Automated technological complex for monitoring and diagnostic vineyard

Agriculture is one of the most important sectors of the economy of various countries. Currently, there is a transition to mass digitalization of business processes in this industry, which makes it possible to effectively implement elements of strategic development and proactive management. At the same time, the use of innovative technologies requires competent conceptual and methodological validity, taking into account the peculiarities of growing individual crops, climatic and other factors. The solution of this problem requires the creation of advanced monitoring systems for agricultural facilities. The authors proposed a development offering automated monitoring and diagnostics of vineyards based on the use of unmanned aerial vehicles (UAVs) and specialized software. The proposed solution makes it possible to assess the phytosanitary condition of the vineyard using the procedures of neural network classification of grape roots based on images of leaves. To perform detection procedures, a neural network based on the Fast R-CNN architecture with the learning algorithm InceptionV2 was used. Preliminary results of testing the effectiveness of the technology showed that the accuracy of detecting affected leaves is at least 91% when using a training sample consisting of 2500 images of healthy and damaged leaves. The article also presents a mathematical model that allows evaluating the performance of the complex, taking into account the topological features of the vineyard, the type of UAV used, meteorological parameters and the performance of computing equipment. The results of the evaluation calculations showed that the complex is capable of monitoring up to 2,5 hectares of vineyard during daylight hours. The introduction of the complex into the production process of agro-industrial enterprises will make it possible to effectively identify and promptly eliminate diseases at early stages, which will increase the yield of manufactured products, as well as reduce possible financial risks of the enterprise.