Research Article | Open Access

Volume 2024 |Article ID 0191 | https://doi.org/10.34133/plantphenomics.0191

UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation

Yandong Yang,^1,4 Qing Li,^2,4 Yue Mu ,¹ Haitao Li,¹ Hengtong Wang,² Seishi Ninomiya ,^1,3 and Dong Jiang²

¹Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center for Modern CropProduction co-sponsored by Province and Ministry, State Key Laboratory of Crop Genetics and GermplasmEnhancement and Utilization, Nanjing 210095, China
²College of Agriculture, National TechniqueInnovation Center for Regional Wheat Production, Key Laboratory of Crop Ecophysiology, Ministry ofAgriculture, Nanjing Agricultural University, Nanjing 210095, China
³Graduate School of Agriculturaland Life Sciences, The University of Tokyo, Nishi-Tokyo, Tokyo 188-0002, Japan
⁴These authors contributed equally to this work

Abstract

Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou’s index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of −0.760 and −0.801, respectively. The accuracies of the yield (coefficient of determination [R2] = 0.616, root mean square error [RMSE] = 1.189 Mg/ha) and biomass estimation model (R2 = 0.798, RMSE = 1.952 Mg/ha) using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters. Therefore, the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.