Featured Application: Based on principal component analysis (PCA), an evaluation and grading model for fresh peaches was developed to provide guidance for the selection of fresh peaches for the consumer market. The approach and findings presented here may be useful for effective evaluation and grading during real-world fruit production; they can potentially improve processing efficiency, reduce costs, and minimize waste in an automated quality evaluation system. Abstract: Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar-acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch "Spring Belle") were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

The application of microclimate control technology in solar greenhouses under a dynamic solar heat load is of great significance for current research on energy consumption and clean efficiency in greenhouses. In particular, solar greenhouses use natural ventilation for the rapid exchange of energy and heat inside and outside the greenhouse. In this study, we proposed a coupling between the natural ventilation model and the computational fluid dynamics (CFD) model to investigate the relationship between ventilation and temperature in solar greenhouses, thus providing a more in‐depth understanding of greenhouse ventilation demands. The influence of vents on the greenhouse microclimate was simulated under dynamic solar thermal loads using CFD to propose a coupling between the natural ventilation model and the CFD model to investigate the relationship between ventilation and temperature in solar greenhouses. Furthermore, we analyzed the daily and annual greenhouse micro‐climates to provide a more in‐depth understanding of greenhouse ventilation demands. Results highlighted the impact of the outdoor wind speed and vent opening on greenhouse ventilation rates and temperatures, with a linear relationship between greenhouse ventilation rates and outdoor wind speed. The outdoor wind speed had little effect on the ventilation rate for reduced vent openings, while the indoor temperature decreased as outdoor wind speed increased. The average temperature in the greenhouse was observed to be greatly affected by wind speeds less than 3 m/s. Simultaneously, opening the greenhouse vent resulted in a fall in indoor temperatures, with openings less than 60% causing the greatest reduction, (the maximum temperature drift was determined as 9.33%). Practical Applications With the ability to conserve energy and reduce pollution, solar greenhouses are crucial to China's contribution and the global agricultural industry. Moreover, they are associated with improvements in the competitiveness of modern agriculture, particularly in the current era of rapidly increasing costs and pollution from fossil fuels and traditional energy. This study provides a basic reference for the natural ventilation of vents in the control of indoor air temperatures under dynamic solar thermal loads. The results can be used as a theoretical basis for the production management of solar greenhouses, and have an important practical significance for the subsequent automation and intelligent control of greenhouse environments.

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