Sugarcane stem nodes based on the maximum value points of the vertical projection function

Abstract

In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.

Sugarcane Bud Detection Method Based onYOLOv3-CSE Network

“…Currently, the directional planting of sugarcane seeds is mainly realized by identifying the sugarcane buds via human eyes, manually determining the direction of sugarcane buds and placing the sugarcane seeds, and such factors as high labor intensity and low work efficiency seriously hinder the development of the sugarcane industry. However, the detection and recognition of the sugarcane seeds at home and abroad primarily focus on the stem node identification at present 3,4 , aiming to realize automatic sugarcane seed cutting, but there is no related research report on the mechanized directional planting of sugarcane seeds. Significant breakthroughs have been made in deep learning technology for object detection in recent years 5 .…”

“…(2)The bounding box regression loss function was improved to strengthen the regression effect on sugarcane bud identification. (3)The Mosaic data augmentation method was introduced to enrich the diversity of data. (4)The SE-ResNet module was embedded to increase the ability of network model to identify sugarcane bud features.…”

Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision

“…The maximum average gray value was used to determine the position of the stem nodes, but the result was affected by the step length and the width of the template, with a recognition rate of 90.77%. Chen proposed a sugarcane stem node identification method based on the extreme point of a vertical projection function, and the recognition rate of three stem nodes was 95.00% [ 12 ]. Although scholars have made significant achievements, there are still some shortcomings in these studies.…”