Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models

Abstract

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Code and data availability

The article describes UAV photogrammetry of maize fields and CSM analysis, but no public phenotype dataset, image repository, or author analysis code deposit is mentioned. Supplemental material (figures/tables) is hosted behind the article DOI and is not a paper-specific public data or code asset. Mentioned tools (RTK,

No evidence-backed public reproduction asset is currently recorded.