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.

Natural variation in the Arabidopsis thaliana root and shoot response to boron deficiency reveals sensitive and responsive phenes and phylogenetic and geographic clustering of boron efficiency adaptations

“…RGB green also correlated with BEIshoot across all imaging days, with Pearson's correlation coefficients ranging from 0.32 at day 9 to 0.58 at day 16 (all p < 0.001). Such a phene could be a useful measure for early detection of plant Bdeficiency, assuming a detection system capable of differentiating B-deficiency from other deficiencies such as nitrogen could be engineered (Zhang et al, 2024). Mean L values of the LAB colour model and V values of the HSV colour model also followed similar patterns to RGB green, and so these phenes could also be fed into any model designed for early B-deficiency detection.…”