Predicting Carbohydrate Concentrations in Avocado and Macadamia Leaves Using Hyperspectral Imaging with Partial Least Squares Regressions and Artificial Neural Networks

Abstract

Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.

Plant phenotyping relevance

葉のハイパースペクトル画像から炭水化物濃度という植物形質を推定するモデルを開発・比較しており、非破壊的な表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks describe hyperspectral imaging of avocado and macadamia leaves and PLSR/ANN model development, but contain no data availability statement, no public dataset or code deposit, and no author-provided URLs. Analysis was performed in proprietary software (Unscrambler, SpectrononPro) with no mention of a.

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