The modern techniques of precision agriculture require accurate soil maps. Important indicators are vegetation heterogeneities which can be mapped on a large scale by remote sensing and image processing methods. In this paper, the potential and the limitations of standard image processing methods are discussed in this context.
Improved farm management, optimised decision support models and the trace-ability of farm products require the automated acquisition of geo-referenced process data. Such a system for Automatic Process Data Acquisition in outdoor operations with tractor-implement combinations has been developed at the Technical University of Munich during the past few years. This system is based on the components LBS, GPS and IMI®. To analyse and to aggregate the huge amount of data collected in such a system an automatic evaluation program has been developed. This program, based on a SQL-based database-system and named IMIlyzer, shows a possible way for data analysing. Another developed and evaluated way is to implement measurement programs in the IMI®. The results of data analysing show a good comparability and possible assessment of fields by various accumulated values, relating to time and machinery efforts.
The paper gives an introduction to electronic process control for site specific plant production. It shows examples of state of the art technology and describes experiences made with practical application on preagro research and development project partner farms. Useful applications and opportunities which are not used to the full extend as well as problems and needs for further development are pointed out to the reader.
The Ground Truth Center Oberbayern (GTCO) is a center for the application of remote sensing data in agriculture funded by the Bavarian State. The center is coordinated by the Institute for Geography of the University of Munich. Partners are the Institute for Meteorology and the company VISTA. The goal of the GTCO is to support agricultural applications by using spaceborne or airborne remote sensing data. To achieve these operational methods for information extraction from remote sensing are developed using ground truth measurements as reference. User oriented products are derived from images depending on the customers needs. Besides the land use, also information on the biophysical stage of vegetation development can be gathered and from this information on actual growth can be derived. This is demonstrated by the spatial determination of agricultural yield based on satellite data which are used as input to an agro-meteorological growth model. Results are presented from a multi-annual analysis, which proves the validity and applicability of the method.
At present the partial project 6 of the IKB Dürnast („Information System Site Specific Crop Management Dürnast“) is developing an information system that records and stores GPSdata for scientific and practical purposes. With the aid of these information system field experiments are planned and controlled. Also a Cost Accounting System that allows ex post evaluations on a sub-field level has to be supplied. Depending on these goals one has to aggregate GPS-data to different sub-fields and/or different ways.
In the preagro project in the last two years a management information system for precision farming data was developed. To support interoperability in precision farming workflow the existing data model will be described and a way to setting up standards for data exchange by usage of XML will be demonstrated.
The software and hardware system FieldGIS has been developed for the increased need of time in updating digital physical block boundaries in Thuringia. Because of the entirely digital processing of the data, extensive work steps like the transmission and the integration in existing data records are curtailed and data errors or incomplete afield data recordings are avoided. The utilization of the systems shall be demonstrated considering the updating measures for the agricultural administration in the Free State of Thuringia as example.
Variable rate technology
Es wird ein integriertes System vorgestellt, das räumliche Daten verwendet, um Informationen über standortangepasste N-Düngung zu erzeugen. Dabei liegt der Fokus des Beitrags auf einem neuronalen Netz, das vor allem zur Ertragsprognose herangezogen wird. Zudem können aus der Analyse des verwendeten neuronalen Netzes Rückschlüsse auf die Qualität der verwendeten Datensätze und deren Attribute gezogen werden.
The paper presents an integrated system that uses spatial data for the creation of information about site specific nitrogen fertilization. The focus of the article is on a neural network that is mainly used for yield prognosis. Conclusions concerning the quality of the used databases and their attributes can be drawn from the analysis of the used neural network.
Site specific crop management
Site specific crop management makes it possible to reduce the amount of data to be entered manually for a computer-based cost-accounting system. Such a cost-accounting system, called "SISCA" is to be developed evaluating site specific data at Weihenstephan. Among other calculation components of an information system for site specific crop management, the cost-accounting system represents a specific excerpt from the farm wide database. All costs and outputs will be gathered in the cost-accounting according to the following partial working procedures: start-up, transport and field work. Sub-cost centers (grids) represent the smallest coverage unit for site-based costs and outputs. Data from mobile process technique will be integrated automatically into the cost-accounting system by a special interface. By means of this interface input data can still be processed before they are gathered with the model.
Site-specific utilization and cultivation of natural resources in plant production is fundamentally important for optimal information- and quality-management in the field of plant production. This paper explains in particular the outstanding role of pedologic data in view of the gross yield of plants as well as the obtaining of soil data and the processing for usage in precision farming.