A Data-driven Approach to Generating Foliar Nutrient Interpretation Ranges and Machine Learning-based Interpretation for Petunia

Historically, leaf tissue standards have been developed and used to interpret foliar tissue analyses for the majority of horticultural crops to diagnose nutrient disorders. However, leaf tissue standards for petunia ( Petunia × hybrida ) are based on survey concentrations from small datasets. This study presents a novel method to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient, low, sufficient, high, and excessive for 11 essential elements based on 1420 data points. Data distributions were analyzed by fitting normal, Gamma, and Weibull distributions. Additionally, four machine learning algorithms J48 (a decision tree classifier), random forest (RF), which is a learning method that uses multiple decision trees, sequential minimal optimization (SMO), which is an optimization technique for support vector machines, and multilayer perceptron (MLP), which is a type of artificial neural network, were examined to determine if machine learning models could accurately classify foliar tissue analysis samples into the correct interpretation range. For all examined essential nutrients, J48 or RF yielded the highest classification accuracy compared with MLP or SMO. This study established the novel use of machine learning for interpreting petunia foliar nutrient analysis results with a higher accuracy rate than that of traditional statistical methods.