Intelligent complex of monitoring and diagnostics of grape plantations

Abstract

Agriculture is considered to be one of the most important sectors of the economy in different countries. Presently, this industry, is witnessing a transition to mass digitalization of business processes, which makes it possible to effectively implement elements of strategic development and proactive management. This problem solving requires the creation of advanced monitoring systems for agriculture. The authors have developed an automated complex for monitoring and diagnostics of vineyards based on the use of an unmanned aerial vehicle (UAV) 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 diseases based on leaves images. To perform the detection procedures, a neural network based on the Fast R-CNN architecture with the InceptionV2 learning algorithm was used. Preliminary testing results of the technology effectiveness have demonstrated that the accuracy of infected leaves is at least 91%, while using a training sample containing 2,500 images of both healthy and damaged leaves. The developed mathematical model showed that the complex is capable of monitoring up to 2.5 hectares of vineyard during daylight hours.