Biosystems Engineering最新文献

Wind-disturbance is a potential eco-friendly technique for tackling leggy seedlings. This study uses orthogonal experimental design and seedlings vigour assessment by strong seedling index (SSI) to investigate wind-disturbance on regulating tomato seedlings growth. Changes in endogenous hormone levels and biomechanical properties of tomato seedlings were investigated using enzyme-linked immunosorbent assay and uniaxial tension tests. Results showed that factors influencing significantly SSI, in descending order, are wind disturbance time (T), seedling age at the onset of wind disturbance (SA), wind velocity (V), and interval time (I). The wind-disturbance effect was found to be optimal with SSI = 0.126 for a condition where V, T, I and SA values are 3 m s⁻¹, 1 min, 30 min, and 15 days, respectively. V and T were positively correlated with the ethylene and abscisic acid content in seedling leaves, abscisic acid and auxin content in stems, cytokinin and ethylene content in roots, and the elastic moduli of stems and roots but negatively associated with the cytokinin content in stems and leaves, auxin and abscisic acid content in roots, and leaves’ elastic modulus. Wind disturbance mechanism for controlling seedlings growth involved eliciting the accumulation of abscisic acid in stems and leaves and reduction of the auxin content in roots to about the optimal threshold for roots growth thereby reducing seedling stems and leaves development and promoting a better roots growth and a high SSI. This work offers theoretical insights and technical guidance for utilising wind-disturbance as a sustainable seedling cultivation and personalised seedling management approach.

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions.

In micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist that are trustworthy enough to forecast the dissolved oxygen and turbidity at the outlet when utilising various media configurations and filter types. The objective of this investigation was to construct a model that can identify turbidity and dissolved oxygen at the filter outlet in advance. This study presents an algorithm for meta-heuristic optimisation inspired by populations termed Differential Evolution (DE) in conjunction with Support Vector Regression (SVR) (DE/SVR-relied model). This is an effective machine learning method, with seven kernel types for calculating the output turbidity (Turb_o) and the output dissolved oxygen (DO_o) from a dataset comprising 1,016 samples of various reclaimed water-using filter types. The type of media and filter, the height of the filter bed, the cycle duration, and the filtration velocity, as well as the electrical conductivity at the filter inlet, pH, inlet dissolved oxygen, water temperature, and the input turbidity are all tracked and analysed in order to achieve this. The best-fitted DE/SVR-relied model was constructed to predict the Turb_o and DO_o as well as the input variables' relative importance. Determination coefficients for the best-fitted DE/SVR-relied model for the testing dataset were 0.89 and 0.92 for outlet turbidity (Turb_o) and outlet dissolved oxygen (DO_o), respectively, showing a good predictive performance which are of great importance for the management of drip irrigation systems.

Greenhouse production of fruits and vegetables in developed countries is challenged by labour scarcity and high labour costs. Robots offer a good solution for sustainable and cost-effective production. Acquiring accurate spatial information about relevant plant parts is vital for successful robot operation. Robot perception in greenhouses is challenging due to variations in plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which provides essential information to harvest the tomato bunches. Specifically, this paper proposes a method that detects four anatomical landmarks in the colour image and then integrates 3D point-cloud information to determine the 3D pose. A comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the performance of different parts of the method. The results showed: (1) high accuracy in object detection, achieving an Average Precision (AP) of [email protected]=0.96; (2) an average Percentage of Detected Joints (PDJ) of the keypoints of [email protected] = 94.31%; and (3) 3D pose estimation accuracy with mean absolute errors (MAE) of 11^o and 10^o for the relative upper and lower angles between the peduncle and main stem, respectively. Furthermore, the capability to handle variations in viewpoint was investigated, demonstrating the method was robust to view changes. However, canonical and higher views resulted in slightly higher performance compared to other views. Although tomato was selected as a use case, the proposed method has the potential to be applied to other greenhouse crops, such as pepper, after fine-tuning.

Dispersive soil is commonly associated with hydraulic erosion due to its tendency to disperse when in contact with water. Nevertheless, the erosion process of dispersive soil remains poorly understood. This study aims to explore the influence of dispersivity and initial moisture content on the splash erosion of dispersive soil, which is a crucial stage of hydraulic erosion. Consecutive single-drop splash tests were conducted on artificially prepared dispersive soil (made by adding sodium carbonate to the soil) with varying dispersivity levels and moisture contents. A high-speed and a single-lens reflex (SLR) camera were employed to capture the process of erosion in exquisite detail. The results demonstrated the significant influence of dispersivity on splash erosion. At moisture content below 20%, increased dispersivity weakened the splash erosion effect, leading to reduced infiltration, splashed soil mass, crater volume, and depth. Conversely, when the initial moisture content reached 20% or saturation, intensified dispersivity exacerbated splash erosion. Dispersivity also increased the sensitivity of the soil splash process to changes in moisture content. Dispersive soil exhibited greater sensitivity compared to non-dispersive soil, affecting the mass of splashed soil, soil-water mixture droplets area, water-soil mass ratio, and particle ejection distance. Dispersivity also caused splashed particles to fragment, resulting in more soil-water mixture droplets and a greater splash distance. Furthermore, dispersivity reduced infiltration ratio and increased runoff yield after erosion events, indicating a higher risk of transporting splashed soil particles through runoff. These insights contribute to erosion models and have practical applications in managing dispersive soil.

A horizontal air-assisted centralised metering device involves the use of conveying airflow for mixing and conveying during the seeding process, making it suitable for high-speed precision seeding. This study identified key structural parameters for the mixing component. Computational fluid dynamics (CFD) simulations were used to analyse the influence of the key structural parameters on airflow distribution and airflow velocity. The results suggested selecting a contraction section conical angle of 40° with maximal conveying airflow velocity and minimal airflow pressure loss. A mixing chamber length of 25 mm prevented the retention and blockage. An expansion section conical angle of 20° achieved higher airflow velocity within the expansion section. CFD-DEM (Discrete Element Modelling) coupled simulation were used to analyse the influence of mixing chamber height, seeding rate, and conveying airflow velocity on seed conveying performance. The results indicated that a mixing chamber height of 16 mm ensured stable seed acceleration, reduced the probability of seed-wall collisions. Seed collisions within the mixing chamber and expansion section increased noticeably along with the rising seeding rates. While airflow velocity in the range of 16–25 m s⁻¹ facilitated timely seed conveyance and reduced seed-wall collisions. Verification experiments for the optimal parameter combination of the mixing component indicated that a conveying airflow velocity of 22 m s⁻¹ resulted in the stability coefficient of variation of total seeding mass not exceeding 1.04 %, the uniformity coefficient of variation of seeding mass in each row not exceeding 3.61 %. This research offers valuable insights for structural improvements in the mixing component of the horizontal air-assisted metering device.

Mechanised harvesting of sugarcane can significantly reduce harvesting costs and improve efficiency. However, there are currently issues with poor chopping performance and significant losses of sugarcane juice. This study aimed to investigate the chopping and damage mechanisms of sugarcane and improve the performance of the chopping system through kinematic and dynamic analysis and finite element simulation. Firstly, the factors affecting the chopping performance and causing sugarcane damage were studied by establishing the kinematics and dynamics models of the chopper. The types of sugarcane damage and sugarcane fragments were summarised. Then, a finite element model of the chopping system was built. An anisotropic and nonlinear material model for sugarcane was developed using Fortran. The transient process of chopping was analyzed by the explicit dynamic analysis method, and the chopping and damage mechanism of sugarcane was revealed. The results indicated that a smaller thickness and bevel angle of blade led to a shorter chopping time and a smoother chopping surface. The upper and lower blades of the chopper had unequal axial chopping depths, which was the primary cause of sugarcane damage. The rotational speed ratio of conveying roller to chopping roller and the blade thickness affected step degree of chopping surface. Finally, the chopping system was improved according to the theoretical and simulation analysis results. Experimental results showed that the damage rate decreased by 6.8%, 12.2%, and 12.6% respectively, and the loss rate decreased by 8.1%, 13.9%, and 21.8%, respectively, when the feed rate was 1, 2, and 3 kg s⁻¹. The study results provide a reference for the design and optimization of sugarcane harvester’s chopping system.

This study presents the development of a maize ear model and a predictive approach for kernel damage in maize threshing, integrating physical simulation and predictive analytics to understand better and forecast threshing-related damage. First, a maize ear model was developed to analyse kernel damage during threshing. Through the angle of repose experiments, the optimal number of spheres for the kernel model was established as 65. Tensile tests were conducted to evaluate the kernel-cob bond strength, revealing an average relative error in the bonding force of 8.71%. Vogel impact energy modelling was applied to the kernel threshing process to determine kernel damage. The correlation between the speed of seed grain movement and the occurrence of damage was analysed by post-processing to identify locations with frequent kernel damage in the drum. In-depth data analysis of kernel damage in the threshing drum further elucidates the inherent relationship between kernel velocity and damage extent. The study then focused on applying neural networks to predict damage rates. The comparative evaluation shows that the PSO-LSTM model has better prediction accuracy than LSTM and RNN models, with the PSO-LSTM network achieving an RMSE of 0.096, a $R^2$ of 99.96%, and a final damage rate of 2.41% in validation tests. Threshing experiments were conducted to verify the model, showing a 1.4% discrepancy between predicted and actual damage rates. This study proposes a kernel damage prediction model and provides new insights and directions for the structural design of threshing drums.

With growing concerns about air pollution and global warming effects, the emissions of NH₃ and greenhouse gases (GHGs) have become significant issues in the pig production industry. In order to discover whether optimizing manure removal strategies could alter the manure decomposition process and benefit the reduction of gas emissions, a scale model approach was used to quantify the gas emissions under controlled conditions. This study compared the gas emission reduction potential of two classic manure removal systems (scraper and pull-plug system) in three manure removal strategies: scraper-I (retaining manure for 24 h), scraper-II (retaining manure for 8 and 16 h) and pull-plug (retaining manure for 44 days). Fresh urine and faeces collected from a gestation sow house were applied to the scale models and then removed through the manure removal systems. The concentrations of gases (NH₃, CH₄, CO_2, and N₂O) within the scale model and removed slurry samples were collected and analysed (pH, electric conductivity, dry matter, total nitrogen, total ammonium nitrogen and total carbon). The results showed that emissions of CH₄, CO_2, and N₂O from scraper-I were on average 54%, 56% and 25% lower than those from scraper-II, and 71%, 67% and 6% lower than those from pull-plug treatment, respectively. The GHGs emission rates (as CO₂-equivalents) of scraper-I and scraper-II were 52% and 26.3% lower than that of pull-plug treatment respectively (P < 0.01). The emissions of NH₃ displayed a temporary peak during each application of urine and removal of manure. In pull-plug treatment, the concentrations of NH₃, CH₄, and CO₂ exponentially increased between Day 39 and Day 41. The nitrogen content, both total nitrogen and total ammonium nitrogen, within the slurry under the scraper system exhibited lower values compared to those from the pull-plug system. Conversely, the total carbon content was higher in the former system. Caution is warranted in extrapolating results to full-scale pig housing, given study limitations (e.g. small scale, mimicked scraper activity, no animals, etc.). Nevertheless, the conclusions and findings of this study provide fundamental data for understanding gas emissions from pig house manure management. This insight can guide the design and daily operations of low-emission manure removal systems in gestation sow houses.

This study compares the performance of Transformers with LSTM for the classification of the behavioural time budget in horses based on video data. The behavioural time budget of a horse consists of amount of time of the activities such as feeding, resting, lying, and moving, which are important indicators of welfare and can be a basis of pain detection. Video technology offers a non-invasive and continuous monitoring approach for automated detection of horse behaviours. Computer vision and deep learning methods have been used for automated monitoring of animal behaviours, but accurate behaviour recognition remains a challenge. Previous studies have employed Convolutional LSTM models for behaviour classification, and more recently, Transformer-based models have shown superior performance in various tasks. This study proposes a multi-input, multi-output classification methodology to address the challenges of accurately detecting and classifying horse behaviours. The results demonstrate that the multi-input and multi-output Transformer model achieves the best performance in behaviour classification compared with single input and single output strategy. The proposed methodology provides a basis for detecting changes in behaviour time budgets related to pain and discomfort in horses, which can be valuable for monitoring and treating horse health problems.