Innovative quantum technologies in agriculture for assessing land fertility

Abstract

Agriculture is a key sector ensuring food security. In the face of modern challenges such as climate change and sustainable use of resources, it becomes necessary to introduce innovative technologies to improve the efficiency of agriculture. Assessing soil fertility plays a critical role in optimizing the use of fertilizers and resources. One innovative approach is the use of quantum technologies to assess soil fertility. Variational quantum chains (VQC) provide a unique opportunity to efficiently solve classification problems in the context of soil characterization data analysis. In this study, we used data on soil chemical and physical properties, including density, moisture, pH, nitrogen, phosphorus, and potassium. To build the VQC model, we converted these data into quantum states using various ansatzes such as ZZFeatureMap and RealAmplitudes. To compare the results, we used traditional classification methods such as support vector machine (SVM) and compared them with the results obtained using VQC. We split the data into training and test sets, trained the models on the training data, and evaluated their performance on the test data. The advantages and limitations of using variational quantum circuits in assessing soil fertility were discussed. The prospects for further development and improvement of the methodology were considered. Variational quantum chains represent a promising direction for the development of innovative methods for assessing soil fertility in agriculture. The results of our study highlight the potential of quantum technologies in agriculture and the need for further research in this direction.