Using Artificial Intelligence Algorithms to Analyze Chromatic Attributes for Soil Quality Indicators

The aim of this study is to estimate soil quality using observable soil color, thereby simplifying the assessment process that traditionally requires expert intervention and extensive analysis. A total of 324 soil samples were collected from a depth of 0-20 cm in the Konya & Ccedil;umra Plain. These samples underwent color readings and principal component analysis. To estimate soil quality, three different scoring methods Soil Management Assessment Framework (SMAF), (Comprehensive Assessment of Soil Health) CASH, and Linear scoring were employed. The soil quality indicators identified by the analysis include clay, organic carbon, active carbon, calcium, available phosphorus, potassium, available water capacity, and potentially mineralizable nitrogen. The average soil quality scores calculated using SMAF, CASH, and Linear scoring were 0.73, 0.43, and 0.65, respectively. Artificial Neural Network (ANN) analysis revealed R2 values of 0.18 for SMAF, 0.32 for CASH, and 0.70 for Linear scoring. The study shows that soil color can be used to predict soil quality with a high degree of accuracy, with the Linear scoring function being the most effective for soil quality assessments. The results highlight the potential of artificial intelligence (AI) algorithms in facilitating rapid and efficient prediction of soil quality. By leveraging the synergy between observable soil characteristics and advanced AI methodologies, this research simplifies soil quality assessment and enables more accessible and scalable environmental analysis.