Cropinfra Research Data Collection Platform for ISO 11783 Compatible and Retrofit Farm Equipment
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This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Backman, Juha; Linkolehto, Raimo; Koistinen, Markku; Nikander, Jussi; Ronkainen, Ari; Kaivosoja, Jere; Suomi, Pasi; Pesonen, Liisa Cropinfra research data collection platform for ISO 11783 compatible and retrofit farm equipment Published in: Computers and Electronics in Agriculture DOI: 10.1016/j.compag.2019.105008 Published: 01/11/2019 Document Version Publisher's PDF, also known as Version of record Published under the following license: CC BY Please cite the original version: Backman, J., Linkolehto, R., Koistinen, M., Nikander, J., Ronkainen, A., Kaivosoja, J., Suomi, P., & Pesonen, L. (2019). Cropinfra research data collection platform for ISO 11783 compatible and retrofit farm equipment. Computers and Electronics in Agriculture, 166, [105008]. https://doi.org/10.1016/j.compag.2019.105008 This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Powered by TCPDF (www.tcpdf.org) Computers and Electronics in Agriculture 166 (2019) 105008 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Cropinfra research data collection platform for ISO 11783 compatible and T retroft farm equipment ⁎ Juha Backmana, , Raimo Linkolehtoa, Markku Koistinena, Jussi Nikanderb, Ari Ronkainena, Jere Kaivosojaa, Pasi Suomia, Liisa Pesonena a Natural Resources Institute Finland (Luke), Production Systems, Tietotie 2, 02150 Espoo, Finland b Aalto University, School of Engineering, Department of Built Environment, Otakaari 4, Espoo, Finland ARTICLE INFO ABSTRACT Keywords: The agricultural machinery produces an increasing number of measurements during operations. The primary use ISOBUS of these measurements is to control agricultural operations on the farm. Data that describes the in-feld variation Document database in plant growth potential and growing conditions is the basis for precision farming. The secondary use for the Development platform gathered information is documentation of work and work performance for business purposes. Researcher also Data capture benefts from the increasing measurement capabilities. Biologists and agronomists can model the crops and Data transfer agronomic phenomena. Work scientists can analyse the agricultural work processes. And fnally, machines with additional accurate sensors can be used for agricultural machine product development and technological re- search purposes. This paper concentrates on an independent research data collection platform (Cropinfra) which can be used to collect data for all above mentioned purposes. Data can be collected both from ISOBUS (ISO 11783) compliant machines as well as older and proprietary systems and stored to database for further analysis. The farm machines in Cropinfra are supplemented with extra sensors that are more accurate than existing in commercial machines. Therefore, the Cropinfra can be used as a reference measurement system to verify the correct operation of the machines as well as to produce data for biological research purposes. This paper will also present how the cloud connection of the data collection system can be realized. The solution was designed to be compatible with the existing ISO 11783-10 standard. The examples presented in this paper verify that the solution works in real farming environment. The data has been used in numerous research projects already, and in the future the data will be an important asset when machine learning and other artifcial intelligence methods will be studied and utilized. 1. Introduction plant protection decisions and prescription maps for precision farming operations (Söderström et al., 2016). Another use for the information is Agricultural machinery has gone through signifcant changes during documentation of work and work performance for business purposes. last decades. The introduction of electronic control units (ECU) has For example, an agricultural contractor can verify the work they have opened up new ways for carrying out agricultural operations (Stone carried out by showing the measurement data to the customer et al., 2008). Especially the GNSS (Global Navigation Satellite System) (Suonentieto, 2019; Dataväxt, 2019). In addition to the land owners based solutions enable position based precision farming (Thomasson and farmers, researchers can also beneft from the increasing mea- et al., 2019). At the same time the sensors and other measurement surement capabilities. Biologists and agronomists can model crops and devices have also developed tremendously and their unit price has agronomic phenomena, verify hypotheses and produce new knowledge come down (SACHS, 2014). Agricultural machinery produces an in- (Kaivosoja et al., 2013). Machinery data is an important source of so creasing number of measurements during operations. This information called big data in agriculture (Wolfert et al., 2017). Also work scientists can be used for many purposes. One primary purpose is to use data can analyse the agricultural work processes in more detail. And fnally, gathered to control agricultural operations. Soil conditions, crop machines with additional and accurate sensors can be used for Product growth and health can be monitored and used to make fertilization and development of agricultural machinery and technological research ⁎ Corresponding author. E-mail address: [email protected] (J. Backman). https://doi.org/10.1016/j.compag.2019.105008 Received 21 November 2018; Received in revised form 30 July 2019; Accepted 12 September 2019 0168-1699/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). J. Backman, et al. Computers and Electronics in Agriculture 166 (2019) 105008 purposes (Öhman et al., 2004; Suomi and Oksanen, 2015) and all necessary implements to operate the farm. The results of Cro- Data can be collected from the machinery using a number of dif- pinfra based research projects have recently been reported in Nikander ferent ways. The existing electronic control units (ECUs) can create et al. (2017), Nikander et al. (2015) and Pesonen et al. (2014). their own proprietary logs, which can be downloaded to USB stick or, in In this paper we report the methods and means used to develop some cases, to cloud servers. Another way is to use an independent Cropinfra into a framework that can be used as a general agricultural logging unit which does the same, but is not directly bundled with any data collection and exploitation system for research purposes. The manufacturer’s control units. In both cases, the data collection includes primary objective of this paper is to depict how the existing technology on four phases: measurement devices or sensors, data capture, data agricultural data collection can be integrated into an open research system. transfer, and data storage. However, the last two phases of the process A secondary objective is to present the experiences and results of diferent can be fundamentally diferent depending on the technology used. ways to realize the cloud connection of data collection system that is at the Farms can be very heterogeneous, as the history of the farm, the moment crucial improvement to be included in the ISOBUS standard. geography of the land used by the farm, the crops and livestock grown The rest of this paper is organized as follows. In Section 2, methods, on the farm, as well as technological capability and business strategy of we describe how the information management and software environ- the farm all afect how the farm works. The type of production and the ment in a modern farm can be organized, focusing on the parts that are size of the farm defne the type and size of machinery required. vital to this paper. In Section 3 we describe how the CropInfra platform Furthermore, farm’s machinery is often a mixture of older and newer implements farm information management infrastructure, and how the machines from a number of manufacturers. Rarely any manufacturer system has been used for data capturing and management using ex- can provide all machines, devices and software that the farmer needs. amples. Section 4 contains the discussion and conclusions of our work. Therefore there is a strong need for compatibility between diferent brands. In agriculture the communication between diferent compo- 2. Methods nents in tractor-implement system is defned by the commonly agreed standard ISO 11783 (ISO, 2017) – marketed under the name ISOBUS This work is based on data collection from agricultural machinery, (AEF, 2019a). The Agricultural Electronic Industry Foundation (AEF) as well as the analysis, and exploitation of that data both in cloud develops and markets the ISOBUS system. systems and in the machinery. In this work, we concentrate on the four In the previous research projects the information management of phases of data collection: measurement, capture, transfer, and storage. In farm machinery and the data transfer from the machinery to farm the measurement phase, the various sensors