This study working multivariate statistical techniques to resolve soil nutrient data for crop categorization, focusing on the "Potato" and "Raagi" crops. The reasoning revealed highly meaningful differences in soil nutrient descriptions between these crop types, with distinguishing soil nutrients exhibiting solid variability. The Fisher Uninterrupted Discriminant Analysis demonstrated irregular discriminative power, gaining perfect crop separation. The confusion forge indicated extreme classification accuracy, accompanying "Potato" reaching 100% veracity and "Ragi" at 96.15%. The ROC value of 0.992 further validated the model's influence in crop discrimination. These findings focal point the utility of multivariate statistical approaches for crop categorization and selection based on soil mineral characteristics.
Author(s) Details:
Rajarathinam A.,
Department
of Statistics, Manonmaniam Sundaranar University, Tirunelvel-627 012, India.
Please see the link here: https://stm.bookpi.org/EIAS-V9/article/view/12651
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