# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 1 Introduction  
The area occupied by sugarcane planting in China ranks third in the world. However, the mechanism of sugarcane harvesting is still at a relatively low level, for the main reasons that the mechanical harvesting destroys the stem nodes kept in the soil for the second year of growth, the impurity rate is high and the cutter is seriously worn by cutting into the soil. In contrast, although inefficient and labour-intensive, manual harvesting is widely adopted with good quality and flexibility. Therefore, it is necessary to improve the intelligence of sugarcane mechanical harvesting, and the recognition of the sugarcane cutting location is the first step toward intelligence. Machine vision technology offers the possibility of identifying sugarcane stem nodes against a single background. [PERSON] et al. [1] first studied the recognition of sugarcane stem nodes by comparing the diameter of different parts of the sugarcane and found that the diameter of the stem node was larger than the rest, which can be used to determine the position of the stem section. [PERSON] et al. [2] proposed a feature extraction and recognition method of sugarcane stem nodes through the support vector machinemethod by extracting features of the S and H component images in the HSV colour space of sugarcane segment pictures. However, the background of the sugarcane image was an ideal one with a single colour. The local mean [3] was another method used to identify the sugarcane stem node by filtering the image and image segmentation on H components of the HSV colour space, and it then found the maximum grey value as the stem node position. The experimental object was the image with only a single sugarcane stem node. [PERSON] et al. [4] studied the method of identifying and locating sugarcane stem nodes based on high spectral light imaging technology. Its recognition range was limited to the area around the sugarcane stem node, and the recognition accuracy was 98.33%. [PERSON] et al. [5] proposed a sugarcane node recognition algorithm based on multi-threshold and multi-scale wavelet transform, even though the sugarcane could only be identified by stripping the sugarcane leaves in advance to expose the sugarcane node. [PERSON] et al. [6] proposed a method of sugarcane stem node recognition based on Sobel edge detection to satisfy the working requirements of the sugarcane seed cutter. [PERSON] et al. [7] proposed a sugarcane nodes identification algorithm based on the sum of local pixels of the minimum points of vertical projection function to analyse the recognition of a single node and double nodes. The methods mentioned above mainly relied on the traditional image-processing machine version method. It is clear that the algorithm of the machine version can identify the object mainly by analysis of the reflecting light and perspective light on the surface of the object, which needs to work in a simple environment. Although some of them cannot meet the requirements of real-time detection against a complex background or deal with sugarcane stem node recognition amidst sugarcane leaf wrapping, the research still puts forward feasible visual identification techniques for sugarcane seeding or harvesting machines. Unlike traditional image processing that focuses on image feature recognition, deep learning is a learning method driven by big data, which has been widely used in agriculture [8; 9; 10], crop classification [11], crop image segmentation [12] and crop object detection [13]. [PERSON], [PERSON] et al. [14] used the Faster R-CNN based on a deep learning algorithm to detect coconut in a complex background. [PERSON], [PERSON] et al. [15] studied the performance of the SSD algorithm in identifying lychee fruits and litchee branches at night. In 2020, [PERSON], [PERSON] et al. [16] studied the detection of apples in apple orchards based on the RetinaNet algorithm through the ground remote sensing system. [PERSON], [PERSON] et al.