CREATING A NEURAL NETWORK SERVICE FOR IDENTIFYING PROBLEM AREAS OF AGRICULTURAL FIELDS
The creation of a neural network service for identifying problem areas of reclaimed agricultural fields is considered. The architectures of modern artificial neural networks are analyzed and the construction of a neural network for the subject area under study is justified. Recommendations on the construction of the client and server parts of the developed service are given. Introduction. Agricultural production requires the processing of significant amounts of information to solve various management problems. The development of neural network and cloud technologies makes it possible to process information in the cloud with the provision of access to computing power by the user. Materials and methods. The article describes the construction of artificial neural networks of various architectures - the ResNext, AlexNet, R-CNN models and their combinations, as well as modifications of the YOLO algorithms. As well as tools for creating a cloud computing runtime. Results and conclusion. The choice of service parameters for recognizing problem areas of reclaimed agricultural fields has been substantiated. Recommendations are given for performing preprocessing of the initial graphic data, methods of training the constructed ANNs. Recommendations are given for the development of the client and server parts of the system using the technologies and tools of CNN, PyTorch, CUDA. The strengths and weaknesses of their use for solving the problem of image recognition are identified using the example of problem areas of agricultural fields. The service can be used as an independent solution, as well as part of other systems. The studies carried out made it possible to formulate the following conclusions. The use of the developed classification ANN has shown that neural networks are able to successfully solve problems of recognizing the state of agricultural fields with the identification of defective areas of various nature. Modern infocommunication technologies make it possible to implement neural network computing in the cloud with the provision of convenient scaling of computing power. Cloud service can be implemented and monetized like SaaS.