The suitable neural network topology design for object recognition in the grass.

This thesis describes testing of artificial neural network depending on learning set for object recognition in grass. Values of experimenting with different topologies in Neuroph Studio were recorded. Testing was conducted on three sets of learning basic shapes. Verification of the ability of learning networks were tested using four basic shapes captured in the grass (square, rectangle, circle and triangle). When evaluating the most appropriate topology was defined by two main criteria. Number of successful recognition of shapes and distribution by recognized shapes. The theoretical part describes the functionality and features of neural networks, image recognition using edge detection and existing applications for recognition of plant species