Detection of Bare Soil in Hyperspectral Images Using Quantum-Kernel Support Vector Machines

Satellite imaging brings exciting opportunities in an array of fields, with precision agriculture being a notable example. Soil analysis at scale with the use of Earth observation satellites coupled with on-board and on-the-ground artificial intelligence algorithms offers actionable items that may be exploited by practitioners to optimize their operations, including the fertilization process. Here, bare soil detection is a pivotal step in the processing chain to limit the detailed analysis to the areas of interest. In this paper, we tackle this task with quantum-kernel support vector machines and verify the utility of quantum machine learning in practical Earth observation. Our experimental study, performed over a real-world hyperspectral scene, indicates that the proposed quantum-kernel models are competitive with well-established classical support vector machines, as well as with approaches based on thresholding spectral indices that are widely exploited in the field.