## 5 Conclusions  
This study presented a novel method for automated and non-invasive segmentation of greenhouse and field plant images. Relevant information was extracted from sensitive features and used to train a one-layer MLP model that accurately distinguishes plant vegetation pixels from non-plant pixels. The model is capable of segmenting plants from different environments, ranging from glasshouses with simple backgrounds to field plants  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyper Parameters**} & \multicolumn{3}{c}{**Model**} \\ \cline{2-4}  & **MLP** & **SVM** & **RF** \\ \hline Hidden layers & 1 & - & - \\ Neurons & 20 & - & - \\ Activation function & ReLU & - & - \\ Kernel & - & RBF & - \\ C & - & 0.001 & - \\ Y & - & 1 & - \\ n\_estimator & - & - & 200 \\ max\_depth & - & - & 20 \\ min\_samples\_leaf & - & - & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal hyperparameters for the different classification models.  
with cluttered backgrounds and varying illumination conditions. The selected features were also used to train classical ML models, SVM and RF, and their performances were compared to the proposed method. Although the classical ML models performed well, the MLP outperformed them in segmenting both glasshouse- and field-based images with high precision. The robustness of all segmentation methods was tested on plants from the glasshouse (cowpea) and the field (wheat) under different illumination conditions, and the proposed method produced the best performance in both cases. This method is not limited to segmenting green vegetation, but also plants with diseases and nutrient deficiencies, making it suitable for high throughput segmentation of plant images in both glasshouse and field environments.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/plants12102035/s1](https://www.mdpi.com/article/10.3390/plants12102035/s1)]([https://www.mdpi.com/article/10.3390/plants12102035/s1](https://www.mdpi.com/article/10.3390/plants12102035/s1)). Table S1: Training, validation, and testing data the classification models Table S2: Training, validation, and testing data for the different machine learning models.  
Conceptualization, F.G.O. and D.C.; methodology, F.G.O. and P.S.-T.; software, F.G.O. and P.S.-T.; validation, M.J.H., F.M. and D.S.; formal analysis, F.G.O.; investigation, N.V. and L.G.; resources, M.C. and A.B.R.; data curation, F.G.O. and D.C.; writing--original draft preparation, F.G.O.; writing--review and editing, M.J.H., N.V., F.M., P.S.-T., D.S. and L.G.; visualization and supervision, M.J.H. and F.M.; project administration, M.M.; funding acquisition, M.J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by OCP S.A. under the University of Mohammed VI Polytechnic, Rothamsted Research and Cranfield University project (FPO4). Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through the Designing Future Wheat program (BB/P016855/1).  
Not applicable.  
The data used in this study are available from the corresponding author on request.  
The authors declare no conflict of interest.