Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Abstract

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

Plant phenotyping relevance

UASリモートセンシングによる作物形質（NUE関連形質・バイオマス）推定の精度、センサー、信号処理、飛行条件、校正をレビューしており、フェノタイピング手法が中心である。

Code and data availability

This is a review/meta-analysis. The paper's meta-analytic dataset (Appendix S1) and supplementary figures/tables are referenced as downloadable supplementary materials, but the supplement URL (https://www.mdpi.com/article/10.3390/rs16050838/s1) is not among the allowed URLs, and no authors' public code repository or de

No evidence-backed public reproduction asset is currently recorded.