Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

Accurate recognition of agricultural pests is crucial for effective pest management and reducing pesticide usage. In recent research, deep learning models based on residual networks have achieved outstanding performance in pest recognition. However, challenges arise from complex backgrounds and appearance changes throughout the pests’ life stages. To address these issues, we develop a multi-task learning framework utilizing the discriminative attention multi-network (DAM-Net) for the main task of recognizing intricate fine-grained features. Additionally, our framework employs the residual network-50 (ResNet-50) for the subsidiary task that enriches texture details and global contextual information. This approach enhances the main task with comprehensive features, improving robustness and precision in diverse agricultural scenarios. An adaptive weighted loss mechanism dynamically adjusts task loss weights, further boosting overall accuracy. Our framework achieves accuracies of 99.7% on the D0 dataset and 74.1% on the IP102 dataset, demonstrating its efficacy in training high-performance pest-recognition models.

South China has a climate characteristic of high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. This paper studied the effect of ventilation conditions on the spatial and temporal distribution of temperature and relative humidity in a Venlo greenhouse. Two ventilation conditions, with and without a fan-pad system, were studied. A GA + BP neural network was applied to predict the temperature and relative humidity in fan-pad ventilation in the greenhouse. The results show that the temperature in the Venlo greenhouse ranged from 15.8 °C to 48.5 °C, and the relative humidity ranged from 24.9% to 100% during the tomato-planting cycle. The percentage of days when the temperature exceeded 35 °C was 67.3%, and the percentage of days when the average relative humidity exceeded 70% was 83.7%. The maximum temperature differences between the three heights under NV (Natural Ventilation) and FPV (Fan-pad Ventilation) conditions were 3.4 °C and 4.5 °C, respectively. The maximum relative humidity differences between the three heights under NV and FPV conditions were 8.4% and 21.7%, respectively. The maximum temperature difference in the longitudinal section under the FPV conditions was 3.2 °C, while the relative humidity was 11.4%. The cooling efficiency of the fan-pad system ranged from 16.6% to 70.2%. The non-uniform coefficients of the temperature under the FPV conditions were higher than those under the NV conditions, while the nonuniform coefficients of the relative humidity were the highest during the day. The R², MAE, MAPE and RMSE of the temperature-testing model were 0.91, 0.94, 0.11, and 1.33, respectively, while those of relative humidity model were 0.93, 2.83, 0.10, and 3.86, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

Industrialized seeding production is one of the most important methods used to raise seedlings in a standardized and efficient manner. However, its consumption of light energy limits its development. In this paper, we studied the effects of LED light intensities (30 (L1), 60 (L2), 90 (L3), 120 (L4), 150 (L5), 180 (L6), and 210 (L7) µmol·m⁻²·s⁻¹) on the phenotypic parameters and health index of seedlings during different growth stages (cotyledon flattening (CF), one bud and one leaf (OBOL), and one bud and two leaves (OBTL)). Ten out of sixteen plants were selected to measure the parameters in every treatment. The control group was treated with 150 µmol·m⁻²·s⁻¹ all the time. The results show that the height of the L1-treated plants was greatest during the cotyledon flattening stage. The L7 treatment produced the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference, the values of which were 2.61 mm, 3.3 g, 60.67 cm², and 39.71 cm, respectively. During the one bud and one leaf stage, the L1 treatment produced the tallest plants. The L7 treatment resulted in the largest stem diameter, the greatest biomass, the largest leaf area, and the largest leaf circumference. During the one bud and two leaves stage, the plant height, stem diameter, leaf circumference, and biomass produced of the L6 treatment were the greatest, with values of 68.37 mm, 3.09 mm, 51.54 cm, and 4.47 g. L5 resulted in the largest leaf area (63.91 cm²). The seedling index of the three stages showed an inflection point at the L4 level. The experimental results showed that the light intensity required by the cucumber seedlings was 120 µmol·m⁻²·s⁻¹, 150 µmol·m⁻²·s⁻¹, and 120 µmol·m⁻²·s⁻¹ during the three stages, from the cotyledon flattening stage to the one bud and two leaves stage. The results provide references regarding the light required during the cultivation of cucumber seedlings.

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

This paper presents a valve-controlled pipeline humidification system aimed at achieving precise and uniform humidity regulation in the multi-layer cultivation environments of plant factories. Since grafted seedlings require stable humidity conditions for effective healing, the system was designed to enable fine-grained adjustments across different cultivation layers. A quadratic regression orthogonal rotational combination design was employed to investigate how valve opening angles affect mean relative humidity (MRH), and a regression prediction model was developed accordingly. The model exhibited strong predictive performance, achieving an R² of 0.9907 and an average relative error of only 0.67%. The optimal valve-opening angles were 60°, 50°, and 50°, respectively, which ensured that the MRH remained above 90% throughout operation. Experimental verification confirmed that the model accurately predicted humidity responses, while the proposed system improved uniformity by reducing the humidity variation from 6.1% to 0.3% and increasing the compliance rate from 58.3% to 100%. To enhance short-term humidity forecasting, three machine learning algorithms—Random Forest, XGBoost, and Transformer—were trained to predict humidity trends within a 6-h window. Among them, the RF model achieved the highest accuracy with an R² of 0.9543, outperforming the other models in both stability and precision. The main contribution of this study is the identification of the optimal valve-opening combination through a quadratic orthogonal rotation regression combination experiment. Additionally, the RF obtained the optimal machine learning model for predicting humidity within 6 h.