Machine learning and computer vision technology to analyze and discriminate soil samples

Abstract

Soil texture is one of the most important elements to consider before planting and tillage. These features affect the product selection and regulate its water permeability. Discrimination of soils by determining soil texture features requires an intense workload and is time-consuming. Therefore, having a powerful tool and knowledge for texture-based soil discrimination could enable rapid and accurate discrimination of soils. This study focuses on presenting new models for 6 different soil sample groups (Soil_1 to Soil_6) based on 12 different machine learning algorithms that can be utilized for various problems. As a result, overall accuracy values were determined as greater than 99.2% (Trilayered Neural Network). The greatest accuracy value was found in Bayes Net (99.83%) and followed by Subspace Discriminant (99.80%). In the Bayes Net algorithm, MCC (Matthews Correlation Coefficient) and F-measure values were obtained as 0.994 and 0.995 for Soil_4 and Soil_6 sample groups while these values were 1.000 for other soil groups. Soil types can visually vary based on their texture, mineral composition, and moisture levels. The variability of this can be influenced by fertilization, precipitation levels, and soil cultivation. It is important to capture the images in soil conditions that are more stable. In conclusion, the present study has proven the feasibility of rapid, non-destructive, and accurate discrimination of soils by image processing-based machine learning.