Proper postharvest storage preserves horticultural products, including tea, until they can be processed. However, few studies have focused on the physiology of ripening and senescence during postharvest storage, which affects the flavor and quality of tea. In this study, physiological and biochemical indexes of the leaves of tea cultivar ‘Yinghong 9′ preserved at a low temperature and high relative humidity (15–18 °C and 85–95%, PTL) were compared to those of leaves stored at ambient conditions (24 ± 2 °C and relative humidity of 65% ± 5%, UTL). Water content, chromatism, chlorophyll fluorescence, and key metabolites (caffeine, theanine, and catechins) were analyzed over a period of 24 h, and volatilized compounds were determined after 24 h. In addition, the expression of key biosynthesis genes for catechin, caffeine, theanine, and terpene were quantified. The results showed that water content, chromatism, and chlorophyll fluorescence of preserved leaves were more similar to fresh tea leaves than unpreserved tea leaves. After 24 h, the content of aroma volatiles and caffeine significantly increased, while theanine decreased in both groups. Multiple catechin monomers showed distinct changes within 24 h, and EGCG was significantly higher in preserved tea. The expression levels of CsFAS and CsTSI were consistent with the content of farnesene and theanine, respectively, but TCS1 and TCS2 expression did not correlate with caffeine content. Principal component analysis considered results from multiple indexes and suggested that the freshness of PTL was superior to that of UTL. Taken together, preservation conditions in postharvest storage caused a series of physiological and metabolic variations of tea leaves, which were different from those of unpreserved tea leaves. Comprehensive evaluation showed that the preservation conditions used in this study were effective at maintaining the freshness of tea leaves for 2–6 h. This study illustrates the metabolic changes that occur in postharvest tea leaves, which will provide a foundation for improvements to postharvest practices for tea leaves.

Decreasing the temperature of a greenhouse in summer is very important for the growth of plants. To investigate the effects of a roof sprinkler on the heat environment of a greenhouse, a three-dimensional symmetrical model was built, in which a k-ε (k-epsilon) turbulent model, a DO (Discrete Ordinates) irrational model, a Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm, and a multiphase model were used to simulate the effects of the roof sprinkler, at different flow rates. Based on the simulation results, it was found that the temperature could be further reduced under a proper sprinkle rate, and the temperature distribution in the film on the roof was more uniform. A test was conducted to verify the accuracy of the model, which proved the validity of the numerical results. The simulation results of this study will be helpful for controlling and optimizing the heat environment of a greenhouse.

The feeding behavior of poultry at a young age is very likely to determine the final growth of the chicken, and can also reflect the feeding status and the health status. It is also of great significance for breeding. This paper describes the development and testing of a real-time automated system and a single passage for monitoring the individual feed intake and body weight of poultry. This system integrates an automatic feeding mechanism, an RFID reading module, a weight scale, a feed scale, and a drop weighting scale. The system uses analog circuits and digital filtering to improve the weighing accuracy, and directly measures the amount of feed falling to the ground using a high-precision weighing scale. Through the sequential opening and closing of a sliding door, group-housed chickens are separated so that the young ones are fed individually. As a result of the experiment, we learned that a young chicken grows at a rate of about 8.88 g/day, and the food conversion rate (FCR) is about 4.4, which proves that this system matches the single passage and can accurately monitor the body weight change and feeding behavior of young chickens.

The traditional method used to determine the moisture content of tea leaves is time consuming and destructive. To address this problem, an effective and non-destructive prediction method based on near-infrared spectroscopy (NIRS) is proposed in this paper. This new method combines discrete wavelet transforms (DWT) with the bootstrap soft shrinkage algorithm (BOSS). To eliminate uninformative or interfering variables, DWT is applied to remove the noise in the spectral data by decomposing the origin spectrum into six layers. BOSS is used to select informative variables by reducing the dimensions of the sub-layers’ reconstruction spectrum. After selecting the effective variables using DWT and BOSS, a prediction model based on partial least squares (PLS) is built. To validate effectiveness and stability of the prediction model, full-spectrum PLS, genetic algorithm PLS (GA-PLS), and interval PLS (iPLS) were compared with the proposed method. The experiment results illustrate that the proposed prediction model outperforms the other classical models considered in this study and shows promise for the prediction of the moisture content in Yinghong No. 9 tea leaves.

Cold-storage containers are widely used in cold-chain logistics transportation due to their energy saving, environmental protection, and low operating cost. The uniformity of temperature distribution is significant in agricultural-product storage and transportation. This paper explored temperature distribution in the container by numerical simulation, which included ventilation velocity and the fan location. Numerical model/numerical simulation showed good agreement with experimental data in terms of temporal and spatial air temperature distribution. Results showed that the cooling rate improved as velocity increased, and temperature at 45 min was the lowest, when velocity was 16 m/s. Temperature-distribution uniformity in the compartment became worse with the increase in ventilation velocity, but its lowest temperature decreased with a velocity increase. With regard to fan energy consumption, the cooling rate of the cooling module, and temperature-field distribution in the product area, velocity of 12 m/s was best. Temperature standard deviation and nonuniformity coefficient in the container were 0.87 and 2.1, respectively, when fans were located in the top four corners of the container. Compared with before, the average temperature in the box was decreased by 0.12 °C, and the inhomogeneity coefficient decreased by more than twofold. The results of this paper provide a better understanding of temperature distribution in cold-storage containers, which helps to optimize their structure and parameters.

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.