The aim of this study was to accurately meet the demand of real-time detection of seedling shortage in large-scale seedling production and solve the problems of low precision of traditional models and insufficient adaptability of mainstream lightweight models. This study proposed a Light-YOLO-Pepper seedling shortage detection model based on the improvement of YOLOv8n. This model was based on YOLOv8n. The SE (Squeeze-and-Excitation) attention module was introduced to dynamically suppress the interference of the nutrient soil background and enhance the features of the seedling shortage area. Depth-separable convolution (DSConv) was used to replace the traditional convolution, which can reduce computational redundancy while retaining core features. Based on K- means clustering, customized anchor boxes were generated to adapt to the hole sizes of 72-unit (large size) and 128-unit (small size and high-density) seedling trays. The results show that the overall mAP@0.5, accuracy and recall rate of Light-YOLO-Pepper model were 93.6 ± 0.5%, 94.6 ± 0.4% and 93.2 ± 0.6%, which were 3.3%, 3.1%, and 3.4% higher than YOLOv8n model, respectively. The parameter size of the Light-YOLO-Pepper model was only 1.82 M, the calculation cost was 3.2 G FLOPs, and the reasoning speeds with regard to the GPU and CPU were 168.4 FPS and 28.9 FPS, respectively. The Light-YOLO-Pepper model was superior to the mainstream model in terms of its lightweight and real-time performance. The precision difference between the two seedlings was only 1.2%, and the precision retention rate in high-density scenes was 98.73%. This model achieves the best balance of detection accuracy, lightweight performance, and scene adaptability, and can efficiently meet the needs of embedded equipment and real-time detection in large-scale seedling production, providing technical support for replanting automation.

Both abiotic and biological stresses reduce the quality and quantity of cultivated plants. In order to observe the response of cucumber plants to potassium stress and cucumber downy mildew infestation, two different levels of downy mildew infestation, B0 (disease infestation) and B1 (disease-free infestation), and three fertilizer requirement levels of potassium fertilization, T1 (K-50%), T2 (K-100%) and T3 (K-150%), were applied in the greenhouse. Results show that the photosynthetic rate and stomatal conductance of cucumber plants leaves treated with B1T2 had an increasing trend, and the increase in stomatal conductance was more significant. The intercellular CO₂ concentration of cucumber leaves treated with B1T2 showed no significant difference. The plant height of cucumbers treated with B0T1, B0T2, B0T3, B1T1 and B1T3 decreased by 32.21%, 16.93%, 21.59%, 53.54% and 6.31%, respectively, compared with that treated with B1T2. The leaf area of cucumbers treated with B0T1, B0T2, B0T3, B1T1 and B1T3 decreased by 27.85%, 15.97%, 21.47%, 7.29% and 18.5%, respectively, compared with that treated with B1T2. The stem diameter of cucumbers treated with B0T1, B0T2, B0T3 and B1T1 decreased by 13.43%, 8.72%, 6.01% and 7.06%, respectively, compared with that treated with B1T2. The stem diameter of cucumbers treated with B1T3 increased by 6.83% compared with that treated with B1T2. The root total length, root surface area, root average diameter, root total volume and total root tips of cucumber plants were significantly different under different experimental conditions (p < 0.05). This study provides a theoretical basis for cucumber stress resistance cultivation in greenhouses and has important practical significance to ensure the sustainable development of the greenhouse cucumber industry.

In order to understand the effects of biotic stress and abiotic stress on hormone metabolism and substance accumulation in cucumber, two different infection levels of downy mildew were set, B0 (diseased) and B1 (disease-free), and T1 (K-50%: 50% of normal potassium application), T2 (K-100%: normal potassium application), and T3 (K-150%: 150% of normal potassium application). The results show that the hormone metabolism in the cucumber plants was measured on the 15th day after transplanting. Compared with the B1T2 treatment, the cane sugar contents in the B0T1, B0T2, B0T3, B1T1, and B1T3 treatments decreased by 35.87%, 23.29%, 25.41%, 12.98%, and 6.05%; the soluble sugar contents in the B0T1, B0T2, B0T3, B1T1, and B1T3 treatments decreased by 36.16%, 27.13%, 31.97%, 9.89%, and 7.84%; the hydrogen peroxide (H₂O₂) contents in the B0T1, B0T2, B0T3, and B1T1 treatments increased by 30.59%, 21.08%, 14.74%, and 9.2%; the catalase (CAT) contents in the B0T1, B0T2, B0T3, B1T1, and B1T3 treatments decreased by 53.78%, 36.57%, 47.18%, 34.75%, and 16.39%; the superoxide dismutase (SOD) contents in the B0T1, B0T2, B0T3, and B1T1 treatments increased by 30.28%, 22.59%, 12.9%, and 13.17%; and the polyphenol oxidase (PPO) contents in the B0T1, B0T2, B0T3, and B1T1 treatments increased by 39.38%, 25.27%, 4.49%, and 23.05%, respectively. Compared with the B1T2 treatment, the malondialdehyde (MDA) contents in the B0T1, B0T2, B0T3, B1T1, and B1T3 treatments increased by 82.78%, 23.07%, 60.21%, 74.55%, and 25.12%. Compared with the B1T2 treatment, the hydrogen peroxide (H₂O₂), superoxide dismutase (SOD), and polyphenol oxidase (PPO) contents in the B1T3 treatment decreased by 16.11%, 4.36%, and 4.43%. On the 20th day after transplanting the cucumber plants, downy mildew infection and potassium stress had significant effects on the dry and fresh weights of the leaves, stems, and roots (p < 0.05). The B0T1 treatment had the greatest effect on the dry and fresh weights of the leaves, stems, and roots of the cucumber plants. The B1T3 treatment had the least effect on the dry and fresh weights of the leaves, stems, and roots of the cucumber plants. This research can provide a theoretical basis for the study and cultivation of cucumber stress resistance.

Nitrogen is a critical nutrient that influences plant growth and resistance to pathogens; however, its impact on disease dynamics, particularly downy mildew infection, and the associated physiological responses in cucumber during early growth stages remains poorly understood. To evaluate the combined effects of downy mildew (caused by Pseudoperonospora cubensis) infection and nitrogen application on cucumber growth and physiological traits during the seedling and vine development stages, two downy mildew treatments— infected (B0) and non-infected(B1)—and three nitrogen levels—T1 (N-50%), T2 (N-100%), and T3 (N-150%)—were applied. Significant differences were observed between all treatments (p < 0.05). Among them, the B1T3 treatment had the most pronounced stimulatory effect, particularly on growth parameters (such as plant height, stem diameter, and leaf area). Without any disease infection (B1), the B1T2 treatment showed an increasing trend in photosynthetic rate and a more notable rise in stomatal conductance. In contrast, with downy mildew infection (B0), photosynthetic rates declined under B0T1 and B0T2. Moreover, with downy mildew infection (B0), the intracellular CO₂ concentration, stomatal conductance, and transpiration rate of cucumber leaves decreased in the B0T1, B0T2, and B0T3 treatments. Plant height, stem diameter, and leaf area responded variably to nitrogen levels and downy mildew infection. The total root length, root surface area, average root diameter, total root volume, and total root tips of cucumber plants were significantly different under different experimental conditions (p < 0.05). Consequently, this study provides a theoretical basis for stress-resistant cucumber cultivation in greenhouses and has practical implications for advancing the sustainable development of the greenhouse cucumber industry.

Rice–wheat combine harvesters face coupled traction and material-processing loads whose variability complicates energy saving without reducing throughput or harvest quality. This review connects five perspectives: intelligent sensing, electrified actuation, hybrid powertrain architecture, field-condition prediction, and energy management. It examines interfaces under common quality and battery energy boundaries. Forward crop sensing, feed-rate monitoring, and component-load observation provide complementary information, while independent electric drives make drum, fan, and sieve settings more controllable. Series, parallel, and power-split architectures offer different compromises between controllability, conversion losses, packaging, and sustained-load performance. Direct evidence remains limited: one field prototype reported 19.5% lower fuel consumption than a conventional harvester, a study-specific result that does not establish a general saving range. Evidence from tractors and other vehicles supports transferable methods but cannot replace rice–wheat validation. A comparison matrix examines rule-based, instantaneous, optimal, predictive, and learning-based energy management. The framework couples fuel use and clean-grain throughput with loss, breakage, impurity, and state-of-charge constraints, and outlines life-cycle cost and carbon accounting. It is methodological rather than a new numerical assessment. The priorities are synchronized field duty cycles, uncertainty-aware coordination, thermal and fault validation, and comparisons across sites and seasons. The synthesis supports powertrain design and controller development for more energy-efficient and intelligent harvesting.