It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R²) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.

Highly sensitive detection of daminozide using terahertz metamaterial sensor

Since the characteristics of plug seedlings affect the effectiveness of automatic transplanting, this study aimed to explore the effect of the addition of biochar into substrates on the growth of plug seedlings before and after transplanting. The physicochemical properties of substrates with 0%, 5%, 10%, 15%, 20%, and 25% biochar addition all met the requirements of seedling cultivation. The growth trend, root systems, and mechanical properties of seedlings before transplanting and the leaf gas exchange parameters of seedlings after transplanting were measured in this study. The results indicated that the seedlings cultivated with 10% biochar added to the substrate achieved the best growth trend and physiological indices, and the root systems under this treatment were also stronger than those of other treatments, while the seedlings cultivated with 25% biochar treatment were the worst, with less than 22.23% of the growth seen in the 10% biochar treatment, and even less than 1.5% of the growth of the seedlings cultivated without biochar treatment. Since the strong root systems could enhance the mechanical properties of seedling pots, the seedling pots cultivated with 10% biochar added into the substrate possessed the best compression resistance properties, with the maximum value of 49.52 N, and could maintain maximum completeness after free-fall impacting, wherein the loss of root and substrate was only 8.22%. The analysis results of seedlings cultivated after impacting proposed that the seedlings with better growth trends and root systems before transplanting could obtain better leaf gas exchange parameters during the flower stage after transplanting, so the seedlings cultivated with 5%~10% biochar added into the substrate grew better after impacting and then transplanting. It was noticed that the seedlings cultivated with appropriate biochar added into the substrate were able to achieve the optimal growth parameters and mechanical properties before and after transplanting, which were better able to meet the requirements of automatic transplanting. Thus, this study can promote the development of automatic transplanting technology to some extent.

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.

Aquaponics is a new type of composite farming system, which combines aquaculture and hydroponics through ecological design to achieve scientific synergism. However, the effects of aquaponics on the welfare status and stress parameters of fish are unclear. In this study, 150 crucian carp with an average initial body weight of 7.06 ± 0.32 g were selected. Nine fish were randomly selected as the control group (NC group and hypoxia group) for stress, antioxidant and gene expression parameters after acclimation and hypoxia stress, respectively. The remaining crucian carps were randomly divided into three experimental groups with 20 crucian carps in each group (T₀, T₁ and T₂, respectively), with three replicates. The fresh weight of the lettuce root in the T₂ group had no significant effect (p > 0.05). Compared with the control group (T₀ and T₁), there were significant differences in the specific growth rate, the weight gain rate, the fresh leaf weight, the chlorophyll content and the water quality parameters in group T₂ (p < 0.05). Regarding the biochemical parameters, superoxide dismutase and catalase showed significant differences between the T₂ and T₁ groups (p < 0.05). At the same time, the HSP70 and Prdx3 genes were upregulated in the liver of the T₂ group compared to the NC group and the hypoxia group. The research suggests that aquaponics may reduce the hypoxia stress of crucian carp without affecting the growth of crucian carp and lettuce.

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.