## 2 Results
### Segmentation Accuracy Analysis  
Segmentation accuracy was evaluated using three quality factors, namely Qseg, Sr, and an error factor (Es), as explained in Section 4.5. Figure 2 shows examples of segmented wheat and cowpea images, using the MLP and ExG methods. By visual inspection, the proposed method produced high-quality images as compared to the traditional ExG segmentation method.  
Moreover, as shown in Figure 3, the ML-based methods (MLP, SVM, and RF) outperformed the color-index methods (ExG and ExGR) for glasshouse-based cowpea plants. The MLP had the highest average Qseg (0.916) and Sr (0.956), with the lowest standard deviations (0.012 and 0.040, respectively), indicating less variability in the Qseg and Sr scores. The RF model followed closely, achieving 0.874 (Qseg) and 0.932 (Sr), with standard deviations of 0.046 and 0.044, respectively. ExG had the lowest Qseg and Sr values (0.785 and 0.816), with the highest Es (0.220) indicating more misclassification of pixels. Again,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**All Features Dataset (AL)**} & \multicolumn{4}{c}{**Selected Features Dataset (SF)**} \\ \hline
**Learning Model** & **Accuracy** & **F-Score** & **Recall** & **Computational** & **Accuracy** & **F-Score** & **Recall** & **Computational** \\  & & & & & **Time (s)** & & & **Time (s)** \\ \hline MLP & 98.464  & 96.142  & 98.144  & 175.098  & 99.382  & 97.423  & 98.321  & 113.521  \\ RF & 98.011  & 95.331  & 97.421  & 191.450  & 98.354  & 96.632  & 97.931  & 128.455  \\ SVM & 96.134  & 92.402  & 95.651  & 560.560  & 96.624  & 92.822  & 96.514  & 321.502  \\ \hline \hline \end{tabular}  
* Superscripts [FOOTNONOTE:2]Footnote 2: footnotemark: [ENDFOOTNOTE]  
\end{table}
Table 1: Classification model performance assessment. MLP, RF, and SVM are the multilayer perceptron, random forest, and support vector machine segmentation models, respectively.  
Figure 1: Classification accuracy scores for the three models (multilayer perceptron (MLP), random forest (RF), and support vector machines (SVMs) based on selected features dataset (SF) and all features (AF) dataset for training.  
MLP had the lowest classification error (0.058), followed by RF and SVM (0.074 and 0.170, respectively).  
The results presented in Figure 4 shows that the color-index-based methods (ExG and ExGR) had the lowest Qseg values (0.725 and 0.693, respectively), with high standard  
Figure 3: Mean segmentation accuracy rate comparison for quality assessment of five different segmentation methods for glasshouse-based images; Qseg measures the segmentation consistency on a pixel-by-pixel basis, Sr measures the consistency of plant pixels between image regions, and Es measures the rate of pixel misclassification. These were applied on the five segmentation methods: multilayer perceptron (MLP), support vector machines (SVMs), random forest (RF), excess green (ExG), and excess green-red (ExGR).  
Figure 2: Examples of glasshouse and field segmented plants using the proposed method and selected segmentation methods. (**a**) Original wheat image, (**b**) ExG wheat segmented image, (**c**) proposed method (MLP) segmented image, (**d**) original cowpea image, (**e**) ExG cowpea segmented image, and (**f**) MLP segmented image.  
deviations (0.082 and 0.112, respectively) for field-based wheat images. On the other hand, the MLP method had the best performance, earning the highest Qseg rating (0.880), with a low standard deviation (0.055). This indicates that the MLP model is the best model, producing better pixel classification with an error rate of 0.128 as compared to the other techniques. In both environments, i.e., the glasshouse and the field, the ML-based methods outperformed the color-based methods, and this is consistent to the finding of [PERSON] et al. (2012), who suggested that ML-based techniques are an improvement over traditional segmentation methods.