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Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses.

Root development alters soil pore structure and connectivity, thereby affecting un-saturated water movement; however, its growth-stage-dependent effects on infiltration and hydraulic properties in loess remain poorly quantified. In this study, one-dimensional soil column infiltration experiments were conducted on Festuca arundinacea root–soil composites under root distribution conditions corresponding to different growth stages ( T  = 0, 35, 52, 76, and 99d). The results showed that total infiltration duration decreased significantly with increasing growth stage, indicating enhanced water transport capacity in loess. Saturated hydraulic conductivity showed a strong linear relationship with root biomass ratio ( RB ), while the other hydraulic parameters varied systematically with increasing RB and were well described by exponential functions. Based on the temporal evolution of root distribution, quantitative relationships between hydraulic parameters and growth stage were established and incorporated into the Richards equation. HYDRUS simulations agreed well with the measured wet-ting-front advance and volumetric water content changes. These findings indicate that herbaceous root growth stage is an important biological factor regulating infiltration behavior and hydraulic properties in loess, providing parameter support for studies of vegetation–soil–water interactions and soil water infiltration modeling under vegetation restoration on the Loess Plateau.

Soybean plants form symbiotic nitrogen-fixing nodules with specific rhizobia bacteria. The root hair is the initial infection site for the symbiotic process before the nodules. Since roots and nodules grow in soil and are hard to perceive, little knowledge is available on the process of soybean root hair deformation and nodule development over time. In this study, adaptive microrhizotrons were used to observe root hairs and to investigate detailed root hair deformation and nodule formation subjected to different rhizobia densities. The result showed that the root hair curling angle increased with the increase of rhizobia density. The largest curling angle reached 268° on the 8th day after inoculation. Root hairs were not always straight, even in the uninfected group with a relatively small angle (<45°). The nodule is an organ developed after root hair curling. It was inoculated from curling root hairs and swelled in the root axis on the 15th day after inoculation, with the color changing from light (15th day) to a little dark brown (35th day). There was an error between observing the diameter and the real diameter; thus, a diameter over 1 mm was converted to the real diameter according to the relationship between the perceived diameter and the real diameter. The diameter of the nodule reached 5 mm on the 45th day. Nodule number and curling number were strongly related to rhizobia density with a correlation coefficient of determination of 0.92 and 0.93, respectively. Thus, root hair curling development could be quantified, and nodule number could be estimated through derived formulation.

To address critical challenges (unstable muck discharge, high blowout risk, and severe local wear) of Earth Pressure Balance (EPB) shield screw conveyors in water-rich sandy-cobble strata—where traditional single-phase simulations (pure Computational Fluid Dynamics [CFD] or Discrete Element Method [DEM]) fail to capture the intricate coupling between discrete cobbles and groundwater—a bidirectional CFD-DEM fluid-solid coupling model was developed, with the Beijing Metro New Airport Line project as the engineering prototype. Following the model development, numerical simulations were conducted under controlled earth pressure (504 kPa) and varied water-soil pressure ratios. Observations indicated that muck discharge remained stable when the water-soil pressure ratio ranged from 0.24 to 0.48; exceeding the critical threshold of 0.56 induced particle segregation and blowout, with the initial signal being reduced filling rate rather than immediate discharge surge, and dry muck efficiency dropping to only 22.4% of the theoretical value. An 80% reduction in total pressure was found to occur at the interface between the excavation chamber and screw conveyor within 80 seconds. Additionally, the screw conveyor exhibited a distinct “dual-peak, three-stage” wear distribution: an impact wear peak at the 0 m inlet and a frictional wear peak at 8.625 m. This is fundamentally distinct from traditional single-peak models, which fail to account for the differential impacts of particle kinetic energy attenuation and stage-dependent wear mechanisms throughout the conveyance process. Subsequently, a comprehensive analysis of flow field characteristics, particle kinematics, and wear mechanisms was carried out. Finally, further discussion was conducted pertaining to the engineering implications of the findings, providing direct technical support for the optimized design of screw conveyors (e.g., targeted structural reinforcement at key wear zones), the development of stratified anti-wear measures, and the configuration of a precise blowout early warning system with the critical water-soil pressure ratio (0.56) as the core monitoring indicator.

Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants.

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.

Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

In this study, a screen-printed electrode (SPE) modified with cobalt oxide nanoparticles (Co3O4 NPs) was used to create an all-solid-state ion-selective electrode used as a potentiometric ion sensor for determining nitrate ion (NO3−) concentrations in aquaculture water. The effects of the Co3O4 NPs on the characterization parameters of the solid-contact nitrate ion-selective electrodes (SC-NO3−-ISEs) were investigated. The morphology, physical properties and analytical performance of the proposed NO3−-ion selective membrane (ISM)/Co3O4 NPs/SPEs were studied by X-ray diffraction (XRD), energy-dispersive spectroscopy (EDS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), potentiometric measurements, and potentiometric water layer tests. Once all conditions were optimized, it was confirmed that the screen-printed electrochemical sensor had high potential stability, anti-interference performance, good reproducibility, and no water layer formation between the selective membrane and the working electrode. The developed NO3−-ISM/Co3O4 NPs/SPE showed a Nernstian slope of −56.78 mV/decade for NO3− detection with a wide range of 10−7–10−2 M and a quick response time of 5.7 s. The sensors were successfully used to measure NO3− concentrations in aquaculture water. Therefore, the electrodes have potential for use in aquaponic nutrient solution applications with precise detection of NO3− in a complicated matrix and can easily be used to monitor other ions in aquaculture water.

Loess slopes with steep gradients are particularly prone to vertical tension cracks at the crest, resulting from unloading and other factors. These cracks significantly affect the spatiotemporal distribution of moisture infiltration during rainfall, potentially leading to slope instability. This study investigates the impact of crest-tension cracks on moisture infiltration in loess slopes under extreme rainfall conditions, focusing on crack position, depth, and width. Soil moisture content and the dynamics of wetting fronts were monitored to assess how these tension cracks influence infiltration patterns. The results indicate that tension cracks at the slope crest act as preferential infiltration pathways, causing water retention within the cracks and forming a “U-shaped” preferential infiltration zone. The extent of this “U-shaped” wetting front is influenced by the crack’s width, depth, and proximity to the slope shoulder; wider, deeper cracks closer to the shoulder result in a more pronounced wetting front. Over time, as rainfall persists, the influence of preferential infiltration decreases, and the infiltration patterns of slopes with crest cracks begin to resemble those of homogeneous slopes. In both cases, wetting fronts exhibit intersecting patterns: one parallel to the slope crest and the other parallel to the slope surface. During the initial stages of rainfall, the migration speed of wetting fronts in slopes with crest-tension cracks was significantly higher than in homogeneous slopes. However, after prolonged rainfall, the migration speeds of wetting fronts in both scenarios converged. A strong linear correlation was observed between the average migration depth of the horizontal wetting front at the slope crest and the parallel wetting front on the slope surface, for both slope types. These findings deepen our understanding of moisture migration dynamics in loess slopes with crest-tension cracks, providing insights for developing effective slope hazard mitigation strategies.

Plant roots are important in ecological slope protection and reinforcement, significantly affecting soil’s water-holding characteristics and shear strength. The typical herb Festuca Arundinacea root-loess composite in the Loess Plateau was taken as the research object in this paper. The matrix suction test, unsaturated shear strength test, nuclear magnetic resonance (NMR) test, and scanning electron microscope (SEM) test were used to systematically study the root–soil composite’s suction state and strength characteristics under different water content conditions and reveal its internal physical mechanism. The main research results are as follows: (i) The incorporation of roots can increase the air entry value (AEV), reduce the residual water content, and significantly enhance the matrix suction within a specific water content range, thereby increasing the unsaturated shear strength and the enhancement effect of shear strength gradually decreases with the increase of water content. (ii) The microscopic test results show that the root system accelerates the water loss of the soil under low suction conditions by changing the pore structure, increasing the total porosity and the proportion of medium and large pores. At the same time, the increase of tiny pores enhances the capillary action, increasing matrix suction. (iii) Roots can also improve the soil stress state, enhance the adsorption strength of matrix suction, and effectively improve soil shear strength. The results of this study are helpful to understanding the evolution mechanism of unsaturated strength characteristics of root–soil composite and provide the experimental basis and theoretical reference for vegetation slope protection engineering in the loess area.