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Neural network image analysis in agriculture using a SaaS system


Neural network image analysis in agriculture using a SaaS system

Belousov I.S., Rogachev A.F.

Incoming article date: 17.07.2022

In a number of branches of agricultural production, including agriculture, land reclamation, etc., there are problems, the solution of which requires the use of artificial intelligence. In particular, the assessment of the reclamation state of agricultural fields in large areas is a very time-consuming task, even with the use of unmanned aerial vehicles. To automate these intelligent approaches, it is effective to use artificial neural networks (INS) implemented in the form of computer programs. The use of software as a service (SaaS) is a modern approach to computer support of various intelligent production processes, including agricultural. Agriculture is a promising industry for the introduction of such technologies. The aim of the study is to develop a methodology and create a cloud-based SaaS system for identifying defective areas of agricultural fields based on INS. The development of neural network technologies and cloud services makes it possible to process a large amount of information in the cloud and provide user access to computing power. The article describes the methodology of building a service architecture for recognizing problem areas of cultivated agricultural fields, data preparation, network training, development of client and server parts. Such implementation is possible with the use of such technologies and tools as CUDA, CNN, PyTorch. As a result, the strengths and weaknesses of their use for solving the problem of image recognition on the example of problem areas of agricultural fields were identified. It has been established that classification-type INS are capable of solving problems of recognizing the reclamation state of fields, and modern information technologies make it possible to transfer calculations to the cloud, while the cloud service can be monetized as a SaaS model.

Keywords: agriculture, color images, SaaS system, artificial neural network, image classification