Filters are essential components for maintaining the stability of drip irrigation systems, effectively reducing the risk of clogging. However, when applied to slurry drip irrigation systems, the complexity of slurry water quality makes it unclear how different filter types and their combinations affect the hydraulic performance of the system. This study provides a comprehensive performance evaluation of two common filter types and their combinations, considering various flow rates and biogas slurry-to-water ratios under drip irrigation conditions. The results revealed the following key findings: (1) In the application of biogas slurry drip irrigation, an increase in the concentration or flow rate of the slurry significantly affects the hydraulic performance of the filter, increasing the risk of clogging and shortening the operational lifespan. Notably, the impact of changes in slurry concentration on the hydraulic performance of the filter is much greater than that of the flow rate. Compared to mesh filters, disk filters offer better hydraulic performance, with the contaminant capacity of disk filters being approximately three times that of mesh filters. (2) In biogas slurry drip irrigation, the filter combination generally outperforms single filters in terms of hydraulic performance and contaminant removal capacity. Due to the unique nature of the water source in biogas slurry, a selection process for filter combinations was conducted. It was found that when a disk filter is used as the pre-filter and a mesh filter as the post-filter, the overall rate of head loss change is the smallest, and the clogging uniformity is the least. (3) In the entropy weight-TOPSIS comprehensive evaluation, the filter’s operating time and contaminant capacity are key factors affecting its overall performance. From the perspective of improving the operational stability of the biogas slurry drip irrigation system, it is recommended to use a disk filter + mesh filter combination. This study conducts practical measurements on the hydraulic performance, contaminant removal capacity, filtration accuracy, and other indicators of commonly used mesh and disk filters, aiming to provide useful references for the practical application of biogas slurry drip irrigation filters.

Coconut coir, a key substrate in soilless cultivation, presents challenges for accurate moisture detection because of its complex internal structure, which limits the understanding of water infiltration and redistribution. This study employed RGB image recognition techniques combined with machine learning algorithms to systematically investigate the effects of initial moisture content (10%, 20%, and 30%), coarse-to-fine coir volume ratio (1:0, 1:1, and 0:1), and emitter discharge rate (1.0, 1.5, and 2.0 L h⁻¹) on wetting front morphology, water transport dynamics, and moisture variation within coir substrates. Morphological features of the wetting front were extracted from images and incorporated into three machine learning models—Support Vector Regression (SVR), Random Forest (RF), and Polynomial Regression—to construct a predictive framework for coir moisture estimation. The results showed that the SVR model achieved the best predictive performance in coarse coir substrates (R² = 0.89, RMSE = 3.37%), whereas Polynomial Regression performed best in mixed substrates (R² = 0.861, RMSE = 4.34%). All models exhibited lower accuracy in fine coir, particularly at high moisture levels. Under the same irrigation volume, increasing the initial moisture content enhanced both the water transport rate and the wetting front extent, with the aspect ratio (AR) decreasing from approximately 2.0 to 1.3, indicating a morphological transition of the wetting front from a “thumb-shaped” to a “hemispherical” pattern. Coarse particles facilitated vertical infiltration, while fine particles exhibited stronger water retention. By integrating RGB image recognition with machine learning approaches, this study achieved reliable prediction of coir moisture content and proposed an optimal management strategy using mixed substrates with an initial moisture content of 20–30% to balance infiltration efficiency and water-holding capacity while minimizing percolation risk. These findings provide a robust technical pathway for precise water management in coir-based cultivation systems.

Under drip irrigation conditions, the transport pattern of soil water in the root zone directly affects the water use efficiency of crops. The type of soil matrix, initial moisture content, and irrigation water quality jointly determine the hydrodynamic process of water infiltration. However, as a special type of irrigation water, the water movement mechanism of biogas slurry under drip irrigation in soilless cultivation substrates still lacks systematic investigation. In this study, transparent soil column infiltration experiments were conducted using two types of cultivation substrates—organic (coconut coir) and inorganic (desert sand)—under controlled facility conditions. Three initial moisture contents (10%, 15%, and 20%) and two irrigation water qualities (tap water and diluted biogas slurry) were combined to form twelve treatment groups. Soil moisture sensors and visualization techniques were employed to quantitatively analyze the wetting front morphology, vertical and horizontal infiltration rates, wetting ratio, and soil moisture profile distribution under different treatments. The results showed that the initial moisture content significantly influenced the advancement pattern of the wetting front. Higher initial moisture levels promoted the transformation of the wetting front shape from a “semi-pear” form to a “hemispherical” one and reduced the rate of infiltration decline. The coconut coir substrate exhibited stronger vertical infiltration capacity and a central water aggregation characteristic, whereas the desert sand demonstrated a wider horizontal expansion range. Under low and moderate initial moisture conditions, the application of biogas slurry enhanced horizontal water diffusion and improved the uniformity of the wetted zone, with the wetting ratio increasing by more than 6% compared with high moisture conditions. In addition, the power function model provided an excellent fit for the cumulative infiltration process across all treatments (R² > 0.96), indicating its suitability for describing the water transport process in facility cultivation substrates. This study provides theoretical support for precise water and fertilizer management and the efficient utilization of biogas slurry in soilless cultivation systems.

Aiming at the problem that the existing front-downward-looking 3D Synthetic Aperture Radar (SAR) needs to transmit wideband signals, which will lead to the high hardware cost, a frequency diversity array based front-downward-looking 3D SAR (3D-FDA-FDL-SAR) imaging technology is proposed. This technology places the FDA in the direction of the cross track on the mobile platform and performs aperture synthesis along the track to form a virtual 2D frequency diversity array, which can achieve 3D imaging of the target area. In this paper, a 3D imaging model of the 3D-FDA-FDL-SAR is established. Based on the back projection (BP) algorithm, the front-downward-looking 3D imaging of the target is realized. The simulation experiment verifies the effectiveness of this model.

Water management is a key factor affecting crop physiology and rhizosphere microbial ecology in protected agriculture. However, the responses of root development and bacterial community structure to different lower irrigation limits and drip irrigation flow rates in coir substrates remain poorly understood. This study investigated greenhouse-grown tomatoes in the Gobi region under three lower irrigation limits (30%, 50%, and 70% of substrate water-holding capacity) and two drip irrigation flow rates (1 and 2 L·h⁻¹). Substrate moisture, root traits, bacterial community structure, co-occurrence networks, and functional potential were evaluated. Increasing the lower irrigation limit maintained higher and more stable substrate moisture and generally enhanced root length, root surface area, root tip number, and root activity. W3Q1 promoted greater spatial expansion of the root system, whereas W2Q2 produced the largest root volume and maintained relatively high root activity. Irrigation combination significantly altered bacterial community structure but had limited effects on bacterial richness. W2Q1 enriched Bacillota-related taxa, whereas W2Q2 enriched Pseudomonadota and exhibited greater bacterial network connectivity and modularity. FAPROTAX-based functional prediction indicated that the bacterial communities were mainly associated with chemoheterotrophy, organic matter decomposition, and nitrogen transformation. Overall, the lower irrigation limit primarily regulated substrate moisture and root traits, whereas the effects of drip irrigation flow rate were trait- and irrigation-limit-dependent, with significant main and interaction effects on root volume. These findings provide a basis for optimizing precision irrigation in coir-substrate tomato cultivation in arid greenhouse systems.