Horticulturae 2025, 11(2), 161; https://doi.org/10.3390/horticulturae11020161 - 3 Feb 2025
Abstract
The ceteris paribus assumption that all features are equal except the one(s) being examined limits the reliability of nutrient diagnosis and fertilizer recommendations. The objective is to review machine learning (ML) and compositional data analysis (CoDa) tools to make nutrient management feature specific.
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The ceteris paribus assumption that all features are equal except the one(s) being examined limits the reliability of nutrient diagnosis and fertilizer recommendations. The objective is to review machine learning (ML) and compositional data analysis (CoDa) tools to make nutrient management feature specific. The average accuracy of the ML methods was 84% across the crops. The additive and orthogonal log ratios of CoDa reduce a D-parts soil composition to D-1 variables, alleviating redundancy in the predictive ML models. Using a Brazilian onion (Allium cepa) database, the combined CoDa and ML methods returned crop response patterns, allowing feature-specific fertilizer recommendations to be made. The centered log ratio (clr) diagnoses plant nutrients as a compositional nutrient diagnosis (CND). Using a Quebec database of vegetable crops, the mean variance of clr variables (
(This article belongs to the Section Plant Nutrition)
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