Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications.

The ‘Diaoganxing’ is the experimental material, with natural grass cover as the control, to compare the effects of 5 different mulching materials. The aim was to identify the most effective mulching type for improving orchard microenvironments and fruit quality. The results demonstrated that waterproof, breathable film and reflective film significantly enhanced orchard microenvironments and fruit quality (p ≤ 0.05). Specifically, the waterproof, breathable film effectively regulated soil temperature and moisture, reducing soil temperature by 4.60% and increasing soil moisture by 17.09% in the 0–60 cm soil layer. Meanwhile, the reflective film optimized light distribution in the mid-lower canopy, increasing light intensity by 161.04–208.71% and reflectance by 2.6–3.3 times. In terms of fruit quality, the reflective film accelerated ripening by 10 d, increased carotenoid content by 15.34%, and achieved a peel color index (CCI) of 6.23. On the other hand, the waterproof breathable film advanced maturation by 7 d and significantly improved vitamin C, soluble sugar, and soluble solids content by 23.26%, 30.77%, and 12.76%, respectively. This study provides a scientific basis for the efficient and high-quality production of apricots in southern Xinjiang through the use of mulching practices.