The drip emitter is a fundamental component of the drip irrigation system, and its performance directly influences the efficiency of water–fertilizer–gas (WFG) coupling irrigation. However, the precise mechanism through which WFG coupling affects emitter clogging and system uniformity remains unclear. To address this, this study conducted a hydraulic performance test of the drip irrigation system based on micro-nano aerated drip irrigation technology. The clogging patterns of emitters and system uniformity were compared and analyzed under non-aerated drip irrigation and WFG coupling drip irrigation conditions. The results indicate that WFG coupling significantly alters the micromorphological structure and microbial diversity of clogged emitters. This change reduces clogging and can delay the clogging process of different types of emitters, thereby extending their service life by up to 29%. Additionally, it effectively improves the uniformity of the drip irrigation system. These findings highlight the potential of WFG coupling as an effective strategy to mitigate emitter clogging and optimize drip irrigation system performance.

Aerated drip irrigation is rendered as a new water-saving irrigation method based on drip irrigation technology, which is endowed with the function of effectively alleviating the problem of rhizosphere hypoxia in crop soil, enhancing the utilization rate of water and fertilizer; as a result, it improves the harvest and quality of crops. However, clogged emitters are important indexes, among others, that pose an influence to the service effect and life duration of drip irrigation systems. At present, the working principle and mechanism of the influence of air feeding on the blockage of drip irrigation emitters remain unclear. Therefore, based on the two gas filling methods of the micro/nano bubble generator and Venturi injector, the dynamic change process for the average flow ratio of an air-filled drip irrigation emitter was studied by the method of emitter plugging test. 16S rRNA sequencing was used to analyze the microbial diversity of the emitter plugs. The results show that the air injection can pose influence on the clogging procedure of drip irrigation emitters, and more importantly, it makes the distribution of blocked emitters more uniform, thus improving the uniformity of the system. Different filling methods have different effects on the blockage of the emitter. Among them, the blockage time of drip irrigation system under the micro/nano aerated drip irrigation (MAI) mode is 5.73 times longer than that under unaerated drip irrigation (UVI), and similarly, Venturi gas drip irrigation (VAI) is close to that under UVI. The filling method changed the microbial diversity of the blockage in the emitter. Among them, the number of operational taxonomic unit (OTU) unique to MAI was 2.1 times that of UVI, and the number of OTU unique to VAI was 1.3 times that of UVI. Meanwhile, gas addition will inhibit the growth of Nitrospirae and Proteobacteria microbial communities and promote the growth of Firmicutes and Actinobacteria microbial communities. Furthermore, the increase in microbial extracellular polymer in the plugging material of the emitter was inhibited and the plugging process of the emitter was slowed down. The research results are of great significance in the disclosure of the clogging mechanism of drip irrigation emitter and constructing the green, anti-blockage technology of aerated drip irrigation.

The problem of emitter clogging has become the main obstacle restricting the application and promotion of drip irrigation technology. Studying the process of emitter clogging helps improve irrigation efficiency and save water resources. A large number of researchers have tried to solve the problem of emitter clogging from many perspectives. However, the influence of micro-nano bubbles as well as generated blockage on the clogging process of drip irrigation systems is less studied. Here, the influence of aeration on emitter clogging was studied by adding micro-nano bubbles to groundwater. Four different emitters were selected. Two treatments, micro-nano aeration and non-aeration, were set up, with a total of eight sets of experiments, running for 1500 h. The degree of emitter clogging was quantitatively characterized using the discharge ratio variation (Dra). The Christiansen uniformity coefficient (Cu) and statistical uniformity coefficient (Us) were used to evaluate the influence of emitter clogging on the performance of the drip irrigation system. Compared with the non-aeration treatment group, the Dra of aerated E1–E4 decreased by 64.74%, 54.22%, 64.20%, and 94.69% in 800 h, respectively. At the same time, the Us of the aerated E1–E4 decreased by 100%, 60.05%, 92.32%, and 100%, while the Cu of aerated E1–E4 decreased by 76.64%, 53.79%, 74.11%, and 100% compared with the unaerated group. The Cu and Us of all emitters under the aeration treatment were smaller than those comparison group. As for the blockage, the main components were typical physical blockage SiO₂ and chemical blockage CaCO₃. Most of the blockages in the non-aeration treatment group are 5–10 μm in length, while those in the aerated treatment group were generally less than 5 μm. Aeration treatment made the blockage more broken and dense and more likely to accumulate in the flow channel, obstructing the flow of water and thus intensifying the clogging process. As a result, micro-nano aeration treatment increased the risk of emitter clogging, accelerated the development of blockage in the emitter, and disturbed the uniformity of the entire drip irrigation system. This study provides a reference idea for solving the problem of blockage in drip irrigation systems.

The co-application of water, fertilizer, and air is a new water-saving irrigation method based on drip irrigation technology, which can effectively alleviate the phenomenon of soil rhizosphere hypoxia, improve water and fertilizer utilization efficiency, and inhibit the clogging of irrigation equipment in drip irrigation systems. The performance of drip irrigation systems is one of the important factors affecting the effectiveness of the co-application of water, fertilizer, and air. However, the impact of factors such as the aeration method, fertilization device, and working parameters on the performance of drip irrigation systems for the co-application of water, fertilizer, and air is still unclear. Therefore, based on two typical aeration methods, i.e., micro-nano and Venturi aeration, the performance of a drip irrigation system under the co-application of water, fertilizer, and air was studied by comparing and analyzing the effects of different aeration methods, working pressures of the drip irrigation system, and the pressure difference between the inlet and outlet of fertilizer irrigation on the spatial distribution uniformity of water, fertilizer, and air in the drip irrigation pipeline network. The results showed that the pressure difference between the inlet and outlet of fertilization irrigation had no significant impact on system performance, while the working pressure significantly affected system performance. Compared with the effective effect of Venturi aeration on system performance, micro-nano aeration can significantly affect drip irrigation system performance and effectively improve drip irrigation system performance. The micro-nano-aerated drip irrigation system with the co-application of water, fertilizer, and air under a working pressure of 0.1 MPa has better system performance. The research results are of great significance for revealing the mechanism underlying the impact of the co-application of water, fertilizer, and air on the performance of drip irrigation systems and constructing efficient drip irrigation technology for the co-application of water, fertilizer, and air.

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.