Principal component analysis of UAV-derived vegetation indices and laboratory tissue nutrients for crop health assessment

Abstract. Remote sensing and laboratory assays can improve field-scale crop assessment and management. This exploratory pilot study analyses relationships between leaf tissue nutrients and UAV-derived normalised difference vegetation index (NDVI) using seventeen paired samples collected across a mixed crop trial. Tissue measures for nitrogen, phosphorus and potassium were standardised and entered into principal component analysis to reduce pairwise correlation and extract orthogonal nutrient axes. The first principal component explained 54.79% of variance, the second explained 34.10%, together accounting for 88.9%. Principal component scores for the first two axes were used in linear and polynomial regression models to predict NDVI. Model skill was assessed on training data and with leave-one-out cross-validation, and bootstrap resampling produced empirical confidence intervals for component loadings. Linear models built on principal components provided the most stable cross-validated performance, while polynomial expansions improved training fit but generalised poorly. These findings indicate that a low-dimensional nutrient representation can predict NDVI with reasonable stability and that combining spectral and biochemical data supports spatially explicit nutrient assessment. The study recommends expanded and stratified sampling, reflectance calibration and targeted spectral bands for follow-up studies, and external validation before wider applications.