## 1 Introduction
Deep learning approaches for the semantic segmentation of plants, including R-CNN, pyramid networks, and encoder-decoder-based models, rely on the concept of CNN modelling [13]. These methods have been extensively used in plant organ segmentation for root and shoot phenotyping. These include the works of [PERSON] and [PERSON] [10], who used SegNet architecture, a deconvolutional network without a fully connected layer to segment plant leaves from its background. [PERSON] et al. [14] also performed instance segmentation to detect and discriminate leaf boundaries, using a pyramid convolution neural network. Recently, [PERSON] et al. [15] developed a fully automated segmentation pipeline based on pretrained U-net deep learning models to phenotype shoots of different crops. Although deep learning methods have been extensively used in plant segmentation and phenotyping, they require a large-volume labelled dataset, which is not readily available [16]. Furthermore, deep learning models have a high number of trainable parameters (e.g., Resnet18 has around 11 million, while denseNet has around 8 million trainable parameters), resulting in the slow processing of data, particularly when dealing with high-throughput data. This forms a bottleneck around plant phenotyping, especially when dealing with a small number of plants datasets. Due to these limitations, deep learning approaches were not considered in this study. Many segmentation methods rely on color information [17], which is a strong descriptor used in object detection and recognition. However, color-based segmentation may not perform well in certain scenarios, such as cluttered backgrounds, variable illumination conditions, etc.). Plant color can vary depending on the position of the sun during image acquisition [18], which can lead to poor segmentation results due to the change in color caused by different illumination conditions. Using different color spaces can minimize the effects of illumination and color variability on segmentation. Color spaces provide additional information about pixel intensity and color distribution and can be used individually or in combination to segment plant images [19]. [PERSON] et al. [20] combined different color spaces to segment and detect mango trees. They transformed the RGB color space to HSV (Hue Saturation Value) and YCbCr (luminance chrominance), where individual color channels were combined with Otsu thresholding for segmentation. Their results showed Cr from YCbCr as effective at separating mango trees from the background. Similarly, [PERSON] et al. [3] used multiple color features obtained from different color spaces to train a supervised machine learning classifier for wheat segmentation in field images. They found that using multiple color spaces improved segmentation accuracy and was more robust to background noise and illumination changes compared to using a single-color space. Although the use of different color spaces for segmentation can be effective, it also can be computationally expensive, particularly when computing millions of pixels, and requires high computer speed and large memory capacity for processing. In addition, some of the individual color channels may have redundant effects on the segmentation process, while others may have little or no effect. Furthermore, this method may fail to properly segment plants with different shades of color and texture across different parts of the plant image if the appropriate color features are not utilized. This study sought to (1) develop an automated phenotyping pipeline based on the neural network to segment field- and glasshouse-based plant images for high-throughput plant phenotyping and (2) predict plant SPAD values based on the proposed and other existing segmentation methods.