Biosystems Engineering最新文献

Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.

X-ray fluorescence (XRF) analyses are fast, clean, non-destructive, and compatible with on-field operations, which are some advantages over traditional determinations using coupled plasma optical emission spectroscopy (ICP-OES). The aim of this study was to advance in situ XRF approaches for assessing the nutritional status of soybean leaves (i.e., P, S, K, Ca, Mn, Fe, Cu and Zn). More specifically, we propose a protocol to ensure accuracy of in-field analysis and then evaluate the predictive performance of XRF via different data modelling strategies for macro- and micronutrient determination. Therefore, the XRF sensor dwell time of 60 s and the maximum time of 5 min were determined for the analysis of the leaves after leaf abscission, taking into account the influence of moisture loss on the signal intensity of the lighter elements. Regarding the predictive performance of XRF data for nutrients determination, multiple linear regression (MLR) models resulted in lower root mean square errors (RMSE) for P (433 mg kg⁻¹), S (204 mg kg⁻¹) and K (1957 mg kg⁻¹); Partial least squares regression (PLS) for Ca (519 mg kg⁻¹); and simple linear regression (SLR) for Mn (9 mg kg⁻¹), Fe (18 mg kg⁻¹), Zn (5 mg kg⁻¹). The different modelling strategies exhibited equivalent RMSE for Cu (2 mg kg⁻¹). These prediction errors are within a ±20% range, demonstrating that the in situ protocols developed in this research are useful for predicting the nutrients concentration in soybean leaves. Our study shows the possibility of using the in situ XRF sensor for the rapid and practical nutrients determination in soybean leaves, presenting good potential as a crop diagnosis tool.

In ovo sexing identifies chicken embryo sex before or during incubation to avoid euthanising male chicks after hatching, enhancing animal welfare in the laying hen industry. Recently, researchers demonstrated the potential for non-invasive and early in ovo sexing through the analysis of volatile organic compounds (VOCs) emitted by eggs. However, a knowledge gap remains in understanding prediction model robustness, the efficacy of faster acquisition techniques, and day-to-day performance. In this study, two experiments were performed to fill these gaps. In Experiment 1, passive VOC extractions were performed on 110 eggs on incubation day 10 using sampling bags employing headspace sorptive extraction-gas chromatography-mass spectrometry (HSSE-GC-MS), proton transfer reaction-time-of-flight-mass spectrometry (PTR-TOF-MS), and selected ion flow tube-mass spectrometry (SIFT-MS). Prediction models were built using partial least squares-discriminant analysis (PLS-DA) and variable selection methods. As a result, prediction accuracies ranged from 57.6 % to 61.4 %, indicating no significant difference between the devices and highlighting the need for further optimisations. In Experiment 2, passive VOC samplings were performed on 42 eggs in glass jars during the initial 12 days of incubation using HSSE-GC-MS. Consequently, the optimised setup yielded higher accuracies ranging from 63.1 % (on day 0) to 71.4 % (on days 4, 6, and 12), revealing VOCs consistently elevated in relative abundance for a specific sex, and overall VOC abundance was higher in male embryos. Suggestions for future experiments to increase the accuracy of VOC in ovo sexing include active sampling with inert materials, expanding sample sets, and targeting consistent compounds.

Transporting pigs poses a significant challenge in maintaining proper interior thermal conditions. This study conducted 36 field trials run in Denmark and collected data from a certified livestock vehicle, during journeys of 8 h and 23 h respectively. This study aims to investigate the air temperature inside a livestock vehicle during the transportation and the influence of five factors on DT (difference in air temperature between interior of the vehicle and exterior): compartment location, deck height, height of openings for natural ventilation, wind speed and vehicle speed. The compartment location was the most important influencing factor of interior air temperature. The maximum percentage of time when air temperature exceeded 30 °C was 13.6% observed in the front compartment of trailer. The maximum difference in mean DT between the front and rear compartments at the same deck was 11.0 ± 0.67 °C occurred in the lorry. The maximum differences in mean DT between the two investigated deck heights were 1.2 ± 0.39 °C in the lorry (70 vs. 90 cm) and 0.9 ± 0.26 °C in the trailer (60 vs. 80 cm), respectively. The DT decreased with increasing height of opening for natural ventilation and wind speed, while the DT was insensitive to vehicle speed. Extra sensors installed on the front partition wall during the last 4 journeys showed significant temperature variability (up to 12 °C) within compartment. Further studies identifying the efficient monitoring of thermal condition and prompt interior environmental control are needed in vehicles for pig transport.

Assessing the feather condition of broilers is crucial for monitoring the animal welfare status and detecting the occurrence of feather pecking activities. Currently, the feather condition of individual broilers is manually scored by trained experts. To provide a more objective and efficient tool for feather condition scoring, a novel deep learning-based model, named Feather Condition Scoring Network (FCS-Net), was proposed based on RGB and thermal infrared images. The FCS-Net model combined the ResNet18 architecture with the proposed Dense Feature Fusion (DFF) module, which can effectively learn the feature mapping relationship between RGB and thermal infrared images. Before inputting the images into the network, an image registration process was conducted to align the RGB and thermal infrared images. The results showed that the FCS-Net model had a good performance for feather condition scoring, with the Accuracy of 97.02%, the Precision of 96.99%, the Recall of 97.04%, the F₁ of 97.01%, and the Inference speed of 15.34 fps. Compared to the ResNet18_RGB model, which only utilise RGB images, the FCS-Net model showed notable improvements in Accuracy by 4.02%, Precision by 3.90%, Recall by 4.08%, and F₁ by 4.01%. Moreover, it was observed that the FCS-Net model focused more on the back region of the broilers through heatmap visualization. Furthermore, the algorithm was compared with six typical image recognition algorithms including VGG16, ResNet18, SE-ResNet18, DenseNet121, Mobilenet_V2, and Shufflenet_V2_x1_0, as well as the state-of-the-art (SOTA) feather condition assessment methods. The results showed that the FCS-Net model achieved better performance than the six algorithms and the SOTA feather condition assessment methods. This study provided a valuable reference for automated monitoring of feather condition scoring of broilers in smart farming.

Sowing depth is a critical factor in crop growth and is determined by both the soil conditions and the force of the opener. The trend for the future is to control sowing depth based on soil dynamic parameters. Therefore, this paper developed a downforce measurement and control system based on the ‘T’-shaped furrow opener and investigated the influence of soil dynamic parameters and opener downforce on sowing depth. A test-rig was constructed and the accuracy of the system in measuring downforce and controlling downforce and sowing depth was verified. The study shows that at different sowing depths, soil moisture, bulk density and their interaction have a significant effect on downforce (P < 0.01). As the moisture content decreases and the bulk density increases, the required downforce increases for the same sowing depth. A mathematical model of downforce-sowing depth-soil bulk density-soil moisture content was established using experimental data, with an R² of 0.916, VIF <5 and a Durbin-Watson value of 1.628. Field experiments show that, at an operating speed of 6 km h⁻¹, the control strategy based on the soil dynamic parameters predicted by downforce theory significantly outperformed the strategy of adjusting the downforce in response to perceived changes in downforce. This indicates that after dynamic and rapid measurement of soil bulk density and moisture content during field operations, sowing depth can be accurately controlled based on the directed downforce of the opener. The mathematical model provides a theoretical basis for sowing depth control based on soil dynamic parameters.

Sensor technologies were integrated into a commercial sensor-guided hoeing system to counteract the force of gravity and reduce crop damage caused by the offset of hoeing in maize fields on sloping terrains. For this study, a hoe was equipped with a contact disc, sensors, an electric cylinder, and a decision support system. The offset of the hoe could be compensated in real time based on the automatic adjustment angle of the support wheel. In maize, three field experiments were conducted over two years to evaluate the system on three different slope gradients (between 4 and 12°). Plant populations were measured in each plot one day before and during hoeing to evaluate crop damage. However, for support wheel angle, Slope Compensation Intensity (SCI) 2 and 3, there were no significant crop plant losses in any trials. As a result, there was no hoe drifting during the sensor-based guidance along the rows. It has been verified that the development presented is functional and can counteract the force of gravity on slopes. This development aims to optimise the use of precision mechanical weed control and support farmers during hoeing on hilly terrain.

Pressurised sand filters used in drip irrigation need periodic backwashing to flush the contaminant particles out of the porous media. This process consumes high amounts of energy and water. The selection of more efficient backwashing operational conditions requires accurate information of the pressure drop and the bed expansion, the latter being not measured in commercial filters. An experimental study with a scaled filter that used a window to observe the bed expansion was conducted with three porous media types (glass microspheres and two silica sands), two packed media bed heights (200 mm and 300 mm) and four nozzles (one commercial and three prototypes). The 24 combinations of the filter experimental configuration were investigated for different superficial velocities. Both data and video recordings for all the 705 tests conducted were carefully analysed to obtain mean values and standard deviations of the height of the expanded bed. The behaviour of the fluidised bed dynamics was characterised. Results indicated that the nozzle design had a strong influence on the pressure drop, and, in consequence, on the power required for backwashing. It also had an observable impact on the fluidised bed dynamics although its effect on determining the overall height of the expanded bed was limited, this being more dependent on the type of the porous media. The most effective combination in terms of energy efficiency and porosity of the expanded bed was obtained with microspheres, though its retention efficiency might be questionable from the literature review, and the frustoconical nozzle geometry.

In the liquorice-soil composite shear test, flexible thick roots bend to create soil resistance which makes measurement results inaccurate. However, the liquorice pulling force characterises the contact strength of the liquorice-soil composite and can be used to study the root-soil interactions. This paper proposed a three-part modelling method to model the liquorice-soil composite at harvesting period. The mechanical parameters of soil particles were calibrated using the soil unconfined compressive strength test. The calibration results showed that the errors of peak force and peak displacement for soil unconfined compressive strength tests were 1.09% and 1.64%, respectively. The flexible liquorice model was constructed based on 3D scanning and particle filling methods, and the simulation model was calibrated based on compression properties. The relative errors in calibration of the flexible liquorice's radial and axial compression forces were 1.35% and 3.9%, respectively. Simplifying liquorice pulling force and liquorice surface area into a linear correlation effectively supports the general modelling method. The contact parameters between soil and liquorice were determined using liquorice pulling force as the target value, and the proportional calibration method was used to improve simulation efficiency. The calibration error for the liquorice pulling force is 4.39%. In addition, the results of the pulling force for the different surface areas show that the calibrated parameters are valid within a liquorice surface area of 0.0075–0.0181 m². This study provided a general and accurate simulation method to the liquorice-soil composite, which can be used as the reference for modelling the long root-soil composite, and provide methodological support for developing root crop harvesters.

Investigations into the use of urease inhibitors for reducing ammonia emission in dairy farming have been published in several papers. The aim of this study is to expand the existing knowledge on the use of urease inhibitors for reducing ammonia emissions in fattening pig houses. In this respect, in addition to the proven standard application approach using a backpack sprayer, the investigation was extended to include different application techniques.

Urease inhibitor was applied on two farms over six experimental periods throughout the year using three different application techniques: a backpack sprayer, and a semi-automatic system that applies the inhibitor both on-floor and under-floor. Two identical compartments, alternated between treatment and control, were used on each farm. A linear mixed model with repeated measurements was used to quantify the reduction effect of the urease inhibitor.

The use of the backpack sprayer led to a reduction in ammonia emissions of 22.9% (standard error, SE: 4.9%). The on-floor application system reduced the emissions by 16.6% (SE: 4.9%), and the under-floor application system resulted in no significant reduction.

The development of the semi-automatic application system can be considered beneficial for reducing emissions. However, further development and improvement of this application system is necessary for its widespread practical use, especially regarding the distribution accuracy of the application liquid, contamination issues, and the manual workload. In addition, the effects of the presence of the animals during the application process need to be investigated in more detail.