Application of quantum technology Variational Quantum Classifier in agriculture for classification of wheat varieties

This study proposes the use of Variational Quantum Classifier for automated classification of wheat varieties. A model trained on a large data set will be able to identify unique patterns and relationships between seed characteristics and cultivar membership. This will allow farmers and researchers to more accurately identify wheat varieties, which in turn can improve growing and crop management processes. This approach is justified not only by the need to optimize agricultural production, but also in the context of the use of advanced technologies to achieve precision and efficiency in the agricultural sector. As a result of this research, it is expected that the quality and sustainability of wheat production will improve, which is important for food security and sustainable agricultural development. The goal of the problem is to classify wheat varieties based on seed characteristics. VQC is trained on the training dataset and then evaluated on the test dataset. To evaluate the performance of the model, various metrics are used, such as accuracy, precision, recall, F1-score and Confusion Matrix.

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