## 4 Results
### Carotenoids Content Estimation
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Plant** & **Spectral Range and** & **Dimensionality Reduction Techniques** & **Accuracy and Technique** & **Number of Samples** & **Ref.** \\  & **Instrument** & & & Multivariate PLS & \\  & & & Data dimensionality: First-order derivative & R = 0.9099 NRMSE = 0.4147. & \\  & & & Multivariate PCR & \\ (baby leaf) & 350 nm\(-\)2500 nm & & 13 most important principal components. & R = 0.9094 NRMSE = 0.4159. & 300 & [22] \\  & & & Multivariate ANN with normalized full HSI data & & \\  & & & Multivariate & ANN with normalized full HSI data & & \\  & & & R = 0.8424 NRMSE = 0.3737. & & \\ \hline \multirow{6}{*}{Spinach} & \multirow{6}{*}{Dual-channel diode array spectrometer: 305 nm\(-\)1800 nm} & Data dimensionality: Three methods: Competitive adaptive reweighed sampling & \multicolumn{1}{l}{CAS-PLSR} & \multirow{6}{*}{1120} & \multirow{6}{*}{[27]} \\  & & (CARS) was used to eliminate the irrelevant & R = 0.989 RMSEP = 0.10 & & \\  & & variables of PLS. Vis. PRI PRI PRI 500, PRI & PUI linear model: & & \\  & & norm, RDV, VOGER3, NDVI, nmTVDVI, & REIP & & \\  & & TCAR, REIP, WI calculated from the & & Best IP linear model: & \\  & & spectrum Parameters based on inflection points (IFVs) of the spectrum (first and second derivatives) & & & \\ \hline \multirow{6}{*}{Lettuco} & \multirow{6}{*}{Fiberoptic spectrometer: 350 nm\(-\)2500 nm} & AutoML (OGBoost, All Indices) R\({}^{2}\) 0.93 RMSE N/A & \multirow{6}{*}{[30]} \\  & & & AutoML (GRV) R\({}^{2}\) 0.91 RMSE 5.50 & & \\  & & & AutoML (Clgren) R\({}^{2}\) 0.90 RMSE 6.15 & & \\  & & & AutoML (MCACH) R\({}^{2}\) 0.888 RMSE 6.43 & & \\  & & & AutoML (NDVD) R\({}^{2}\) 0.89 RMSE 6.20 & & \\  & & & Random Forest (All Indices) R\({}^{2}\) 0.91 RMSE N/A & 3600 fiberoptic measurement & \\  & & & Random Forest (KRV) R\({}^{2}\) 0.98 RMSE 5.81 & & \\  & & & Random Forest (NVD) R\({}^{2}\) 0.78 RMSE 5.00 & & \\  & & & PISR (GRV) R\({}^{2}\) 0.82 RMSE 7.40 & & \\  & & & PISR (NOVD) R\({}^{2}\) 0.79 RMSE 7.44 & & \\  & & & BFNN (Clgren) R\({}^{2}\) 0.87 RMSE 6.23 & & \\  & & & BFNN (NDV) R\({}^{2}\) 0.84 RMSE 6.63 & & \\  & & & SVM (GRV) R\({}^{2}\) 0.81 RMSE 8.81 & & \\  & & & SVM (NDV) R\({}^{2}\) 0.77 RMSE 7.94 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chlorophyll. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Plant** & \begin{tabular}{l} **Spectral Range and** \\ **Instrument** \\ \end{tabular} & \begin{tabular}{l} **Dimensionality Reduction Techniques** \\ \end{tabular} & \begin{tabular}{l} **Accuracy and Technique** \\ \end{tabular} & \begin{tabular}{l} **Number of** \\ **Samples** \\ \end{tabular} & **Ref.** \\ \hline \multirow{4}{*}{\begin{tabular}{l} Lettuce \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{l} Hyperspectral Camera: \\ 400 nm–1000 nm \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{l} Data dimensionality: Spectral index (CI700) \\ from the HRI \\ \end{tabular} } & \
\begin{tabular}{l} **Accuracy and Technique** \\ \end{tabular} &
\begin{tabular}{l} **Number of** \\ **Samples** \\ \end{tabular} & **Ref.** \\ \cline{1-1}  & & & Best results of each method over four lettuce species & \\ \cline{1-1}  & & & cultivated with two different systems each (8 datasets) & \\ \cline{1-1}  & & & Results for hydroponically grown black-seeded & \\ \cline{1-1}  & & & - First derivative of the reflectance to identify & Simpson lettuce: & \\ \cline{1-1}  & & band regions with the most differences & FDR: & \(\text{RP}^{2}=0.95\) RMSE \(=1.05\) & Not specified \\ \cline{1-1}  & & - PLRS/PCA: & & \\ \cline{1-1}  & & - VIP score of a predictor variable over \"1\" & \(\text{RP}^{2}=0.94\) RMSE \(=1.171\) & \\ \cline{1-1}  & & & VIP Score Index & \\ \cline{1-1}  & & & RP\({}^{2}=0.99\) RMSE \(=0.03\) & \\ \cline{1-1}  & & & However, there is high performance variability & \\ \cline{1-1}  & & & depending on the dataset & \\ \hline \hline \end{tabular}
\end{table} 
Table 4. Chlorophyll.