Biosystems Engineering最新文献

A design was proposed for a seed turbine installed within the distribution device to reduce the influence of surface slope variations on the uniformity of seeding mass at each row. Through a comparative analysis based on the computational fluid dynamics (CFD) 6 degrees of freedom (DOF) dynamic mesh model simulation and the bench test, the influence of five different stabilising turbines on airflow distribution performance was investigated. The type Ⅰ stabilising turbine, characterised by acute inlet and outlet angles, exhibited a smaller vortex region at the blade inlet and improved the conveying and mixing performance of the seed. A CFD 6DOF simulation experiment was conducted to investigate the influence of the type I stabilising turbine on the airflow field distribution. As the number of blades increased from 4 to 10, the stability and uniformity of the conveying airflow distribution were enhanced at the turbine outlet. Simulations using a comprehensive performance test platform of the planter evaluated the influence of stabilising turbines with different numbers of blades on uniformity during field operations at varying surface slopes. When the angles of the front-rear and lateral one-way oscillation combination, and the front-rear and lateral reciprocating oscillation combination varied within the range of −5°–5°, the stabilising turbine with 8 blades exhibited the smallest uniformity coefficient of variation of the seeding mass at each row. The values ranged from 4.1 % to 5.8 % for rapeseeds and from 3.8 % to 5.0 % for wheat seeds.

In this study, a new microwave infrared hot air rolling bed dryer (MIHRBD) was developed and computational fluid dynamics (CFD) techniques were introduced into the design process of the integrated drying system. The structure of the air distribution device was optimised to improve the airflow uniformity over the curved surface of the rolling bed in the microwave-hot air drying combined equipment. The research findings reveal that, across eleven models, the outlet airflow velocity stabilises once the number of mesh elements reaches 5 million, achieving significant computational accuracy at that point. Optimizing components like the uniform air distribution pipe, turbulence plates, and wind deflectors significantly enhanced airflow distribution uniformity by 52.1%. The best airflow and temperature distribution uniformity on the rolling bed surface was achieved when the inlet airflow velocity ranged from 1 to 3 m s⁻¹, with minimum V_d, U_v and temperature non-uniformity coefficients of 0.007 m s⁻¹, 7.2% and 0.2%, respectively. Validation tests on the MIHRBD pilot equipment showed that after optimizing the uniform air distribution device, the minimum temperature difference on the pleurotus eryngii surface was 3.1 °C. This confirmed the feasibility of the computational fluid dynamics method. Introducing hot air significantly enhanced pleurotus eryngii's drying uniformity, with the Page model effectively predicting the MIHRBD drying process. This study provides technical support for future developments in this field of equipment manufacturing and drying process analysis.

In this study, a novel microwave sensing system, consisting of a microstrip self-resonant spiral coil inductively coupled to an external concentric planar probe loop, is presented and applied to the non-destructive detection of morpho-physiological plant responses to water stress. The optimised set-up of the proposed sensor ensures a highly sensitive spiral coil, which is a fundamental requirement to derive accurate information on plants' behavioural alterations related to water stress conditions. The proposed microwave sensor was tested it on two potted maize cultivars (Zea mays L.), namely “Cinquantino Bianchi” (CB) and “Scagliolo Frassine” (SF). For each cultivar, half of the samples were maintained at 100% (T100) field capacity while the other half was at 25% (T25) from 46 to 74 Days After Sowing (DAS). The frequency ($f_r$) shift and the amplitude peaks variation of the real component of the external planar probe input impedance (ℜ($Z_{input}$)) were obtained daily by positioning the sensor on the stem. These measured data were related to morpho-physiological parameters destructively acquired at four different growth stages. The resulting linear correlation between the stem's freshwater content (${FWC}_{stem}$) with both $f_r$ (r > −0.64) and the amplitude peaks (ℜ ($Z_{input}$)) (r > -0.70) provided evidence of the sensor's ability to identify stem dielectric properties' variations between the two water treatments. Concurrently, the sensor response demonstrated the capability to identify changes in the morphology and histology of the stem. Based on preliminary findings, the proposed sensor shows potential for employment in the real-time monitoring of plant water status, contributing to more economically and environmentally sustainable crop management practices. While the current correlations between plant water content and sensor measurements require further refinement to meet the rigorous industrial standards, nevertheless a large-scale adoption can be envisioned by leveraging IoT methodologies.

Emitter clogging adversely affects the performance of drip-irrigation systems. Many studies overlook the primary reason for emitter clogging by substances that precipitate within the emitter inlet. This study used computational fluid dynamics (CFD) to analyse the process of sedimentation in the inlet of emitters. Subsequently, the inlet structure was optimised based on the simulation results, production demand, and produced dripline. Anti-clogging physical tests were conducted in the laboratory and verified. Simulation results revealed that compared to the maximum discharge at the inlet of the domestic (CM) and Netafim (NF) emitters, that of the optimised (OS) emitter was increased by 60.0% and 13.2%, respectively; the maximum turbulent kinetic energy was increased by 88.9% and 13.3%, respectively; and the escape rate of solid particles in the dripline was increased by 3.2 and 5.9%, respectively. The results of an eighth-stage laboratory experiment with particle size ranges from 0.045 to 0.25 mm showed that the solid concentration was 1400 mg l⁻¹ for the CM-type emitter and 200 mg l⁻¹ for the OS-type emitter. However, the relative discharge of the OS-type emitter increased by 17.5%. At the end of the anti-clogging test, the relative discharge of the OS-type emitter was 0.12% more than that of the NF-type emitter. The water flowing through the OS-type emitter had a lower sediment content and higher relative discharge than of both comparison emitters. Therefore, optimising the emitter inlet can be an effective physical method for reducing the entry of solid particles into the emitter channel, which can greatly promote the sustainable development of drip irrigation.

Enhancing the utilisation rate and productivity of saline-alkali land is critical in ensuring sufficient cultivated land resources and food security. Although drip irrigation technology maintains the crop yield in saline-alkali land, the irrigation water amount must be higher than the crop demand. To address this, the present study develops a novel variable discharge emitter (VDE), which consists of an upper cover, a bottom cover, and a diaphragm with a linear incision. The experimental results showed that the VDE achieved two rated discharge levels of 4.1 L h⁻¹ and 9.7 L h⁻¹ when the working water pressure was at 0.10 MPa and 0.16 MPa and when the length of the incision, the thickness of the diaphragm, and the hardness of diaphragm inside the VDE were 3.5 mm, 1.5 mm, and 55.0 HA, respectively. It suggests that VDE has two rated discharges for irrigation and salt-leaching based on two working water pressure ranges.

Livestock feeding behaviour is an influential research area in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Current automated monitoring systems mainly use motion, acoustic, pressure and image sensors to collect and analyse patterns related to ingestive behaviour, foraging activities and daily intake. The performance evaluation of existing methods is a complex task and direct comparisons between studies is difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. This review on the analysis of the feeding behaviour of ruminants emphasise the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies and the main techniques to analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potential of the valuable information provided by automated monitoring systems to expand knowledge in the field, positively impacting production systems and research. The paper closes by discussing future engineering challenges and opportunities in livestock feeding behaviour monitoring.

Accurately identifying the fish feeding intensity plays a vital role in aquaculture. While traditional methods are limited by single modality (e.g., water quality, vision, audio), they often lack comprehensive representation, leading to low identification accuracy. In contrast, the multimodal fusion methods leverage the fusion of features from different modalities to obtain richer target features, thereby significantly enhancing the performance of fish feeding intensity assessment (FFIA). In this work a multimodal dataset called MRS-FFIA was introduced. The MRS-FFIA dataset consists of 7611 labelled audio, video and acoustic dataset, and divided the dataset into four different feeding intensity (strong, medium, weak, and none). To address the limitations of single modality methods, a Multimodal Fusion of Fish Feeding Intensity fusion (MFFFI) model was proposed. The MFFFI model is first extracting deep features from three modal data audio (Mel), video (RGB), Acoustic (SI). Then, image stitching techniques are employed to fuse these extracted features. Finally, the fused features are passed through a classifier to obtain the results. The test results show that the accuracy of the fused multimodal information is 99.26%, which improves the accuracy by 12.80%, 13.77%, and 2.86%, respectively, compared to the best results for single-modality (audio, video and acoustic dataset). This result demonstrates that the method proposed in this paper is better at classifying the feeding intensity of fish and can achieve higher accuracy. In addition, compared with the mainstream single-modality approach, the model improves 1.5%–10.8% in accuracy, and the lightweight effect is more obvious. Based on the multimodal fusion method, the feeding decision can be optimised effectively, which provides technical support for the development of intelligent feeding systems.

This study investigates the passive vibration dynamics of a sweep tool in a laboratory soil bin test, employing various spring configurations. A discrete element method (DEM) model of simulating the passively vibrating sweep tool was developed based on the laboratory soil bin tests. Ensuring precision in the DEM model parameters was achieved by applying a genetic algorithm tailored for this purpose. The genetic algorithm revealed that within the particle assemblies of the three geometries used in the DEM, several parameter sets were suitable for accurately describing the modelled soil. The final parameter set was chosen by integrating the DEM model with results from the laboratory direct shear box test. Employing Fast Fourier Transformation, both the laboratory soil bin test and the calibrated DEM model of the soil and the vibrating sweep tool facilitated an examination of frequencies and amplitudes during force and displacement measurements. The results indicated that, compared to a rigid tool, the draught force required by the 16 spring sweep tool was reduced by 6–9%. The absence of DEM would have limited the investigation of kinetic energy in the sweep tool and the dynamics of energy dissipation in the soil, if measurement equipment alone was used. This research successfully demonstrated that the reduced draught force with the 16 spring passively vibrating sweep tool, operating near the system's eigenfrequency, resulted from its ability to generate higher kinetic energy in the sweep tool while minimising energy dissipation in the soil.

The real-time and high-precision detection methods on embedded platforms are critical for harvesting robots to accurately locate the position of the table grapes. A novel detection method (ESP-YOLO) for the table grapes in the trellis structured orchards is proposed to improve the detection accuracy and efficiency based on You Only Look Once (YOLO), Efficient Layer Shuffle Aggregation Networks (ELSAN), Squeeze-and-Excitation (SE), Partial Convolution (PConv) and Soft Non-maximum suppression (Soft_NMS). According to cross-group information interchange, the channel shuffle operation is presented to modify transition layers instead of the CSPDarkNet53 (C3) in backbone networks for the table grape feature extraction. The PConv is utilised in the neck network to extract the part channel's features for the inference speed and spatial features. SE is inserted in backbone networks to adjust the channel weight for channel-wise features of grape images. Then, Soft_NMS is modified to enhance the segmentation capability for densely clustered grapes. The algorithm is conducted on embedded platforms to detect table grapes in complex scenarios, including the overlap of multi-grape adhesion and the occlusion of stems and leaves. ELSAN block boosts inference speed by 46% while maintaining accuracy. The [email protected]:0.95 of ESP-YOLO surpasses that of other advanced methods by 3.7%–16.8%. ESP-YOLO can be a useful tool for harvesting robots to detect table grapes accurately and quickly in various complex scenarios.

Camellia oleifera is an oilseed crop with high economic value. The short optimum harvest period and high labour costs of C. oleifera harvesting have prompted research on intelligent robotic harvesting. This study focused on the determination of grabbing points for the robotic harvesting of C. oleifera fruits, providing a basis for the decision making of the fruit-picking robot. A relatively simple 2D convolutional neural network (CNN) and stereoscopic vision replaced the complex 3D CNN to realise the 3D positioning of the fruit. Apple datasets were used for the pretraining of the model and knowledge transfer, which shared a certain degree of similarity to C. oleifera fruit. In addition, a fully automatic coordinate conversion method has been proposed to transform the fruit position information in the image into its 3D position in the robot coordinate system. Results showed that the You Only Look Once (YOLO)v8x model trained using 1012 annotated samples achieved the highest performance for fruit detection, with mAP₅₀ of 0.96 on the testing dataset. With knowledge transfer based on the apple datasets, YOLOv8x using few-shot learning realised a testing mAP₅₀ of 0.95, reducing manual annotation. Moreover, the error in the 3D coordinate calculation was lower than 2.1 cm on the three axes. The proposed method provides the 3D coordinates of the grabbing point for the target fruit in the robot coordinate system, which can be transferred directly to the robot control system to execute fruit-picking actions. This dataset was published online to reproduce the results of this study.