Segmentation of Porous Structure in Carbonate Rocks with Applications in Agricultural Soil Management: A Hybrid Method Based on the UNet Network and Kriging Geostatistical Techniques

In the context of soil management, the porous structure present in these systems plays a relevant role due to its capacity to store and transport water, nutrients, gases, and provide root fixation. A detailed and precise analysis of these structures can assist specialists in determining specific agricultural solutions and management practices for each soil, depending on the characteristics of its porous structure. In this regard, this study presents a hybrid method for segmenting porous structures in micro computed tomography (micro CT) images of carbonate rocks, with a focus on applications in agricultural soil analysis and management. Initially, preprocessing steps such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram specification are applied in order to improve image contrast and uniformity. Subsequently, a UNet convolutional neural network is employed to identify pore contours, followed by the application of two geostatistical approaches, ordinary kriging and Universal Kriging, with the purpose of completing segmentation through the interpolation of unclassified regions. The proposed approach was evaluated using the dataset “16 Brazilian Pre Salt Carbonates”, which includes high-resolution micro CT images. The results show that the integration of UNet with ordinary kriging achieved superior performance, with 79.2% IoU, 93.3% precision, 81.7% recall, and 87.1% F1 Score. This method enables detailed analyses of pore distribution and the porous structure of soils and rocks, supporting a better understanding of inherent characteristics such as permeability, porosity, and nutrient retention in soil, thus contributing to more assisted agricultural planning and more efficient soil use strategies.