Hyperspectral imaging for estimating leaf, flower, and fruit macronutrient concentrations and predicting strawberry yields.

ABSTRACT: Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R²_P) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R²_P = 0.64, 0.60, 0.81, and 0.30, and

SUBMITTER: Dung CD 

PROVIDER: S-EPMC10663281 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature