as mobility. Data from the equipment itself, such as implement status, engine performance, or speed are collected using sensors [ 41 ] to optimize their maintenance cycle. Additionally, due to the mobility of agricultural equipment, opportunistic computing was used to collect data from remote crop areas by using sensors coupled to tractors [ 174 ].…”

Section: Discussionmentioning

confidence: 99%

“…MQTT is an open-source messaging protocol that enables communication between constrained devices and in unreliable networks [ 40 ]. MQTT runs over TPC/IP or similar protocols (e.g., Bluetooth) [ 41 ], which makes the use of MQTT appropriate for different IoT solutions. The MQTT protocol, which is based on the publish/subscribe architecture, allows communication between devices to take place in the following way.…”

Section: Conceptsmentioning

confidence: 99%

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A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

219

100

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The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Conceptsmentioning

confidence: 99%

A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

219

100

View full text Add to dashboard Cite

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“…Still new and massive field trials are needed in order to understand the relation between different quality factors of crops and their spectral reflectance. The suggestion for future PF research is to first have an overall data collection infra, such as Cropinfra (Pesonen et al 2012, Backman et al 2019, on top of that there is a need to improve data quality with accurate positioning and headland automation and then combine the entire system with field trial infrastructure, where the knowledge for thematic and temporal accuracy is gained and beneficial PF applications can be designed. A missing part of this chain will leave propagating uncertainties.…”

Section: Discussionmentioning

confidence: 99%

Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

Kaivosoja

Näsi

Hakala

et al. 2019

Communications in Computer and Information Science

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“…Platforms like CyCAN [ 28 ], deployed in past experiments like [ 35 , 36 ], can collect CAN data only. Other platforms, namely ISOBlue 2.0 [ 31 ] and Cropinfra [ 34 ], are able to capture GNSS data in addition to CAN data. However, none of these platforms collect rich-enough data to infer operational contexts.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Lin

Krogmeier

et al. 2020

Sensors

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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.

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Cropinfra research data collection platform for ISO 11783 compatible and retrofit farm equipment

Cited by 12 publications

References 17 publications

A Systematic Review of IoT Solutions for Smart Farming

A Systematic Review of IoT Solutions for Smart Farming

Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

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

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