## 1 Introduction  
Sugarcane is an important ingredient for sugar production. Although it is widely planted in China, less than 15% of sugarcane is harvested mechanically. The current sugarcane harvesters are mainly large non-intelligent combined harvesters with a high impurity content and high broken root rate, which is not suitable for the small hilly planting areas in southern China. Therefore, it is necessary to develop a small sugarcane harvester with high sugarcane harvesting quality that is suitable for small plots. Inspired by human sugarcane harvesting, the intelligent identification and location of the stem node and cutting position can effectively reduce the impurity rate and broken root rate. In order to realise a sugarcane harvesting robot that can simulate manual harvesting, the key is to identify and locate the cutting position of the sugarcane by simulating human eyes. For a harvesting robot, the most important thing is to complete the task of identifying and locating the harvesting target in space. This task can be divided into two stages. The first stage is the target identification and two-dimensional location; the second stage is the three-dimensional location. At present, on the basis of the two-dimensional location, the three-dimensional positioning of the harvesting robot is acquired by obtaining depth information through 3D sensors, such as a time-of-flight (TOF) 3D camera, stereo camera, or structured light camera. For example, [PERSON] et al. [1] designed a 7-DOF apple-picking robot with a precise location ability. The Circular Hough Transformation (CHT) was adopted to identify clearly visible individual apples in clusters, and Blob Analysis (BA) was applied in an iterative fashion to identify partially visible apples. Then, a TOF-based 3D camera (Camcube 3.0) was used to obtain the spatial three-dimensional coordinates of the apple. [PERSON] et al. [2] put forward a method of identifying and locating apple stems and calves based on near infrared array structured light and 3D reconstruction. Firstly, identification of the apple stems and calyxes was completed by an image processing algorithm, and then their depth information was obtained by structured light. [PERSON] et al. [3] designed a novel multi-arm kiwifruit harvesting robot, which detected kiwifruit by the FCN-8S algorithm and located the fruit by several stereo cameras installed below. In the literature there have been many research works on the identification and spatial location of apple [1,2], kiwifruit [3], tomato [4], litchi [5] and other crops in the natural environment, but research on the identification and spatial location of sugarcane nodes in the natural environment has rarely been reported. A few scholars have studied the identification of the stem nodes of sugarcane without leaves in constant light [6, 7, 8]. There are several difficulties in locating sugarcane nodes in a complex environment. Firstly, in the stage of stem node identification, the complex form of sugarcane and the light changes in the natural environment, such as clumped growth, stalk cover and being wrapped by sugarcane leaves, lead to the need for a huge amount of recognition data collection, a large number of image features, and result in reduced recognition accuracy. In order to solve the problem of identifying the complex form of stem nodes, [PERSON] et al. [9] introduced the deep learning algorithm YOLOv4 [10] as a preliminary study on the identification of stem nodes in the complex field environment. However, although the series of YOLO algorithms and their improved methods [11,12] have high detection accuracy in the agricultural field, their model parameters are huge and the demand for a GPU is too high, limiting their application on the embedded chip of the harvesting robot. Secondly, in the three-dimensional location stage of sugarcane nodes, mismatching often occurs due to the similarity of the root stem node and sugarcane leaves, which leads to larger location error.