# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 3 Results
### Binary and Multi-Classification of Imbalanced Disease Classes: Deep Methods Trained from Scratch
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Category** & **Model** & **AUC** & **CA** & **F1-Score** & **Precision** & **Recall** & **MCC** \\ \hline \multirow{4}{*}{Average Over All Classes} & Neural Network & 0.9966 & 0.9086 & 0.9085 & 0.9084 & 0.9086 & 0.8905 \\  & KNN & 0.9952 & 0.9248 & 0.9237 & 0.9258 & 0.9248 & 0.9102 \\  & Random Forest & 0.9902 & 0.8855 & 0.8830 & 0.8814 & 0.8855 & 0.8625 \\  & Stack & 0.9979 & 0.9564 & 0.9562 & 0.9562 & 0.9564 & 0.9478 \\  & SVM & 0.9940 & 0.9206 & 0.9203 & 0.9201 & 0.9206 & 0.9048 \\ \hline \multirow{4}{*}{Banded Chlorosis} & Neural Network & 0.9944 & 0.9678 & 0.7941 & 0.7984 & 0.7899 & 0.7767 \\  & KNN & 0.9941 & 0.9727 & 0.8363 & 0.7909 & 0.8872 & 0.8231 \\  & Random Forest & 0.9804 & 0.9623 & 0.7489 & 0.7876 & 0.7138 & 0.7296 \\  & Stack & 0.9954 & 0.9817 & 0.8833 & 0.8847 & 0.8819 & 0.8734 \\  & SVM & 0.9875 & 0.9741 & 0.8355 & 0.8364 & 0.8346 & 0.8214 \\ \hline \multirow{4}{*}{Brown Spot} & Neural Network & 0.9990 & 0.9902 & 0.9831 & 0.9787 & 0.9875 & 0.9762 \\  & KNN & 0.9983 & 0.9914 & 0.9852 & 0.9819 & 0.9886 & 0.9792 \\  & Random Forest & 0.9956 & 0.9744 & 0.9569 & 0.9308 & 0.9845 & 0.9394 \\  & Stack & 0.9995 & 0.9932 & 0.9882 & 0.9835 & 0.9930 & 0.9835 \\  & SVM & 0.9973 & 0.9886 & 0.9803 & 0.9779 & 0.9828 & 0.9723 \\ \hline \multirow{4}{*}{Brown Rust} & Neural Network & 0.9945 & 0.9746 & 0.7595 & 0.7571 & 0.7619 & 0.7461 \\  & KNN & 0.9901 & 0.9783 & 0.7828 & 0.8282 & 0.7421 & 0.7726 \\  & Random Forest & 0.9804 & 0.9710 & 0.7120 & 0.7461 & 0.6810 & 0.6976 \\  & Stack & 0.9962 & 0.9844 & 0.8478 & 0.8698 & 0.8270 & 0.8399 \\  & SVM & 0.9877 & 0.9785 & 0.7960 & 0.7976 & 0.7944 & 0.7847 \\ \hline \multirow{4}{*}{Grassy Shoot} & Neural Network & 0.9999 & 0.9987 & 0.9888 & 0.9898 & 0.9877 & 0.9881 \\  & KNN & 0.9991 & 0.9990 & 0.9917 & 0.9906 & 0.9928 & 0.9912 \\  & Random Forest & 0.9998 & 0.9982 & 0.9848 & 0.9848 & 0.9848 & 0.9839 \\  & Stack & 0.9999 & 0.9992 & 0.9931 & 0.9942 & 0.9920 & 0.9927 \\  & SVM & 0.9999 & 0.9992 & 0.9927 & 0.9949 & 0.9906 & 0.9923 \\ \hline \multirow{4}{*}{Pokkah Boeng} & Neural Network & 0.9918 & 0.9660 & 0.6588 & 0.6525 & 0.6653 & 0.6410 \\  & KNN & 0.9914 & 0.9732 & 0.7568 & 0.6860 & 0.8441 & 0.7473 \\  & Random Forest & 0.9773 & 0.9640 & 0.6287 & 0.6408 & 0.6170 & 0.6099 \\  & Stack & 0.9987 & 0.9927 & 0.9272 & 0.9102 & 0.9449 & 0.9236 \\  & SVM & 0.9920 & 0.9710 & 0.6993 & 0.7154 & 0.6839 & 0.6843 \\ \hline \multirow{4}{*}{Sett Rot} & Neural Network & 1.0000 & 0.9996 & 0.9983 & 0.9985 & 0.9981 & 0.9981 \\  & KNN & 1.0000 & 0.9998 & 0.9992 & 0.9996 & 0.9988 & 0.9991 \\  & Random Forest & 1.0000 & 0.9966 & 0.9846 & 0.9719 & 0.9977 & 0.9828 \\  & Stack & 1.0000 & 1.0000 & 0.9998 & 1.0000 & 0.9996 & 0.9998 \\  & SVM & 1.0000 & 0.9999 & 0.9994 & 0.9992 & 0.9996 & 0.9994 \\ \hline \multirow{4}{*}{Smut} & Neural Network & 0.9864 & 0.9566 & 0.5821 & 0.5907 & 0.5738 & 0.5593 \\  & KNN & 0.9811 & 0.9658 & 0.6234 & 0.7423 & 0.5373 & 0.6146 \\  & Random Forest & 0.9746 & 0.9544 & 0.5491 & 0.5740 & 0.5262 & 0.5257 \\  & Stack & 0.9965 & 0.9874 & 0.8767 & 0.9025 & 0.8524 & 0.8705 \\  & SVM & 0.9841 & 0.9632 & 0.6513 & 0.6503 & 0.6524 & 0.6319 \\ \hline \multirow{4}{*}{Viral Disease} & Neural Network & 0.9969 & 0.9740 & 0.8836 & 0.8799 & 0.8872 & 0.8689 \\  & KNN & 0.9927 & 0.9783 & 0.9031 & 0.8984 & 0.9079 & 0.8910 \\ \cline{1-1}  & Random Forest & 0.9838 & 0.9712 & 0.8706 & 0.8698 & 0.8714 & 0.8544 \\ \cline{1-1}  & Stack & 0.9962 & 0.9819 & 0.9195 & 0.9114 & 0.9278 & 0.9094 \\ \cline{1-1}  & SVM & 0.9894 & 0.9785 & 0.9037 & 0.8991 & 0.9083 & 0.8916 \\ \hline \multirow{4}{*}{Yellow Leaf} & Neural Network & 0.9968 & 0.9898 & 0.9744 & 0.9801 & 0.9688 & 0.9681 \\ \cline{1-1}  & KNN & 0.9960 & 0.9910 & 0.9774 & 0.9876 & 0.9674 & 0.9719 \\ \cline{1-1}  & Random Forest & 0.9942 & 0.9789 & 0.9470 & 0.9507 & 0.9433 & 0.9338 \\ \cline{1-1}  & Stack & 0.9974 & 0.9926 & 0.9813 & 0.9832 & 0.9795 & 0.9767 \\ \cline{1-1}  & SVM & 0.9960 & 0.9882 & 0.9706 & 0.9705 & 0.9707 & 0.9633 \\ \hline \end{tabular}
\end{table}