Unmanned aerial vehicles (UAVs) are becoming increasingly important for military operations, including reconnaissance, providing battlefield support, and performing a variety of missions. The possibility of attacking targets using a group of UAVs increases the relevance of protection measures, in particular, camouflage. The literature review highlights the experience of using UAVs in military spheres of activity and their high efficiency in reconnaissance and support on the battlefield. Visual orientation on the terrain using high-resolution images was considered as a priority direction of UAV navigation in the conditions of the operation of electronic warfare tools. On the basis of hybrid convolutional neural networks, hardware for working with the UAV platform is serially produced. It is suggested to use vines for masking stationary objects due to their rapid growth and the corresponding quality/cost ratio. The purpose of the work is to check the effectiveness of masking small objects using herbaceous and spindly plants in relation to the prospects of their identification by optical means. The research was conducted using our own data obtained from UAVs in the visible range on experimental fields and the botanical garden of NUBiP of Ukraine and on pictures of buildings from the Internet. It has been experimentally established that the variety of shapes, sizes and structures of vegetation complicates the recognition process for convolutional neural networks. The appearance of plants can change significantly depending on the angle of photography, lighting and the state of mineral nutrition. Organic camouflage in the form of grasses and vines can completely or partially hide buildings and structures, changing their brightness and contour. This can obscure details and increase the risk of false object recognition. For example, structures covered with Aristolochia manshuriensis Kom. vines were correctly recognized only 27% of the time using a neural network. Convolutional neural networks require a large amount of data to train if the goal is to achieve high accuracy. However, if data with vine-covered objects is limited or unavailable, the training process may be insufficient, which may affect the network's ability to accurately recognize such objects.