Kostas Stamkopoulos’s scientific contributions

Profitability is not given the necessary attention in contemporary precision agriculture. In this work, a new tool, namely ProFit, is developed within a pre-existing farm management system, namely ifarma, to assess the profitability of precision agriculture applications in extended crops, as most of the current solutions available on the market respond inadequately to this need. ProFit offers an easy-to-use interface to enter financial records, while it uses the dynamic map view environment of ifarma to display the profitability maps. Worked examples reveal that profitability maps end up being quite different from yield maps in site-specific applications. The module is regulated at a 5 m spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of ifarma, together with a flexible architecture allowing future interventions and improvements, renders ProFit an innovative commercial tool.

Profitability is an underestimated concept in precision agriculture. In this research, a new module is developed within a pre-existing farm management system to assess the profitability of precision agriculture applications in extended crops. The module is regulated on a 5-meter spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of the farm management system, together with a flexible architecture and an easy-to-use interface, renders the new module an innovative commercial application.

Today, bridging precision agriculture (PA) with farm management is a high priority. To answer to this challenge, a precision fertilization service for extended crops was embedded as a new module in the platform of a pre-existing cloud-based farm management information system (FMIS), namely ‘ifarma’. The new module (namely, ‘PreFer’) uses interoperable geospatial formats, to guarantee free and rapid data exchange between a server-based geographic information system (GIS) and the ifarma platform. The GIS is used to store and process the complete farmers’ geodatabases, which are fed with data from multiple sources (e.g., soil surveys, satellite data, yield monitors, etc.). It is also used to feed the machine learning algorithms of ‘PreFer’ with the required data to produce the fertilization prescriptions in the form of digital maps. All the necessary maps are transferred automatically to the platform upon their production, where they can be viewed by the farmers. The platform is used also by farmers to insert pre-known or last-minute agronomic information required to the analysis. The overall developmental experience showed that such a customized application (such as ‘ifarma/PreFer’) is the most effective solution today, compared to other alternatives, towards fully operational precision agriculture services.

As innovative information and communication technology (ICT) tools were gradually introduced over the past decades into the agricultural sector, the use of farm management information systems (FMIS) was widely expanded and nowadays are regarded as important tools for managing the agricultural business. Nevertheless, the necessary workload for collecting, aggregating and importing data related to farming activities into a FMIS, is a task which is often time-consuming and farmers are reluctant to perform. The current paper describes the implementation of three automation levels, which enhance a FMIS by providing solutions related to the collection of fragmented-missing data and time-consuming data entry. The three levels involve: (i) the development of a modular FMIS based on future internet technologies, (ii) the use of standard values for assessing the cost of performed agricultural tasks and (iii) automating the process of importing task-related data into a FMIS using tractor's CAN-Bus ISO 11783 and SAE J1939 communication information. To assess the financial analysis of the developed FMIS, related data were collected, recorded and analysed for an entire growing season, from two distinct crops, i.e. winter wheat and maize. Furthermore, to assess the automated task formulation in the FMIS, machine data were acquired while ploughing with a mouldboard plough. The application proved capable of performing a profitability analysis based on the recorded cost transactions but also based on the information given by the user related to the performed tasks. With the automatically created task, the FMIS gave the possibility to the user to present and process the necessary information with minimum effort.

This paper is focusing on how to eliminate the required time for importing all the necessary data into a farm management information system (FMIS). This process was automated by using ISO 11783 and SAE J1939 communication information from the tractor’s CAN-Bus. Using a data logger and a machine to machine (M2M) gateway inside the tractor’s cabin, CAN-Bus data were recorded and transmitted to the cloud-based server of the FMIS. There, a script was responsible for parsing and aggregating the raw machine data into specific agricultural tasks and then importing them into the FMIS. The operator could choose the type of the performed task by a number of switches connected with the digital inputs of the data logger.

Agricultural production management is entering into a new era where every day farmer’s decisions are supported by highly sophisticated Farm Management Information Systems (FMISs). The latter have evolved from simple record keeping software into complex systems that can manipulate large amounts of data and provide decision support capabilities. In this paper, the development of an FMIS, which utilizes new technologies, such as those which were introduced by the European initiative Future Internet Public-Private Partnership Program (FI-PPP), is described. The developed application is focused upon individual farmers or farmer cooperatives, who wish to perform precision agriculture via the usage of mobile devices and modern technology. The main focus is to perform farm financial analysis based on all farm transactions but also estimating profitability based upon fixed values that the farmer imports. The application was successfully tested on a winter wheat crop (Triticum aestivum L.) for one season, where all related costs were recorded.

Agricultural production management is entering into a new era where every day farmer’s decisions are supported by highly sophisticated Farm Management Information Systems (FMISs). The latter have evolved from simple record keeping software into complex systems that can manipulate large amounts of data and provide decision support capabilities. In this paper, the development of an FMIS, which utilizes new technologies, such as those which were introduced by the European initiative Future Internet Public-Private Partnership Program (FI-PPP), is described. The developed application is focused upon individual farmers or farmer cooperatives, who wish to perform precision agriculture via the usage of mobile devices and modern technology. The main focus is to perform farm financial analysis based on all farm transactions but also estimating profitability based upon fixed values that the farmer imports. The application was successfully tested on a winter wheat crop (Triticum aestivum L.) for one season, where all related costs were recorded.