## 3 Results and Discussion
### Model Performance  
In order to discriminate between plants growing with different nitrogen fertilization levels and under different water availability the five algorithms were tested. For these models, the overall accuracy and kappa coefficient used as the evaluation indicator were tabulated in Tables 1 and 2. The results showed that all studied models achieved overall accuracies of 67-83% and kappa coefficients of 0.59-0.81. It can be stated that the water supply level did not influence model accuracies. Among all models, the RF method resulted in the highest, whether overall accuracy or kappa coefficient. The overall accuracies were greater than 81% and 78% for sugar beet and celery plants, respectively. The kappa coefficient obtained for sugar beet and celery was higher than 0.74 and 0.70, respectively. The potential of this algorithm for the prediction of leaf N content was confirmed in previous studies [57,58]. The performance of the classification model depends on the ability to accurately forecast the target classes of the unlabeled instances. To demonstrate the class-wise effectiveness of the RF model, the TP rate, FP Rate, precision, recall, and F1 score are presented in Tables 3 and 4, respectively. Among the two studied plants, the model performance was a little bit better for HW and OW than for LW water treatments. The F1- score, which is calculated as the harmonic mean of the model's precision and recall, evaluates the accuracy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Validation Set**} \\ \cline{3-6}  & & **Overall** & **Kappa** & **Overall** & **Kappa** \\  & & **Accuracy (\%)** & **Coefficient** & **Accuracy (\%)** & **Coefficient** \\ \hline \multirow{6}{*}{HW} & BNN & 98 & 0.98 & 83 & 0.78 \\  & Logistic & 66 & 0.54 & 78 & 0.70 \\  & RF & 100 & 1 & 86 & 0.81 \\  & SVM & 83 & 0.78 & 75 & 0.67 \\  & kNN & 100 & 1 & 78 & 0.70 \\ \hline \multirow{6}{*}{OW} & BNN & 96 & 0.95 & 78 & 0.70 \\  & Logistic & 83 & 0.78 & 80 & 0.74 \\  & RF & 100 & 1 & 83 & 0.78 \\  & SVM & 0.85 & 0.80 & 80 & 0.74 \\  & kNN & 100 & 1 & 78 & 0.70 \\ \hline \multirow{6}{*}{LW} & BNN & 92 & 0.89 & 69 & 0.59 \\  & Logistic & 81 & 0.75 & 72 & 0.63 \\ \cline{1-1}  & RF & 100 & 1 & 81 & 0.74 \\ \cline{1-1}  & SVM & 78 & 0.70 & 72 & 0.63 \\ \cline{1-1}  & kNN & 100 & 1 & 67 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall accuracy and kappa coefficient of the models for classification of sugar beet plants subjected to four leaf nitrogen supplies and three water treatments. LW: low water supply, OW: optimum water supply, HW: high water supply. of a model. In the case of both plants, both classes achieved a similar F1 score, ranging from 0.706 to 1.00 and from 0.706 to 0.941 for sugar beet and celery plants, respectively. A good result was obtained for the detection of nitrogen deficiency. The F1 scores for N33 and N67 classes were higher than 0.73 and 0.70 for sugar beet and celery plants, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Validation Set**} \\ \cline{3-6}  & & \multicolumn{1}{c}{**Overall**} & \multicolumn{1}{c}{**Kappa**} & \multicolumn{1}{c}{**Overall**} & \multicolumn{1}{c}{**Kappa**} \\  & & **Accuracy (\%)** & **Coefficient** & **Accuracy (\%)** & **Coefficient** \\ \hline \multirow{5}{*}{HW} & BNN & 93 & 0.91 & 78 & 0.70 \\  & Logistic & 86 & 0.81 & 75 & 0.67 \\  & RF & 100 & \(1\) & 81 & 0.74 \\  & SVM & 68 & 0.58 & 69 & 0.59 \\  & kNN & 100 & \(1\) & 75 & 0.67 \\ \hline \multirow{5}{*}{OW} & BNN & 97 & 0.96 & 72 & 0.63 \\  & Logistic & 86 & 0.81 & 78 & 0.70 \\  & RF & 100 & \(1\) & 81 & 0.74 \\  & SVM & 86 & 0.81 & 75 & 0.67 \\  & kNN & 100 & \(1\) & 67 & 0.56 \\ \hline \multirow{5}{*}{LW} & BNN & 94 & 0.93 & 67 & 0.56 \\  & Logistic & 88 & 0.84 & 72 & 0.63 \\  & RF & 100 & \(1\) & 78 & 0.70 \\  & SVM & 84 & 0.79 & 69 & 0.59 \\  & kNN & 100 & \(1\) & 75 & 0.67 \\ \hline \hline \end{tabular}
\end{table}