# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 1 Introduction  
Plant phenotype is the expression of a plant's genes and its external performance under the dynamic effects of genes and environment. The study of plant phenotype is of great significance in the field of crop breeding and so on [1]. Plant height is one of the most important morphological parameters in the study of plant phenotype. The acquisition of traditional phenotypic data such as plant height usually relies on manual measurements and collection, which have defects such as a low efficiency, being time consuming, a small scale, large errors, and a poor adaptability [2]. The application of sensors to collect plant height data can realize automatic and rapid acquisition, which has broad research prospects and is of great significance for the development of breeding, seedling raising, and other fields [3, 4]. Recently, a new branch has emerged in the field of phenomics, known as high-throughput phenotyping (HTP). Essentially, HTP harnesses modern data-sampling techniques to accumulate vast amounts of data, which significantly enhances the efficacy of phenotypic studies. By conducting multi-scale, comprehensive, and automated assessments, HTP dynamically monitors plant growth conditions. This approach enables a more effective exploration of the intricate 'genotype-phenotype-environment' interplay. As a result, crop phenotyping research is gradually shifting from traditional methodstowards HTP technologies. HTP integrates expertise from phenomics with computer science, engineering, and data analytics, facilitating the rapid acquisition of phenotypic data and the efficient extraction of crucial information from immense datasets. This is where HTP technology plays a pivotal role in agricultural development [5; 6]. Compared with traditional artificial plant height measurements, plant height measurements based on computer vision have the advantages of non-contact, a high precision, and automation. This can achieve efficient and non-destructive measurements, which is an important research direction in phenotypic measurement. [PERSON] et al. were among the first to anticipate the shift towards using deep learning techniques in phenotyping [7]. Since then, machine learning has shown its effectiveness in various automated agricultural systems [8; 9; 10]. Additionally, [PERSON] et al. conducted a review of studies utilizing machine learning algorithms in plant and agricultural research, highlighting their superior accuracy compared to traditional image-processing methods [11]. At present, there are certain research results in the related direction of the automatic collection of plant height information based on computer vision, mainly including the following methods: (1) measuring plant height based on the actual scale reference; (2) obtaining color and depth images of plants based on RGB-D cameras and calculating plant height; (3) obtaining three-dimensional information of the plant based on binocular camera disparity and calculating the plant height; and (4) the monocular depth estimation method based on a neural network, which can be used to obtain distance information and calculate plant height. All the above methods directly or indirectly obtain the relationship between the actual size and the pixel size, and then further calculate the plant height. The measurement method based on the reference object is relatively simple. By comparing the pixel relationship between the plant and the reference object, the pixel information and the known actual size information of the reference object are compared, and then the plant height is calculated. Single/stereo-vision cameras for plant height estimation can alleviate the bottleneck of manual plant height measurement [4; 12; 13]. [PERSON] et al. described a method to calculate the plant height by dividing the color area of the ruler with HSV color and taking the red and blue double-color ruler as the real reference [14]. [PERSON] et al.