The sand media filter is a crucial component of micro-irrigation filtration systems. Investigating the effects of various factors on filtration performance and the migration patterns of sediment particles within the filter can enhance the healthy operation of sand media filters. This study, based on a sand media filter model, conducts indoor hydraulic experiments, selecting different experimental factors and observation indices for research under varying conditions. Results indicate that filter thickness and raw water concentration are positively correlated with the turbidity of filtered water samples, while changes in filtration rate have no obvious impact on turbidity. When filter thickness is large, particle content differs obviously from other filter thicknesses, and the effect of raw water concentration changes on particle content is similar to that of filter thickness changes. Sediment particle size distribution within the filter layer is primarily concentrated in the upper region. By focusing on the area situated 20 cm below the filter layer surface, the sediment retention rate reached 80% or higher at a depth of 20 cm below the filter layer surface. As the depth of the filter layer increases, the sediment retention characteristics show a decreasing trend for larger particle sizes and an increasing trend for smaller particle sizes. Head loss is positively correlated with filtration rate, raw water concentrations, and filter thickness changes. It is suggested that, when meeting micro-irrigation water quality requirements, the filter material particle size can be appropriately increased to improve filtration efficiency and reduce energy consumption. These research findings are highly significant for sand media filter material selection and working condition design.

The quartz sand filter medium used in micro-irrigation media filters has the disadvantages of short filtration cycle, surface filtration, and mining pollution. Selecting resources as new filter media is essential to improve the performance of the media filter and boost sustainable development. In this study, the traditional quartz sand filter medium and two new filter media were selected, and their corresponding filtration performances were comparatively studied. The influence of the type, particle size, and height of the filter medium on filtration performance was evaluated. The sediment content and distribution based on the size of particles in quartz sand, crushed glass, and glass bead filter layers was measured and analyzed. The hydraulic performance of different filter columns was analyzed. The results showed that for a given particle size, quartz sand exhibits the best sediment retention ability. This promoted the aggregation of small sediment particles into larger ones, whereas the crushed glass and bead glass filter layers promoted the splitting of large sediment particles into smaller ones, which enabled the reduction of blockage during the micro-irrigation process. The filtration rate of the quartz sand filter column exhibited the least fluctuation relative to crushed glass and glass bead filter media, and the pressure in each column exhibited a linear incremental change. In summary, glass microbeads are not suitable as filter material, crushed glass is suitable for general micro-irrigation systems, and quartz sand is suitable for micro-irrigation systems with elaborate filtration requirements. The findings of this study can provide theoretical guidance for the selection of the micro-irrigation filter material.

Rapid and non-destructive diagnosis of plant nitrogen (N) status is crucial to optimize N management during the growth of summer maize. This study aimed to evaluate the effectiveness of continuous wavelet analysis (CWA) in estimating the nitrogen nutrition index (NNI), to determine the most suitable wavelet analysis method, and to identify the most sensitive wavelet features across the visible to near-infrared spectrum (325–1,025 nm) for accurate NNI estimation. Field experiments were conducted across two sites (Kaifeng and Weishi) during the 2022 and 2023 growing seasons using four summer maize cultivars (XD20, ZD958, DH661, and DH605) under varying N application rates (0, 80, 160, 240, and 320 kg N ha^-1). Canopy reflectance spectra and plant samples were collected from the V6 to V12 growth stages. The wavelet features for each spectral band were calculated across different scales using the CWA method, and their relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) were analyzed using four regression models. The results showed that NNI varied from 0.61 to 1.19 across different N treatments during the V6 to V12 stages, and the Mexican Hat wavelet was identified as the most suitable mother wavelet, achieving an R^² value of 0.73 for NNI assessment. The wavelet features derived from the Mexican Hat wavelet were effective in estimating NNI, PDM, and PNC under varying N treatments, with the most sensitive wavelet features identified as 745 nm at scale 7 for NNI, 819 nm at scale 5 for PDM, and 581 nm at scale 6 for PNC using linear regression models. The direct and indirect methods for NNI estimation were compared using independent field data sets. Both methods proved valid to predict NNI in summer maize, with relative root mean square errors of 10.8% for the direct method and 13.4% for the indirect method. The wavelet feature at 745 nm, scale 7, from the direct method (NNI = 0.14 WF (745 nm, 7) + 0.3) was found to be simpler and more accurate for NNI calculation. These findings provide new insights into the application of the CWA method for precise spectral estimation of plant N status in summer maize.

The effective way to resolve the shortage of agricultural irrigation water resources is the research and application of new water-saving technology of surface irrigation in China. The aim of this paper is to improve the quality of field irrigation and water use efficiency, introduced two advanced water-saving irrigation technologies which were the improved ground irrigation technology and the precision surface irrigation technology. The improved ground irrigation technology mainly carried out the water-saving border irrigation technology, furrow irrigation technology and surge flow irrigation technology, and the precision surface irrigation technology mainly carried out control technology of the laser controlled land leveling and real feedback control technology of surface irrigation process. Through the promotion and application of the above technologies, improve the level of irrigation management and the quality of surface irrigation, obtain the effect of save water and increase production, promote the development of modern agriculture in China.

To further improve the prediction accuracy for greenhouse crop evapotranspiration (ET) under different irrigation conditions and enhance irrigation water use efficiency, this study proposes three methods to revise the Priestley–Taylor (PT) model coefficient α for calculating ET at different growth stages: (1) considering the leaf senescence coefficient f_S, plant temperature constraint parameter f_t, and soil water stress index f_sw to correct α (MPT model); (2) combining the Penman–Monteith (PM) model to inversely calculate α (PT-M model); (3) using the machine learning XGBoost algorithm to optimize α (PT-M_(XGB) model). Accordingly, this study observed the cumulative evaporation (Ep) of a 20 cm standard evaporation pan and set two different irrigation treatments (K0.9: 0.9Ep and K0.5: 0.5Ep). We conducted field measurements of meteorological data inside the greenhouse, tomato physiological and ecological indices, and ET during 2020 and 2021. The above three methods were then used to dynamically simulate greenhouse tomato ET. Results showed the following: (1) In 2020 and 2021, under K0.9 and K0.5 irrigation treatments, the MPT model mean coefficient α for the entire growth stage was 1.27 and 1.26, respectively, while the PT-M model mean coefficient α was 1.31 and 1.30. For both models, α was significantly lower than 1.26 (conventional value) during the seedling stage and the flowering and fruiting stage, rose rapidly during the fruit enlargement stage, and then gradually declined toward 1.26 during the harvest stage. (2) Predicted ET (ET_e) using the PT-M model underestimated the observed ET (ET_m) by 8.71~16.01% during the seedling stage and the harvest stage, and overestimated by 1.62~6.15% during the flowering and fruiting stage and the fruit enlargement stage; the errors compared to ET_m under both irrigation treatments over two years was 0.1~3.3%, with an R² of 0.92~0.96. (3) The PT-M_(XGB) model achieved higher prediction accuracy, with errors compared to ET_m under both irrigation treatments over two years of 0.35~0.65%, and R² above 0.98. The PT-M_(XGB) model combined with the XGBoost algorithm significantly improved prediction accuracy, providing a reference for the precise calculation of greenhouse tomato ET.