Quality grading of greenhouse lettuce requires rapid external appearance screening and nondestructive internal quality assessment. However, existing detection methods struggle to simultaneously evaluate both external and internal quality while maintaining efficiency, resulting in a lack of scientific and comprehensive integrated evaluation standards for current crop grading. To address this issue, this study leveraged the technical strengths of different sensors to construct separate models: an RGB image-based monitoring model for external quality and a hyperspectral-based estimation model for internal quality. Using a combined objective–subjective weighting method, this approach scientifically integrated external and internal quality monitoring indicators to establish a comprehensive evaluation method for greenhouse lettuce quality. The results demonstrate that features such as canopy projection area, compactness, and color components can be extracted from RGB images. Combined with Ridge regression, this approach achieves high-accuracy estimation of lettuce fresh weight and leaf area (R² ≥ 0.880). For intrinsic quality, by combining hyperspectral data with the CARS and SPA band selection algorithms, a Random Forest (RF)-based inversion model for chlorophyll, soluble sugar, protein, and vitamin C content was developed. The AHP-CRITIC method effectively resolved the weight imbalance caused by an excessive coefficient of variation in appearance indicators, thereby achieving the scientific integration of appearance and internal quality data. The grading outcomes of this integrated evaluation method were highly consistent with industry standards (kappa coefficient: 0.788). This approach establishes an effective link between the rapid monitoring of external and internal quality for comprehensive evaluation, providing a novel technical pathway and scientific basis for nondestructive post-harvest detection and automated grading of greenhouse vegetables.

Changes in atmospheric CO₂ concentration strongly affect the photosynthetic performance of C₃ plants. Cotton (Gossypium hirsutum L.), a major global cash crop, provides a suitable model to study CO₂ fertilization effects. While moderate CO₂ enrichment can promote growth and yield, the optimal regime for field-scale application remains unclear. In this study conducted in Xinjiang, China, CO₂ gas was dissolved in irrigation water at four concentrations (0.04, 0.08, 0.12, 0.16 kg·m^-³) and applied via a drip irrigation system. The effects on canopy CO₂ distribution, plant physiological responses, yield, and fibre quality were assessed. Drip-applied CO₂ solutions increased canopy CO₂ concentration by gradually releasing CO₂ from the soil, which in turn enhanced plant growth indicators (SPAD, AGB, LAI, plant height). Growth promotion followed a dose–response trend, with effects rising at lower concentrations and declining at higher levels. Yield analysis showed that lint yield increased by 1.9% and 8.4% under 0.04 and 0.08 kg·m^-³ treatments, respectively, compared with the control (p < 0.05). In contrast, 0.12 and 0.16 kg·m^-³ treatments reduced yield by 13.4% and 5.4%, respectively (p < 0.05). Fibre quality indicators remained within the optimal range across all treatments. Overall, 0.08 kg·m^-³ was identified as the most effective concentration, producing the highest yield while maintaining fibre quality. These findings provide a scientific basis for the field application of CO₂-enriched irrigation, offering a promising approach to enhance cotton productivity and the ecological sustainability of farmland systems.