The paper is devoted to the application of a machine learning model with reinforcement for automating the planning of the deployment of logging sites in forestry. A method for optimizing the selection of cutting areas based on the algorithm of optimization of the Proximal Policy Optimization is proposed. An information system adapted for processing forest management data in a matrix form and working with geographic information systems has been developed. The experiments conducted demonstrate the ability to find rational options for the placement of cutting areas using the proposed method. The results obtained are promising for the use of intelligent systems in the forestry industry.

Keywords: reinforcement learning, deep learning, cutting areas location, forestry, artificial intelligence, planning optimization, clear-cutting

From the viewpoint of locomotor activity studies the ciliary apparatus stroboscopic method discussed in comparison with the main video speed by now, in their development. Showing difficulties stroboscopic method. Identified technical parameters that are required to create a complex research stroboscopic channel at the present stage of development of methods available on the new element base.

Keywords: motor activity of the ciliary apparatus; stroboscopic effect; computer video microscopy method.