Application of quantum computing in image processing for recognition of infectious diseases of wheat

Abstract

This study is devoted to the development and application of quantum methods in the field of diagnostics of infectious diseases of wheat. Taking into account the relevance of the problem of agriculture and the need to improve the efficiency of plant disease control, the work proposes a new approach based on the combined use of quantum computing, image processing and machine learning. Quantum image processing techniques have been applied to improve contrast, filter noise, and analyze key features of infectious diseases in the early stages of their development. The developed quantum machine learning models demonstrate high ac-curacy in image classification, which contributes to earlier and more accurate detection of diseases. The study results highlight the effectiveness of quantum methods in agriculture and provide new tools for more accurate diagnosis of infectious plant diseases. The prospects for introducing this approach into agriculture mean the possibility of improving yields, reducing the use of chemicals and ensuring food security.