Innovation project: Intelligent adaptation and networking for agricultural machinery
Purchasing agricultural machinery involves large investments. At the same time, much of this machinery is used for only a short period of the year. Combine harvesters, for example, are used on average for just 22 days each year. This makes it important to bring in an optimum harvest quickly and efficiently. To do so, machinery operators must take into consideration the conditions of each field, such as crop ripeness or soil conditions. At the same time, individual processes such as harvesting, transport and storage must be optimally coordinated. Until now, this has been a predominantly manual process based on experience. In order to increase the quality and efficiency of the entire harvesting process, agricultural machinery must autonomously adapt to the conditions of each individual field. A further issue is that of optimally coordinating individual processes, which requires the involvement of all participants in the harvesting process, such as manufacturers, contractors and farmers.
The aim of the research project is to develop software that allows different agricultural machinery to autonomously adapt to the current harvesting conditions and intelligently links individual processes and participants.
To that end, various field properties – such as ripeness and soil condition – and the sequence of individual processes – such as mowing, transport and storage – are analyzed. Next, the requirements for optimum use of the agricultural machinery and intelligent networking between the abovementioned participants are defined. On that basis, field properties are recorded and analyzed for different machinery and situations, and autonomous adaptation is designed and developed for the machinery. This involves integrating participants’ hardware, such as manufacturer and contractor databases. The project draws on the results of cross-sectional projects in self-optimization, intelligent networking and systems engineering. The intelligent software is supplemented with simulation technology, trialed in forage harvesting and implemented in agricultural machinery as a model.
It is estimated that utilization of agricultural machinery can be increased by at least 10%, thereby making better use of resources and improving the quality of harvesting processes. Autonomous adaptation also makes the machinery easier for drivers to use as they are no longer required to make manual changes during the harvesting process. The software can be transferred to other applications such as snow clearance, construction site operations and transport logistics.
01 October 2012 - 30 September 2014