# 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 \hline
**Class** & **Model** & **AUC** & **CA** & **F1** & **Precision** & **Recall** & **MCC** \\ \hline \multirow{8}{*}{Average Over Classes} & SVM & 0.9462 & 0.7302 & 0.7333 & 0.7547 & 0.7302 & 0.6831 \\  & Neural Network & 0.9966 & 0.9123 & 0.9123 & 0.9124 & 0.9123 & 0.8950 \\  & KNN & 0.9944 & 0.9206 & 0.9194 & 0.9213 & 0.9206 & 0.9051 \\  & Random Forest & 0.9920 & 0.8951 & 0.8935 & 0.8924 & 0.8951 & 0.8741 \\  & Stack & 0.9982 & 0.9519 & 0.9517 & 0.9516 & 0.9519 & 0.9423 \\ \hline \multirow{8}{*}{Banded Chlorosis} & SVM & 0.9347 & 0.9406 & 0.5950 & 0.6415 & 0.5548 & 0.5648 \\  & Neural Network & 0.9931 & 0.9688 & 0.7988 & 0.8113 & 0.7867 & 0.7820 \\  & KNN & 0.9912 & 0.9710 & 0.8266 & 0.7795 & 0.8798 & 0.8126 \\  & Random Forest & 0.9831 & 0.9640 & 0.7616 & 0.7943 & 0.7314 & 0.7429 \\  & Stack & 0.9949 & 0.9797 & 0.8693 & 0.8831 & 0.8559 & 0.8584 \\ \hline \multirow{8}{*}{Brown Spot} & SVM & 0.9439 & 0.8634 & 0.7343 & 0.8374 & 0.6538 & 0.6531 \\  & Neural Network & 0.9987 & 0.9897 & 0.9823 & 0.9808 & 0.9838 & 0.9751 \\  & KNN & 0.9984 & 0.9882 & 0.9796 & 0.9727 & 0.9867 & 0.9713 \\  & Random Forest & 0.9965 & 0.9810 & 0.9676 & 0.9524 & 0.9832 & 0.9543 \\  & Stack & 0.9995 & 0.9921 & 0.9864 & 0.9812 & 0.9917 & 0.9809 \\ \hline \multirow{8}{*}{Brown Rust} & SVM & 0.9709 & 0.9743 & 0.7495 & 0.7699 & 0.7302 & 0.7362 \\  & Neural Network & 0.9943 & 0.9766 & 0.7797 & 0.7730 & 0.7865 & 0.7674 \\  & KNN & 0.9893 & 0.9790 & 0.7915 & 0.8320 & 0.7548 & 0.7815 \\  & Random Forest & 0.9860 & 0.9732 & 0.7412 & 0.7543 & 0.7286 & 0.7272 \\  & Stack & 0.9966 & 0.9861 & 0.8665 & 0.8785 & 0.8548 & 0.8592 \\ \hline \multirow{8}{*}{Grassy Shoot} & SVM & 0.9999 & 0.9963 & 0.9687 & 0.9418 & 0.9971 & 0.9671 \\  & Neural Network & 0.9999 & 0.9991 & 0.9920 & 0.9993 & 0.9848 & 0.9915 \\  & KNN & 0.9996 & 0.9997 & 0.9971 & 0.9985 & 0.9957 & 0.9969 \\  & Random Forest & 0.9999 & 0.9992 & 0.9935 & 0.9949 & 0.9920 & 0.9931 \\  & Stack & 0.9999 & 0.9997 & 0.9975 & 1.0000 & 0.9949 & 0.9973 \\ \hline \multirow{8}{*}{Pokkah Boeng} & SVM & 0.9934 & 0.9757 & 0.7378 & 0.7907 & 0.6915 & 0.7269 \\  & Neural Network & 0.9917 & 0.9674 & 0.6747 & 0.6642 & 0.6856 & 0.6576 \\  & KNN & 0.9896 & 0.9735 & 0.7566 & 0.6925 & 0.8339 & 0.7463 \\  & Random Forest & 0.9804 & 0.9648 & 0.6395 & 0.6470 & 0.6322 & 0.6211 \\  & Stack & 0.9986 & 0.9903 & 0.9004 & 0.9085 & 0.8924 & 0.8953 \\ \hline \multirow{8}{*}{Sett Rot} & SVM & 1.0000 & 0.9944 & 0.9749 & 0.9510 & 1.0000 & 0.9721 \\  & Neural Network & 1.0000 & 0.9996 & 0.9983 & 0.9969 & 0.9996 & 0.9981 \\  & KNN & 1.0000 & 0.9992 & 0.9966 & 0.9931 & 1.0000 & 0.9961 \\  & Random Forest & 1.0000 & 0.9982 & 0.9916 & 0.9863 & 0.9969 & 0.9906 \\  & Stack & 1.0000 & 0.9997 & 0.9987 & 0.9973 & 1.0000 & 0.9985 \\ \hline \multirow{8}{*}{Viral Disease} & SVM & 0.9230 & 0.9364 & 0.6848 & 0.7636 & 0.6207 & 0.6541 \\  & Neural Network & 0.9959 & 0.9757 & 0.8921 & 0.8829 & 0.9015 & 0.8785 \\ \cline{1-1}  & KNN & 0.9925 & 0.9776 & 0.8993 & 0.9008 & 0.8977 & 0.8867 \\ \cline{1-1}  & Random Forest & 0.9862 & 0.9701 & 0.8671 & 0.8585 & 0.8759 & 0.8504 \\ \cline{1-1}  & Stack & 0.9966 & 0.9814 & 0.9180 & 0.9003 & 0.9365 & 0.9077 \\ \hline \multirow{8}{*}{Yellow Leaf} & SVM & 0.8928 & 0.8115 & 0.6165 & 0.5197 & 0.7577 & 0.5128 \\ \cline{1-1}  & Neural Network & 0.9978 & 0.9883 & 0.9707 & 0.9749 & 0.9665 & 0.9634 \\ \cline{1-1}  & KNN & 0.9948 & 0.9877 & 0.9690 & 0.9817 & 0.9565 & 0.9614 \\ \cline{1-1}  & Random Forest & 0.9960 & 0.9839 & 0.9598 & 0.9602 & 0.9594 & 0.9498 \\ \cline{1-1}  & Stack & 0.9989 & 0.9905 & 0.9762 & 0.9796 & 0.9728 & 0.9703 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for models on SqueezeNet embedded features across all classes. The class-specific results highlight that, for challenging classes such as smut and pokkah boeng, the InceptionV3 embedded features yield better F1-scores (0.8767 for smut and 0.9272 for pokkah boeng) compared to SqueezeNet (0.8693 for smut and 0.9004 for pokkah boeng).