Naimin Kong

High summer temperatures, intense solar radiation, and restricted ventilation in arid desert regions often cause heat accumulation and spatially non-uniform temperature distributions in greenhouses, constraining sustainable protected agriculture. This study investigated a multi-span plastic greenhouse in Xinjiang, China, using a field-validated three-dimensional computational fluid dynamics (CFD) model. Nine combinations of side- and roof-vent openings were evaluated under sunny and cloudy conditions to compare crop-zone temperature and airflow characteristics. The results showed that side-vent opening primarily controlled outdoor-air inflow, airflow penetration, and crop-zone cooling, whereas roof-vent opening mainly affected upper-level heat removal and airflow continuity. Their performance therefore depended on coordinated opening ratios. Under sunny conditions, high-temperature zones above 45 °C developed near the roof and arch shoulders. Enlarging the side vents and appropriately matching the roof-vent opening effectively reduced heat accumulation. In the integrated crop-zone assessment, S3T3 achieved the lowest mean crop-zone temperature of 37.32 °C, 7.35% lower than that under S1T1. Its temperature standard deviation was σ = 0.012, and its cooling demand index was Y_c = 0.406, approximately 74.5% lower than that of S1T1. Optimizing side- and roof-vent combinations can therefore improve the greenhouse thermal environment without energy-intensive mechanical cooling, providing a basis for energy-efficient operation and sustainable protected crop production in extreme climates.

Purple celery (Apium graveolens L.) is a high-value functional vegetable rich in anthocyanins, but achieving a balanced optimization between biomass accumulation and nutritional quality in plant factories remains challenging due to species-specific spectral trade-offs, and current research lacks integrated light recipes that simultaneously optimize photosynthetic efficiency, antioxidant defenses, and functional quality while suppressing nitrate accumulation. This study evaluated five LED treatments over 30 days: white light (CK), monochromatic red (R), and red-to-blue photon flux density ratios of 5:5, 7:3, and 8:2. Red light induced stem elongation but suppressed root development. Conversely, 8R2B delivered the best performance, significantly elevating leaf, petiole, root, and total fresh biomass and increasing total dry weight by 68.8% over CK. It also markedly enhanced chlorophyll a, b, total chlorophyll, and carotenoids, as well as improved PSII quantum yield (YII), photochemical quenching (qP), and electron transport rate (ETR), while reducing non-photochemical heat dissipation (NPQ) by 61.0%, indicating superior light-use efficiency. Furthermore, 8R2B upregulated CAT and POD activities promoted soluble sugars (up 688.4% in leaves), soluble proteins, and ascorbic acid accumulation, yet reduced leaf nitrate by 28.5%. Principal component analysis confirmed 8R2B as the most effective formulation across all growth, photochemical, defense, and quality traits. These findings provide a novel, evidence-based 8:2 red-to-blue LED light recipe for high-yield, high-quality, and commercially viable purple celery production in controlled environment agriculture.

In agricultural production, plant diseases and pests are among the major threats to crop yield and quality. Existing agricultural pest and disease identification methods have problems such as small target scales, complex background environments, and unbalanced sample distributions. This paper proposes a lightweight improved target detection model, YOLOv5s-LiteAttn. Based on YOLOv5s, the model introduces GhostConv and Depthwise Conv to reduce the number of parameters and computational complexity, and it combines CBAM and Coordinate Attention mechanisms to enhance the network’s feature representation capability. Experimental results show that, compared with the basic YOLOv5s model, the number of parameters of the improved model is reduced by 22.75%, and the computational load is reduced by 16.77%. At the same time, mAP@0.5–0.95 is increased by 3.3 percentage points, and recall is improved by 1.1 percentage points. In addition, the inference speed increases from 121 FPS to 142 FPS at an input resolution of 640 × 640, further confirming that the proposed model achieves a favorable trade-off between accuracy and efficiency. The average precision of YOLOv5s-LiteAttn is 97.1%, which outperforms the existing mainstream lightweight detection models. Moreover, an independent test set containing 4328 newly collected field images was established to evaluate generalization and practical applicability. Despite a slight performance decrease compared with the validation results, the model maintained an mAP@0.5–0.95 of 95.8%, significantly outperforming the baseline model, thereby confirming its robustness and cross-domain adaptability. These results confirm that the model has high precision and is lightweight, making it effective for the detection of agricultural diseases and pests.

To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum relative errors between simulated and measured values were 6% and 9%, respectively. Significant spatial heterogeneity in both temperature and airflow was observed. Vertically, temperature rose with height; horizontally, it declined from the center toward the sidewalls. Under prevailing meteorological conditions, the daily maxima occurred at distinct elevations above the fan-vent outlets. Airflow was most vigorous near the vents, whereas extensive stagnant zones aloft reduced overall ventilation efficiency. These findings provide a quantitative basis for designing single-span plastic film greenhouses in China’s hot–humid regions, informing ventilation improvements, and guiding future optimization efforts.