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.

Plant phenotyping relevance

UAV空撮とフォトグラメトリによる作物表面モデル（CSM）を用いたトウモロコシ生育差・収量変動の検出時期を検証しており、表現型取得法が研究の中心である。

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