## 1 Introduction  
There is an increasing need to improve agriculture to meet the 2050 global food demand agenda. One aspect of this improvement is the implementation of high-throughput plant phenotyping (HTPP) to enable the large-scale evaluation of plant performances in breeding programs. HTTP involves the automatic sensing of plant physical features to understand the dynamic relationship between plants and their environment [1]. This method could involve the use of non-invasive methods to collect plant data and aims to analyze plant physiology, phenology, and morphology by using sequentially acquired images from multiple sensors, including visible RGB, thermal, hyperspectral cameras, 3D laser scanners, and fluorescence imagers, taken from multiple view angles [2]. Digital RGB cameras are the most common and widely used image-sensing tools due to their relatively low cost, high resolution, and portability [3]. RGB cameras measure the light reflected from objects by using three broad bands centered at 650 nm (red), 550 nm (green), and 450 nm (blue) [4]. In processing digital images, segmentation is a critical step that separates and identifies regions of interest in images. At the basic level, segmentation separates image pixels into two classes: foreground (i.e., plant vegetation) and background (i.e., non-plant objects). Over the years, several segmentation techniques have been developed, which can broadly be categorized as threshold, color-index, and learning-based methods [5]. Threshold-based methods separate image pixels into two groups based on their intensity values above or below a given threshold. This technique transforms an image from RGB to grayscale and binarizes the gray level based on the threshold value. The threshold is either global (using a single threshold value) or variable (threshold value varies). Binary, adaptive, and Otsu thresholding are examples of commonly used threshold-based segmentation techniques. Color-index-based techniques use combinations of color indices to differentiate plant vegetation from other non-plant objects. This method works on the assumption that the histogram distribution of an input image is bimodal and can be distinctively separated by a color threshold index. The color index of vegetation extraction (CIVE), excess green-red index (ExGR), and normalized difference index (NDI) are examples of commonly used color-based segmentation techniques [6]. [PERSON] and [PERSON] [7] developed an improved color index method (ExG-ExR) to segment plants (from the glasshouse) with field illumination conditions from their background. [PERSON] et al. [8] utilized a combination of color and threshold methods in estimating the leaf area index of cereal crops. Their method used a combination of greenness and intensity-derived indices from the red and green spectrum. Learning-based methods utilize supervised and unsupervised machine learning approaches to perform semantic and instance segmentation of plant images for disease detection [9], plant growth analysis [1], leaf counting [10], wheat spike counting [11], etc. Examples include k-means clustering, supervised mean-shift algorithm, fuzzy clustering, etc. Studies have shown that machine learning models, when trained with carefully selected features, outperform the segmentation methods described above. [PERSON] et al. [12] utilized a supervised learning method to segment plants by classifying pixels-based datasets whose labels were produced using a k-means clustering algorithm. Their results showed the better performance of their approach as compared to traditional thresholding methods. In recent times, deep learning, a branch of the learning-based method, has been extensively applied to segment plants with different backgrounds. A commonly used deep learning architecture called the convolution neural network has gained much attention in computer-vision-based plant phenotyping.