Cutting the roots of sugarcane using cutters is a critical part of sugarcane harvesting, and the degree of breakage of the roots after cutting affects the germination and growth of sugarcane to a certain extent in the following year. However, the intricate interactions between the cutter and the stalk remain unclear. In order to fill this gap, this study first analyzed the conditions for no missed cuts during the operation of a double-disk cutter. Secondly, the research established a model of sugarcane stalk with anisotropy using the User-defined Material Mechanical Behavior (UMAT) subroutine based on the secondary development module of ABAQUS/Explicit. The cutting force curves obtained from simulation and test show a high correlation coefficient (R²=0.9621), indicating the reliability of the model of sugarcane stalk in mechanical transfer. Subsequently, the simulation test of the blade rotating cutting characteristics in this study indicates that at a blade tilt angle of 11.3°, a blade rotating speed of 659.3 r/min, and a forward speed of 1.5 km/h, the maximum shear force on the blade is the largest, while the maximum cutting force is the smallest. Finally, based on the simulation results, this paper discussed the internal factors affecting the breakage rate of sugarcane stalks and predicted the damage location and damage force of the stalks by studying the stress wave transmission effect. Additionally, it analyzed the effects of single-knife cutting and multi-cutting on stalk incisions. The results indicated that multi-cutting causes more damage to the stalks and increases the breakage rate of sugarcane. The results of this study can provide a theoretical basis and technical reference for exploring the reduction of sugarcane residual cutting rate.

The traditional recognition algorithm is prone to miss detection targets in the complex tea garden environment, and it is difficult to satisfy the requirement for tea bud recognition accuracy and efficiency. In this study, the YOLOv7 model was developed to improve tea bud recognition accuracy for some extreme tea garden scenarios. In the improved model, a lightweight MobileNetV3 network is adopted to replace the original backbone network, which reduces the size of the model and improves detection efficiency. The convolutional block attention module is introduced to enhance the attention to the features of small and occluded tea buds, suppressing the interference of the complex tea garden environment on tea bud recognition and strengthening the feature extraction capability of the recognition model. Moreover, to further improve recognition accuracy for dense and occlusive scenarios, the soft non-maximum suppression strategy is integrated into the recognition model. Experimental results show that the improved YOLOv7 model has the precision, recall, and mean average precision (mAP) values of 88.3%, 87.4%, and 88.5%, respectively. Compared with the Faster R-CNN, SSD, and original YOLOv7 algorithms, the mAP of the improved YOLOv7 model is increased by 7.4, 7.9, and 3.9 percentage points, respectively, and its recognition speed is also promoted by 94.9%, 46.2%, and 16.9%. The proposed model can rapidly and accurately identify the tea buds in multiple complex tea garden scenarios - such as dense distribution, being close to the background color, and mutual occlusion - with high generalization and robustness, which can provide theoretical and technical support for the recognition of tea-picking robots.