# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 3 Principles and Methods
### Segmentation of Plants  
#### 3.3.1. Binarized Pixel Statistics  
The morphological structure of the plant body is greatly affected by the external environment, but has general characteristics, mainly composed of roots, stems, leaves, flowers, and fruits. In the study of plant height measurement, the organs above the ground are mainly concerned. This study mainly considered the stem and leaf parts, which accounted  
Figure 6: Main processing flow of image preprocessing.  
for most parts above the ground. The stem directly represents the plant height, and accurate stem height measurements provide insights into the plant growth status. Emphasizing the stem morphological features and using suitable image segmentation algorithms to precisely extract the stem region are key to achieving accurate plant height measurements. While leaves are not directly measured for plant height, their growth characteristics indirectly reflect the plant health and growth rate. Attending to leaf morphological features enhances the understanding of plant growth and provides a basis for subsequent growth regulation. Additionally, optimizing the segmentation of leaf regions serves to minimize interference with stem identification, thereby enhancing the accuracy of height measurements. In terms of morphological structure, most plant bodies have branched structures. Side branches grow from the side of the main stem or taproot, terminal buds grow from the tip of the stem, and lateral buds grow from the side of the stem [34; 35]. Plants grow radially symmetrical outward from the root or are axially symmetrical with the main stem as the symmetrical axis, so plants are mainly characterized by being dense in the middle and gradually outwardly sparse in appearance.  
In this study, the longitudinal pixels of the binarized image were counted, that is, the number of binarized pixels in each column was calculated, as follows:  
\[\mathrm{n_{j}}=\sum_{\mathrm{i}}\mathrm{n_{(j,i)}} \tag{5}\]  
Treating a binarized image as a two-dimensional matrix, where \(\mathrm{n_{(j,i)}}\) represents the binarized values of the pixels in row j and column i.  
The binary pixel count of the collected image will result in horizontally one-dimensional data, as shown in the following Figure 7:  
The individual plant conforms to the symmetry and branching characteristics of the plant morphology, so we analyze the symmetry of plant structures. Plants often exhibit symmetrical growth patterns, where the shape of the plant mainly has a dense middle and outward gradually sparse characteristics. By identifying symmetrical regions in the image, we can localize potential plant candidates. This symmetry-based analysis helps us to distinguish individual plants, then we utilize the branching patterns of plants to further refine the segmentation. The intersections and bifurcations of the branches create unique signatures that help to differentiate between overlapping plants. So, the distribution characteristics of the binarized pixel count results are mainly high in the middle and gradually reduced on both sides, that is, the distribution characteristics of the individual plant are a \"peak\". It can be seen that there are four \"peaks\" in the image, and each peak corresponds to a plant, that is, there are four plants.  
Figure 7: Binarized pixel count.