The study aims to fully explicate the bending behavior of tea stalks under the condition of large deflection, which is crucial to improve the working performance of mechanized harvesting equipment. The mechanical model of the stalk was assumed to be a non-prismatic beam with virtual internodes that could differ from actual internodes. With the model, the stalk can be freely divided into multiple virtual internodes, whose flexural rigidities can be determined by solving an optimization problem, and deflection curves can be predicted after determining the positions of virtual nodes under given loads. Moreover, a novel method was proposed to obtain the deflection curve of the stalk based on the techniques of binocular vision and non-uniform rational B-spline (NURBS) curve fitting. The results show that R-squared values of fitted 2nd-degree NURBS curves of bending shape of tea stalks range from 0.9576 to 0.9964, with an average of 0.9797. The results indicate that flexural rigidity decreases from the bottom to the top of the tea stalk, and the deflection curve could be predicted more precisely with the model of piecewise flexural rigidities than that of average flexural rigidity. The study could be applied to the optimization design of the cutter and adaptive adjustment techniques of operational parameters for tea picking machines.

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1mm, MV < 2mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

The blade is one of the most critical components in the fracturing tea-picking machine, and this study is conducted to optimize the blade's working parameters. In this study, the effects of blade width, blade thickness, and cutting angle on the maximum fracturing force of tea stems were analyzed using the L9 (34) standard orthogonal table, with the maximum fracturing force used as the evaluation index. The results indicate that the main factors affecting the maximum fracturing force (MFF) of tea stems are cutting angle (CA), blade width (BW), and blade thickness (BT) in that order. Furthermore, microscopic observation of the fracture surface revealed that compared with the thickness of the other two blades, the thickness of 0 mm caused the cross-section uneven and had lots of burrs, correspondingly resulting in the section's oxidation and the deterioration of tea leaf quality. Therefore, the optimal combination of design parameters was a cutting angle of 90°, a blade width of 2.0 mm, and a blade thickness of 0.5 mm. The findings of this study can provide reference for blade design to reduce the fracturing force of tea-picking machines, lower the working power consumption, and improve the quality of freshly plucked tea leaves.