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The article presents our solution for the classification of moving flying objects in a video sequence captured by a static camera. The tool uses the extraction of scale and rotation invariant SIFT features, which allow the multi-class SVM to classify the examined object into one of the considered classes: ‘bird’, ‘plane’ or ‘negative’. The most successful of our tested models achieved accuracy of over 90% and their recall and precision for each class reached values above 90%.

Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure loses its purpose. To help eliminate the difficulty, we present a system to detect flocks and to trigger an actuator that will scare the objects only when a flock passes through the monitored space. The actual detection is performed with artificial intelligence utilizing a convolutional neural network. Before teaching the network, we employed videocameras and a differential algorithm to detect all items moving in the vineyard. Such objects revealed in the images were labeled and then used in training, testing, and validating the network. The assessment of the detection algorithm required evaluating the parameters precision, recall, and F1 score. In terms of function, the algorithm is implemented in a module consisting of a microcomputer and a connected videocamera. When a flock is detected, the microcontroller will generate a signal to be wirelessly transmitted to the module, whose task is to trigger the scaring actuator.

The large set of scientific activities supported by MRI includes, among others, the research of water and mineral compounds transported within a plant, the investigation of cellular processes, and the examination of the growth and development of plants. MRI is a method of major importance for the measurement of early somatic embryos (ESE) during cultivation, and in this respect it offers several significant benefits discussed within this paper. We present the following procedures: non-destructive measurement of the volume and content of water during cultivation; exact three-dimensional differentiation between the ESEs and the medium; investigation of the influence of ions and the change of relaxation times during cultivation; and multiparametric segmentation of MR images to differentiate between embryogenic and non-embryogenic cells. An interesting technique consists in two-parameter imaging of the relaxation times of the callus; this method is characterized by tissue changes during cultivation at a microscopic level, which can be monitored non-destructively., Velký soubor vědeckých činností využivajících MRI zahrnuje, mimo jiné, výzkum vody a minerálních sloučenin, které jsou přepravovány v rostlinách, vyšetřování buněčných procesů a vyšetření růstu a vývoje rostlin. MRI je metoda zásadního významu pro měření raných somatických embryí (ESE) během pěstování, a v tomto ohledu nabízí několik významných přínosů, o kterých je projednáno v tomto článku. Uvádíme následující postupy: nedestruktivní měření objemu a obsahu vody během pěstování; přesná trojrozměrná diferenciace mezi ESE a médiem; zkoumání vlivu iontů a změny relaxačních časů během pěstování; víceparametrická segmentace obrazů pro rozlišení mezi embryogenními a neembryogenními buňkami. Navržená technika spočívá v analýze dvou parametrů zobrazování relaxačních časů kultury. Navržená metoda zobrazování je charakterizována změnou tkáně během pěstování na mikroskopické úrovni, kterou lze sledovat nedestruktivně.