Prediction of sugar beet yield and quality parameters with varying nitrogen fertilization using ensemble decision trees and artificial neural networks

Abstract

Nitrogen fertilization has a crucial role in sugar beet production, especially concerning root yield and quality. This study employed a machine learning approach to predict root yield and quality parameters based on the nutrient status of sugar beet leaves in relation to nitrogen fertilization. The field experiment included the following N fertilization treatments for sugar beet production: control (N0), presowing (N1 = 45 kg N ha−1) and presowing with top-dressing (N2 = 99 and 154.5 kg ha−1). Leaf samples were collected during the vegetation period in six intervals (May-Sept) to determine the levels of N, K and Na in the leaf dry matter. The machine learning regression based on ensemble decision trees and artificial neural network was used to determine the relationship of leaf samples based on varying N fertilization with yield parameters. Among the leaf elements analyzed, Na exhibited the highest average relative variable importance for root yield, sucrose content, and other quality parameters during the season with greater precipitation. In the season with less precipitation, N content at the beginning of July showed higher importance on root yield (74.6). The evaluated machine learning methods consistently achieved high accuracy across various combinations of input data and yield parameters, with a median R2 of 0.927 and a range from 0.842 to 0.998.