The emitter is one of the most critical components in drip irrigation. The flow path geometry parameters have a significant effect on the emitter’s hydraulic performance and have a direct impact on the emitter’s irrigation uniformity and lifetime. The hydraulic characteristics of the emitter are the key indicators of its performance. However, obtaining the hydraulic characteristics of the emitter is complex. Typically, only a small number of calibrations are performed for specific equipment models, making it difficult to obtain the parameter. Therefore, limited data corresponding to the morphological parameters and the flow rate were simulated using the FLUENT software, and the influence of the characteristics was analyzeanalyzed, based on which a flow rate prediction model was constructed using the ensemble learning (CatBoost) model. The extended data set was generated by stochastic simulation and parameter fitting. The flow index and flow coefficient prediction model were built and evaluated using the CatBoost model again with the augmented data set as a benchmark. The results show that the significant correlation between the geometric structure and the flow index and flow coefficient provides the basis for the correlation model. CatBoost can fit the complex nonlinear relationships between the parameters well, achieving excellent simulation accuracy for the flow rate (R² = 0.9987), flow index (R² = 0.9961), and flow coefficient (R² = 0.9946), where the path width has the highest importance score in the model construction for the flow index (score = 55.97) and flow coefficient (score = 45.2). Furthermore, the CatBoost models used in this study achieved the best prediction results compared to seven typical models (XGBoost, Bagging, Random Forest, Tree, Adaboost, and KNN).

The light intensity is low in haze weather, and the facility is in a weak light environment for a long time. As a functional film, light conversion film (LCF) can improve the light conversion performance and is conducive to regulating the environment in the facility to promote crop growth. It can be seen from the test that the light transmittance of LCF under visible light conditions (400–780 nm) is 8.67% higher than that of ordinary film (OF), with stronger light transmittance. In the red–orange light band (600–700 nm), the LCF is 1.3% higher than that of the OF. Through the detection of irradiance, it was found that the irradiance was outdoor environment > LCF > OF in any weather. A two-year greenhouse experiment was conducted to study the effect of LCF on the whole growth process of facility agriculture (environment-soil-crop) under weak light. It is found that LCF reduces the air humidity by 0.47~2.83%; it has an obvious warming effect on the surface soil of greenhouse, and it is linearly correlated with temperature. In terms of crop growth, LCF significantly (p < 0.05) increased the photosynthetic rate at heading stage, and finally increased the yield, total soluble sugar and reduction-type Vitamin C by 8.97–39.53%, 9.22–30.14%, and 1.41–21.09%, respectively. In addition, considering the frequent haze weather in North China, the use of LCF can improve air temperature, CO₂ concentration, photosynthetically active radiation (PAR), and soil temperature, and it can effectively deal with the challenge of weak light. In conclusion, LCF can improve the facility environment and improve crop yield and quality, indicating that the implementation of LCF has potential benefits in solving crop yield reduction and quality decline in haze weather. In addition, as the main component of LCF, rare earth materials are a new type of clean energy, which can effectively promote the sustainable development of the agricultural ecosystem.

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U²Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U²net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R²) of 0.953. The final R² between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize.