A Precise Apple Quality Prediction Model Integrating Driving Factor Screening and BP Neural Network.

Abstract

Apple fruit quality is primarily determined by Vitamin C (VC), Soluble Saccharides (SSs), Titratable Acid (TA), and the Soluble Saccharides/Titratable Acid (SSs/TA). This study aims to establish a prediction model based on the Back Propagation (BP) neural network by analyzing the intrinsic relationships between these quality indicators and the photosynthetic physiological characteristics of fruit trees, providing a new method for the precise prediction and regulation of fruit quality. Using 'Fuji' apple as the material, fruit quality indicators, leaf photosynthetic parameters, canopy structure indicators, and carbon-water-nitrogen metabolism indicators were systematically measured. Correlation analysis was employed to identify key influencing factors, BP neural network models with different hidden layer structures were constructed, and the optimal feature subset was screened through feature importance analysis, single-factor sensitivity analysis, and ablation experiments, ultimately establishing a simplified and efficient prediction model. Pn, Gs, SPCI, and DUE showed significant positive correlations with VC, SS, and SS/TA, whereas N and NLT were significantly positively correlated with TA content. SUE was identified as a common core driving factor for VC, SS, and SS/TA. The BP neural network demonstrated strong predictive performance for the four quality indicators, with the optimal model achieving validation set R 2 values of 0.87, 0.86, 0.86, and 0.89, respectively. The simplified model developed through feature screening exhibited further improved performance: the validation set R 2 for the VC prediction model increased to 0.93, while MAE and MAPE decreased by 32% and 35%, respectively. Photosynthetic characteristics and nitrogen metabolism status of the fruit trees serve as key physiological foundations determining apple quality. The quality prediction model based on the BP neural network achieved high accuracy, and its predictive performance was significantly enhanced after feature refinement, providing an effective tool for precise apple quality prediction and smart orchard management.

Plant phenotyping relevance

リンゴ果実品質という植物形質を対象に、BPニューラルネットワーク、特徴量選択、感度分析、アブレーション実験を組み合わせた予測手法を開発・検証しており、形質推定法が研究の中心である。

Code and data availability

The paper's underlying phenotype/physiological measurement data are only available from the corresponding author upon request, and no public code, models, or raw data are deposited. The MDPI supplement (plants-14-03795-s001.zip) contains only supplementary tables of BP neural network model metrics under differenthidden

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