Abstract
The aim of the study was a mobile development based on computer vision technologies and site parsing, which allows automating the process of diagnosing diseases of agricultural crops and issuing recommendations for treatment. The article discusses methods for recognizing plant diseases using computer vision, describes the principles of convolutional neural networks, selects the most appropriate machine learning model based on the accuracy, speed and efficiency of the model in conditions of limited resources of a mobile device, describes the tools: libraries and frameworks used for development. The detailed architecture of the application is presented, as well as the results of the developed software are demonstrated. A new contribution to the development of this topic is the experimental substantiation of the choice of a neural network model based on the analysis of its effectiveness on a prepared dataset, as well as the introduction of an automatic search for recommendations for a certain disease of agriculture. In the future, it is planned to introduce a voice assistant into this mobile application.
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