The outdoor spectral detection of winter jujube quality is affected by complex ambient light and surface heterogeneity, resulting in limited inversion accuracy. To address this problem, this study proposes a correction method for outdoor spectral inversion based on the bidirectional polarization reflectance distribution function (BPDF) model. It was used to enhance the detection accuracy of water content and soluble solid (SSC) content of winter jujube. Experimentally, 900–1750 nm hyperspectral data of ripe winter jujube samples were collected at non-polarization and 0°, 45°, 90°, and 135° polarization azimuths. The spectra were inverted using four semi-empirical BPDF models, Nadal–Breon, Litvinov, Maignan and Xie–Cheng, and the corrected spectra were obtained by mean fusion. The quality prediction models are subsequently combined with the competitive adaptive reweighting algorithm (CARS) and partial least squares (PLS). The results showed that the modified spectra significantly optimized the prediction performance. The prediction set correlation coefficients (Rp) of the water content and SSC models were improved by 10–30% compared with the original spectra. The percentage of models with RPIQ values greater than 2 increased from 40% to 60%. Among them, the Litvinov model performs outstandingly in the direction of no polarization and 135° polarization, with the highest Rp of 0.8829 for water content prediction and RPIQ of 2.54. The Xie–Cheng model has an RPIQ of 2.64 for SSC prediction at 90° polarization, which shows the advantage of sensitivity to the deeper constituents. The different models complemented each other in multi-polarization scenarios. The Nadal–Breon model was suitable for epidermal reflection-dominated scenarios, and the Maignan model efficiently coupled epidermal and internal moisture characteristics through the moisture sensitivity index. The study verifies the effectiveness of the spectral correction method based on the BPDF model for outdoor quality detection of winter jujube, which provides a new path for the spectral detection of agricultural products in complex environments. In the future, it is necessary to further optimize the dynamic adjustment mechanism of the model parameters and improve the ability of environmental interference correction by combining multi-source data fusion.

Moisture content is one of the key indicators for evaluating the quality of apricots. When moisture levels fluctuate over an excessively wide range, scattering effects and absorption characteristics interfere with each other, making it difficult for a single model to achieve accurate predictions across the entire range. This study investigates precision modeling methods applicable to different moisture intervals based on spectral morphological features. By extracting the spectral morphological features of the water-sensitive regions (peak and valley) and conducting Pearson correlation analysis, the spectral morphological feature parameters with relatively strong correlations were selected, and they were combined with the characteristic bands to construct a segmented model for water content intervals. The results indicate that spectral morphological features of apricots within the 25–40% and 40–55% moisture range exhibit a certain correlation with moisture content. A weak correlation is observed in the 55–70% moisture range. After preliminary fusion modeling of spectral morphological features and characteristic bands for apricots across different moisture ranges, further analysis revealed that moisture content models based on valley morphology features and characteristic bands outperformed those based on peak morphology features and characteristic bands, demonstrating superior representational capability. By establishing a fusion model based on the spectral morphological parameters selected through Pearson’s method and the characteristic bands, the detection accuracy and model stability in the 25–70% moisture content range have been effectively improved. Among all the models covering different moisture content ranges, the model for the 55–70% moisture content range has the best prediction effect. The correlation coefficient of its prediction set reaches 0.8723, and the Ratio of Performance to Interquartile Range (RPIQ) is 2.5220, indicating that this range is the most suitable for establishing a high-precision quantitative moisture content detection model. This research effectively solved the problem of spectral response distortion caused by wide variations in moisture content and improved the prediction accuracy of the moisture content detection model for apricots.

Agricultural robots and unmanned farmland management require real-time and precise parsing of crop leaves at the edge to support variable application of pesticides, seedling condition monitoring, and phenotypic analysis. However, the field environment features drastic changes in light, leaf occlusion, and interference from background weeds, which can cause semantic fragmentation and boundary artifacts in lightweight models. This paper presents ES2-LeafSeg, a lightweight framework for leaf semantic segmentation tailored for edge deployment. The method employs EfficientNetV2 as the backbone encoder and introduces the State Space Semantic Enhancement Module (S2FEM) on skip connection features, modeling long-range dependencies and suppressing local texture noise through SSM pooling in row and column directions. Meanwhile, a cross-scale decoder (CSD) and a global context transformation (GCT) are designed to achieve multi-scale semantic fusion and boundary refinement. On the three-class segmentation task of the SoyCotton dataset, ES2-LeafSeg achieved mIoU of 0.817, mDice of 0.869, Fβw of 0.925, and MAE of 0.011, outperforming multiple classic and recent baselines while maintaining 23.67 M parameters and 49.62 FPS. Ablation experiments further verified the complementary contributions of S2FEM and GCT to regional consistency and boundary quality.