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.