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In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng’s grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.

The static drill-rooted energy pile is an emerging green technology increasingly applied in coastal soft soil areas. Existing research has mainly focused on its heat transfer and bearing characteristics, while studies on its environmental impacts remain limited. Based on the Green Building Evaluation Standard and the Life Cycle Assessment method and drawing on practical energy pile projects in coastal areas, this study developed an environmental impact assessment system for energy piles. A comprehensive evaluation method was established, incorporating four indicators: muck and slurry discharge, vibration, noise, and carbon reduction benefits. Using a pilot project, field testing and theoretical analysis were conducted to assess the environmental impact of static drill-rooted energy piles. The results revealed that muck and slurry discharge is significantly lower compared to bored energy piles. Vibration levels at a site office located 30 m from the construction point were below the annoyance threshold of 0.05 g in terms of relative vibration acceleration. Noise levels dropped below the emission limit of 85 dB at a distance of 5 m. Carbon emissions during the material production stage were reduced by 22–45% compared to bored energy piles and by 12% during the construction stage. During the operation stage, compared to air-source heat pumps, electricity savings of 0.691–0.836 kWh per hour and CO2 emission reductions of 0.471–0.57 kg per hour were achieved. Based on the comprehensive scoring of all indicators, the static drill-rooted energy pile technology received an overall rating of ‘‘excellent.’’ This study provided an evaluation framework for the environmental assessment of energy piles and contributed positively to promoting the development of green infrastructure.

The treatment of refractory nitrogenous organic matter in industrial wastewater management poses challenges in the removal of organic matter and nitrogen. To address these issues, this study utilized a novel composite hydrodynamic cavitator, mainly consisting of spiral pipes and a step drain, which could generate cavitation twice per pass at the throat of the spiral pipe and the step drain of the cavitation cavity, thereby distinguishing it from other existing cavitators that produce cavitation only once per pass. The composite hydrodynamic cavitator, optimized using ANSYS 19.2 simulation software, offers significant advantages in energy utilization and mass transfer efficiency. Moreover, it generates a high concentration of hydroxyl free radicals, which are crucial for organic matter degradation. Batch experiments demonstrated the effective treatment of 4-aminophenol. Within 120 min, 4-aminophenol degradation efficiency reached 74.7% and total nitrogen concentration decreased slightly from 1.28 mg/L to 1.06 mg/L, while ammonia nitrogen concentration initially increased before decreasing from its peak value of 0.82 mg/L to 0.77 mg/L. During the cavitation treatment of 4-aminophenol, intermediate products, such as benzoquinone, were generated. Under the strong oxidizing action of hydroxyl radicals, nitrogen undergoes deamination to form ammonium ions, which were likely removed predominantly as nitrogen gas. The experimental results are anticipated to establish a foundation for the application of hydrodynamic cavitation technology in the treatment of refractory organic wastewater degradation and to support denitrification processes.

The implantable capsule grouting pile is a novel pile foundation technology in which a capsule is affixed to the side of the implanted pile to facilitate grouting and achieve extrusion-based reinforcement. This technique is designed to improve the bearing capacity of implanted piles in coastal areas with deep, soft soil. This study conducted model tests involving multiple grouting positions across different foundation types to refine the construction process and validate the enhancement of bearing capacity. Systematic measurements and quantitative analyses were performed to evaluate the earth pressure distribution around the pile, the resistance characteristics of the pile end, the evolution of side friction resistance, and the overall bearing performance. Special attention was given to variations in the lateral friction resistance adjustment coefficient under different working conditions. Furthermore, an actual case analysis was conducted based on typical soft soil geological conditions. The results indicated that the post-grouting process formed a dense soil ring through the expansion and extrusion of the capsule, resulting in increased soil strength around the pile due to increased lateral earth pressure. Compared to conventional piles, the grouted piles exhibited a synergistic improvement characterized by reduced pile end resistance, enhanced side friction resistance, and improved overall bearing capacity. The ultimate bearing capacity of model piles at different grouting depths across different foundation types increased by 6.8–22.3% compared with that of ordinary piles. In silty clay and clayey silt foundations, the adjustment coefficient ηs of lateral friction resistance of post-grouting piles ranged from 1.097 to 1.318 and increased with grouting depth. The findings contribute to the development of green pile foundation technology in coastal areas.

Real-time object detection plays an indispensable role in facilitating the intelligent harvesting process of passion fruit. Accordingly, this paper proposes an FSOne-YOLOv7 model designed to facilitate the real-time detection of passion fruit. The model addresses the challenges arising from the diverse appearance characteristics of passion fruit in complex growth environments. An enhanced version of the YOLOv7 architecture serves as the foundation for the FSOne-YOLOv7 model, with ShuffleOne serving as the novel backbone network and slim-neck operating as the neck network. These architectural modifications significantly enhance the capabilities of feature extraction and fusion, thus leading to improved detection speed. By utilizing the explainable gradient-weighted class activation mapping technique, the output features of FSOne-YOLOv7 exhibit a higher level of concentration and precision in the detection of passion fruit compared to YOLOv7. As a result, the proposed model achieves more accurate, fast, and computationally efficient passion fruit detection. The experimental results demonstrate that FSOne-YOLOv7 outperforms the original YOLOv7, exhibiting a 4.6% increase in precision (P) and a 4.85% increase in mean average precision (mAP). Additionally, it reduces the parameter count by approximately 62.7% and enhances real-time detection speed by 35.7%. When compared to Faster-RCNN and SSD, the proposed model exhibits a 10% and 4.4% increase in mAP, respectively, while achieving approximately 2.6 times and 1.5 times faster real-time detection speeds, respectively. This model proves to be particularly suitable for scenarios characterized by limited memory and computing capabilities where high accuracy is crucial. Moreover, it serves as a valuable technical reference for passion fruit detection applications on mobile or embedded devices and offers insightful guidance for real-time detection research involving similar fruits.

Water quality sampling and monitoring are fundamental to water environmental protection. The purpose of this study was to develop a water quality sampling and multi-parameter monitoring system mounted on a multi-rotor unmanned aerial vehicle (UAV). The system consisted of the UAV, water sampling and multi-parameter detection device, and path planning algorithm. The water sampling device was composed of a rotating drum, a direct current (DC) reduction motor, water suction hose, high-pressure isolation pump, sampling bottles, and microcontroller. The multi-parameter detection device consisted of sensors for potential of hydrogen (pH), turbidity, total dissolved solids (TDS), and a microcontroller. The flight path of the UAV was optimized using the proposed layered hybrid improved particle swarm optimization (LHIPSO) and rapidly-exploring random trees (RRT) obstacle avoidance path planning algorithm, in order to improve the sampling efficiency. Simulation experiments were conducted that compared the LHIPSO algorithm with the particle swarm optimization (PSO) algorithm and the dynamic adjustment (DAPSO) algorithm. The simulation results showed that the LHIPSO algorithm had improved global optimization capability and stability compared to the other algorithms, validating the effectiveness of the proposed algorithm. Field experiments were conducted at an aquaculture fish farm, and the device achieved real-time monitoring of three water quality parameters (pH, TDS, turbidity) at depths of 1 m and 2 m. A rapid analysis of three parameters (ammonia nitrogen, nitrite, dissolved oxygen) was performed in the laboratory on the collected water samples, and validated the feasibility of this study.

In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP, while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations.

To address the persistence of traditional welded joints in the construction of static drill rooted nodular piles, high-strength bolted connections are introduced. Tensile performance tests were conducted on seven sets of full-scale joint specimens to evaluate the ultimate tensile bearing capacity, deformation ductility, and damage characteristics of high-strength bolted joints. Numerical models were established using ABAQUS 2020 software to complement the experimental findings. The results indicate that the ultimate tensile capacity test values of high-strength bolted joints and welded joints are comparable, both exceeding the values calculated by the pile ultimate tensile capacity specification formula. Moreover, the ultimate tensile capacity values of specimens with improved high-strength bolted joints surpass those of ordinary joints. Notably, in the final stages of testing, both high-strength bolted joints and welded joints experienced pull-off at the pier head of the prestressing reinforcement, with the joints remaining intact. The load-displacement curves obtained from the ABAQUS numerical model align closely with the experimental measurements. These findings offer valuable insights and serve as an experimental foundation for promoting the adoption and utilization of high-strength bolt joints.

The pre-stressed high-strength concrete (PHC) nodular pile is a type of PHC pile with a variable cross-section of the pile shaft, and it has normally been applied in ground treatment projects in recent years. The PHC nodular pile shaft consists of nodules, which introduce differences for the load transfer mechanism of the PHC nodular pile compared to the conventional PHC pipe pile. In this paper, the load transfer mechanism and influencing factors of the bearing capacity of the PHC nodular pile were investigated based on a group of field tests and numerical simulations. The following conclusions were obtained based on the analysis of the field test and simulation results: the nodules along the pile could effectively increase the ultimate capacity of the PHC nodular pile, and the field test results showed that the ultimate capacity of 450 (500) mm PHC nodular piles was about 1.23–1.38 times of the 450 mm PHC pipe pile after being cured for 40 days, which can be used for the design of PHC nodular pile. The simulation results showed that the bearing capacity of the PHC nodular pile would decrease with the increase in nodular spacing and nodular length along the pile shaft, while increasing with the increase in nodular diameter, and the diameter of the nodule can be increased moderately to improve the ultimate capacity of the PHC nodular pile.

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.