Supplemental figures for "Integrating Machine Learning and Remote Sensing to Determine Crop Nitrogen Content of Maize"

Abstract

This study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery. Several models were evaluated, with Random Forest demonstrating the best performance. The study emphasizes that combining vegetation indices enhances prediction accuracy more than using individual spectral bands. Results show reliable CNC estimation across growth stages, although predictions at early stages are less precise due to low canopy cover and soil interference. The approach allows for real-time, field-to-regional-scale nitrogen monitoring, supporting improved fertilizer management and precision agriculture decision-making.

Plant phenotyping relevance

UAV・衛星画像からトウモロコシの窒素含量を推定する機械学習・リモートセンシング手法の開発とモデル比較が中心であり、植物形質の取得方法に該当する。

Code and data availability

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