Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments.

ABSTRACT: Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower cultivation and evaluated 14 soil physio-chemical features for constructing fine-resolution maps. The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test set

SUBMITTER: Sharma M 

PROVIDER: S-EPMC11696571 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature