Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images

The rapid and efficient estimation of aboveground biomass (AGB) in maize proves advantageous for growth assessment, grain quality and yield prediction, and timely field management. The unmanned aerial vehicle (UAV) imaging system's adaptable operation and its capability to capture images with comparatively high temporal and spatial resolutions underscore its potential for supplanting conventional techniques in crop AGB measurement. However, previous studies have predominantly focused on utilizing spectral, structural, textural, and vegetation indices extracted from UAV imagery to forecast crop AGB through linear models or machine learning methods. Frequently, these approaches lack essential mechanistic elucidation. This study seeks to formulate a three-dimensional (3D) AGB estimation model for maize inbred lines by utilizing plant height and canopy coverage data obtained from UAV digital images. The results indicated that: (1) The amalgamation of UAV digital imagery with the canopy height model (CHM) proficiently enabled the estimation of maize plant height. The coefficient of determination (R2) and root mean square error (RMSE) values for measured and estimated plant height across various growth stages were 0.93 and 0.17 m, respectively. (2) The maize AGB estimation models employing canopy coverage exhibited higher accuracy in comparison to those relying on plant height within the testing set, with the R2 value for the measured and estimated maize AGB across four genotypes all equal to or greater than 0.91. (3) The accuracy of the maize AGB estimation model, developed by incorpo-rating both comprehensive plant height and canopy coverage, surpassed that achieved by solely utilizing plant height or canopy coverage. In applying different genotypes across multiple years, the AGB estimation model presented in the study demonstrated both high accuracy and versatility. The utilization of UAV digital imaging technology offers an expedient and cost-effective approach for AGB in maize breeding.