Use of Machine Learning Techniques for Fertilization Traceability Discrimination via Core Quality Indicators of Korla...

Rational fertilization directly affects the fruit quality of the Korla fragrant pear. However, the variation patterns of fruit appearance and texture indicators under different N-P₂O₅-K₂O ratios are complex, and redundancy among high-dimensional indicators restricts the practical application of quality discrimination and fertilization traceability. In this study, Korla fragrant pear fruits harvested under eight fertilization treatments (including the control) were selected as research materials. Significant differences existed in nutrient composition and application rate among treatments: no N-P₂O₅-K₂O was applied in the CK treatment; for treatments H1-H7, nitrogen (N) application rate ranged from 396.36 to 524.2 g·plant^-1, phosphorus (P₂O₅) from 326.08 to 652.17 g·plant^-1, and potassium (K₂O) from 450.67 to 1200.08 g·plant^-1, with the most prominent differences observed in P-K ratios and application rates. On this basis, 12 appearance and flesh texture indicators were determined, including single-fruit weight, longitudinal diameter, transverse diameter, fruit shape index, pericarp thickness, sclereid content, hardness, adhesiveness, cohesiveness, springiness, gumminess and chewiness. Three machine-learning algorithms, namely Random Forest (RF), Extreme Learning Machine (ELM) and K-Nearest Neighbor (KNN), were used to construct fruit quality discriminant models. The results showed that the RF model achieved the optimal discriminative performance, with accuracy values of 0.876 and 0.865 for the training and validation sets, respectively. Seven core quality indicators, including sclereid content and longitudinal diameter, were screened via feature-importance intersection analysis. The reconstructed RF model based on this indicator set exhibited nearly no loss in discriminative accuracy despite a ~42% reduction in indicator quantity, providing theoretical and technical support for quality grading, fertilization traceability and precision fertilization of Korla fragrant pear.