Water is a sensitive and limited resource, mainly in intensively used agricultural areas in Austria, where groundwater is used as drinking water as well as for irrigation purposes. In order to guarantee a sustainable use of irrigation water, soil water measurement devices can be used to opti-mise irrigation, which means that controlling the soil water content in the entire root system may prevent water stress due to water deficiency on the one hand, and over wetting on the other hand. Furthermore, losses of nutrients due to leaching can be avoided. Several research studies on that topic were initiated during the last few years. The soil water status on selected fields planted with different crops (onions, carrots, sugar beets, sweet maize, zucchini) was monitored continuously by FDR (Fre-quency Domain Reflectometry) soil water measurement devices. Sensors in different depths measure the plant water uptake in the root zone under standard irrigation practices on different sites and dif-ferent soils, respectively. The deepest sensor is installed to avoid deep percolation caused by over irrigation. By means of these data, irrigation could be regulated based on the actual plant water re-quirements to keep the soil water content within an ideal range for crop development.

Surface roughness parameter prediction and evaluation are important factors in determining the satisfactory performance of machined surfaces in many fields. The recent trend towards the measurement and evaluation of surface roughness has led to renewed interest in the use of newly developed non-contact sensors. In the present work, an attempt has been made to measure the surface roughness parameter of different machined surfaces using a high sensitivity capacitive sensor. A capacitive response model is proposed to predict theoretical average capacitive surface roughness and compare it with the capacitive sensor measurement results. The measurements were carried out for 18 specimens using the proposed capacitive-sensor-based non-contact measurement setup. The results show that surface roughness values measured using a sensor well agree with the model output. For ground and milled surfaces, the correlation coefficients obtained are high, while for the surfaces generated by shaping, the correlation coefficient is low. It is observed that the sensor can effectively assess the fine and moderate rough-machined surfaces compared to rough surfaces generated by a shaping process. Furthermore, a linear regression model is proposed to predict the surface roughness from the measured average capacitive roughness. It can be further used in on-machine measurement, on-line monitoring and control of surface roughness in the machine tool environment.