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

• Published in: Computers and electronics in agriculture Vol. 212; p. 108076
• Main Authors: Varga, Ivana, Radočaj, Dorijan, Jurišić, Mladen, Markulj Kulundžić, Antonela, Antunović, Manda
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023
• Subjects: Accuracy assessment; Leaf samples; Machine learning; Precipitation; Sugar beet root yield; Precipitation; Accuracy assessment; Leaf samples; Sugar beet root yield; Machine learning
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2023.108076

•Sugar beet nitrogen fertilization was optimized using leaf nutrient status samples.•Root yield and yield quality parameters were used as training and test data.•Ensemble decision trees and artificial neural network regression were used.•Na had high variable importance for root yield and sucrose content.•The top accuracy had a median R2 of 0.927 and ranged from 0.842 to 0.998. 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.