Biosystems Engineering最新文献

To intelligently remove the yellow-rotten leaf of hydroponic lettuce, a new leaf removal method was proposed. The lettuce position was adjusted for yellow-rotten leaf removal according to the visual recognition and localisation, and then the adsorbed yellow-rotten leaf was lifted by air pipes. Finally, the leaf was clamped and removed along the flipping-tearing-twisting trajectory. The adsorbing pressure, adsorbing position, and clamping position for yellow-rotten leaf removal were confirmed by the adsorbing and stretching tests. To improve the leaf removal success rate, the tearing angle, flipping angle, and torsional time radio were optimised by Box-Behnken tests. A quadratic model for the three factors and leaf removal success rate was established to analyse the orders of significance, and the order of significance for single factor was (i) the tearing angle, (ii) the flipping angle, and (iii) the torsional time radio. The order of significance for interaction terms was (i) the flipping angle and tearing angle, and (ii) the flipping angle and torsional time ratio. The solved optimal combination of factors was a flipping angle of 100.5°, a tearing angle of 131.0°, and a torsional time ratio of 0.68, which gave the maximum leaf removal success rate. The optimal combination of factors was verified, and the leaf removal process was shot by high speed camera. The verification tests showed that the maximum leaf removal success rate was 82.8%, and the leaf removal process took 6.58 s, meeting the requirements of yellow-rotten leaf removal for hydroponic lettuce.

The main cause of damage to maize during harvesting and processing is impact damage. This study aimed to investigate the evolution of impact damage to maize kernels under different impact velocities and orientations. Based on the damage characteristics observed in impact tests, an elastoplastic model has been established to accurately simulate the damage behaviour of maize kernels. The microscopic impact behaviour of maize kernels was presented by the finite element method. The results indicated that there were differences in the evolution of damage for different damage morphology in maize kernels. The nature of surface damage was the diffusion and reflection of stress waves, while the nature of local breakage was the concentration of tiny cracks and the release of elastic potential energy. The nature of fracture was the combined effect of compressive and tensile stresses. Meanwhile, under the surface damage, the maximum stresses in the contact area of maize kernels subjected to front orientation were 20.08 MPa, 10.71 MPa for side orientation, and 13.56 MPa for bottom orientation. Under the local breakage, the front orientation with the highest number of cracks occurred at a velocity of 27.3 m s⁻¹, while for the side orientation, it occurred at 24.6 m s⁻¹, and for the bottom orientation, it occurred at 26.2 m s⁻¹. The results can be extended to the study of impact damage in irregularly shaped grains, which was beneficial for controlling product quality and optimising the design of relevant mechanical parameters in agricultural engineering and food engineering fields.

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.