## 2 Related Works  
In recent years, as traditional agriculture has evolved toward intelligence, an increasing number of researchers have integrated information technology with conventional machinery to achieve precision agriculture. [PERSON] et al. [6] employed traditional image segmentation and semantic segmentation methods for sugarcane tip recognition and proposed an improved PSO + OTSU algorithm. By integrating an asymmetric acceleration factor with a nonlinearly decreasing inertia weight W into the PSO algorithm and utilizing the H component in the HSV color space, the method applies median filtering to remove small isolated noise areas, effectively segmenting the sugarcane stalk and tip regions. The improved algorithm achieved recognition rates of 88.33% and 91.67% for sugarcane images, with an average accuracy of 90.0%, and an average processing time of 0.3687 s per image. [PERSON] et al. [7] proposed a bifurcation point recognition method for sugarcane tips based on an improved YOLOv5s model. This model introduces a BiFPN feature fusion structure and a CA attention mechanism into YOLOv5s while incorporating GSConv convolution and a Slim-Neck paradigm. Additionally, a Ghost module replaces the standard convolution in the backbone network to reduce computational complexity and parameter size. The improved model achieved an average precision of 92.3%, a recall rate of 89.3%, and a detection time of 19.3 ms. [PERSON] et al. [8] proposed a method for estimating the cutting position height of sugarcane tips based on multimodal registration and depth image fusion. The measured cutting height closely matched the manually measured actual cutting height, with a root mean square error (RMSE) ranging from 1.22 cm to 1.78 cm and an R2 value between 0.79 and 0.86. Beyond the aforementioned studies on adaptive adjustment of sugarcane tail tip cutting positions, significant research has been conducted both domestically and internationally on the application of deep learning in intelligent sugarcane processing. [PERSON] et al. [9] employed a deep learning-based convolutional neural network (CNN) to identify sugarcane leaf diseases in the field. [PERSON] et al. [10] developed a sugarcane cutting system based on machine vision to achieve bud protection and automatic single-bud segment cut ting. The system integrates mechanical, electrical, and visual processing components, with machine vision serving as the core technology for identifying sugarcane stalk segments. The experiments showed a recognition accuracy of 93% and an average processing time of 0.539 s. The throughput capacity of the cutting unit reached 2400 buds per hour, and the cutting point precision met agricultural requirements, with the bud damage rate reduced to zero, providing a practical foundation for intelligent sugarcane seed cutting machinery. [PERSON] et al. [11] developed a machine vision-based system for the automatic cutting of sugarcane single-bud segments. The system uses image processing techniques such as mean filtering, thresholding, morphological operations, and maximum area selection to identify the position of sugarcane internodes in the H component of the HSV color space. The method also utilizes a rectangular template to move along the G-B color difference component map of the image, calculating the average grayscale value for each step, and identifying the position with the highest average grayscale value as the internode location. Experimental results showed that, in 36 combination tests, the optimal combination, with a template width and step size of 6, achieved a recognition rate of 90.77% and an average processing time of 0.481539 s.  
The above research has greatly promoted the development of all-round intelligence of sugarcane, which is of great practical significance.