Roots of winter wheat grown in the field were examined for the occurrence of the fungi Gaeumannomyces graminis, Phialophora and Fusarium spp. Plants were sampled and examined in the autumn of 2000 and 2001 and in the following spring. Root systems were visually assessed and a percentage of affected roots were determined on 100 plants per field. More and less virulent members of the G. graminis- Phialophora complex and other fungi were isolated from infected roots. Above 85% of isolated fungi were classified as Gaeumannomyces-Phialophora complex. Morphological characteristics of the fungi isolated from plant roots were analysed in laboratory tests. In pathogenicity tests were assessed: disease severity, height of plants, percentage of chlorotic or necrotic leaves and biomass of whole plants.

The paper deals with the application of the feed-forward and cascade-forward neural networks to mechanical state variable estimation of the drive system with elastic coupling. The learning procedure of neural estimators is described and the influence of the input vector size and neural network structure to the accuracy of state variable estimation is investigated. The quality of state estimation by neural estimators of different types is tested and compared. The simple optimisation procedure is proposed. Optimised neural estimators of the torsional torque and the load machine speed are tested in the open-loop and closed-loop control structure of the drive system with elastic joint, with additional feedbacks from the shaft torque and the difference between the motor and the load speeds. It is shown that torsional vibrations of the two-mass system are damped effectively using the closed-loop control structure with additional feedbacks obtained from the developed neural estimators. The simulation results are confirmed by laboratory experiments.