AUTHOR=Zhang Chao , Lu Xinyi , Zhang Haolei , Cui Hongwei , Ma Hao , Ji Jiangtao , Ding Tianhang 

TITLE=Multispectral UAV imaging and machine learning for estimating wheat nitrogen nutrition index

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1709459

DOI=10.3389/fpls.2025.1709459

ISSN=1664-462X

ABSTRACT=IntroductionMonitoring nitrogen nutrition indices is crucial for assessing current wheat growth conditions and guiding nitrogen fertilizer application.MethodsTo estimate the wheat nitrogen nutrition index (NNI) and explore the effects of planting density and nitrogen application rates on NNI, this study employed UAVs to capture multispectral canopy imagery of wheat at key growth stages (tillering, jointing, booting, and filling) under varying planting densities and nitrogen application rates. Vegetation indices were selected using Pearson correlation and feature importance analysis. A Bayesian optimized random forest model was constructed to estimate the NNI.ResultsExperimental results indicate that vegetation indices DVI, MDD, NGI, MEVI, NDVI, EVI, and ENDVI exhibit strong resistance to interference, enabling the construction of highly robust models. The NNI estimation model developed under nitrogen application level N2 (210 kg/hm2) demonstrated optimal performance, with R2 and RMSE values of 0.785 and 0.137, respectively. The NNI estimation model constructed at planting density P1 (1 million plants/hm2) was optimal, with R2 and RMSE of 0.716 and 0.158, respectively. It was also found that NNI generally exhibited an initial increase followed by a decrease as planting density increased.DiscussionThe research findings systematically reveal the patterns of planting density and nitrogen application levels affecting wheat NNI. The constructed NNI estimation model plays a crucial role in assessing wheat growth status and also provides reference for rationally determining planting density and nitrogen application levels for spring wheat.