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 (R 2 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 R 2 P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R 2 P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R 2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.

Plant phenotyping relevance

イチゴの葉・花・果実の栄養状態と収量という植物形質を、ハイパースペクトル画像とPLSRで推定・検証しており、表現型取得法の適用と性能評価が中心である。

Code and data availability

The paper's hyperspectral imaging data (leaf, flower, unripe/ripe fruit spectra), nutrient measurements, and PLSR models are not publicly deposited. The Data availability statement says data are available only upon request, and no authors' public code or data URL is provided. The supplementary DOCX is not shown to be a

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