# 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
However, for classes with well-defined features like sett rot, both models perform exceptionally well, with SqueezeNet occasionally achieving higher F1-scores, such as 0.9987 for sett rot. Table 6 shows the DeepLoc embeddings, where the stacking model again achieves the best results, similar to its performance with the InceptionV3 and SqueezeNet embeddings. However, the DeepLoc embeddings demonstrate slightly lower overall F1-scores (average F1-score of 0.9252) compared to InceptionV3 (0.9562) and SqueezeNet (0.9517). While DeepLoc excels in challenging classes such as pokkah boeng and smut (F1-scores of 0.8623 and 0.8275), its performance still lags behind that obtained using InceptionV3 for the same classes (F1-scores of 0.9272 and 0.8767). In simpler or well-defined classes like sett rot and grassy shoot, the DeepLoc embeddings remain competitive, with F1-scores of 0.9921 and 0.9636, respectively, although these scores are comparable to or slightly below those of SqueezeNet. Moreover, models like SVM perform particularly poorly with the DeepLoc embeddings (e.g., F1-scores as low as 0.3793 for smut), highlighting limitations in non-ensemble models when using this feature representation. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Class** & **Model** & **AUC** & **CA** & **F1-Score** & **Precision** & **Recall** & **MCC** \\ \hline \multirow{8}{*}{Average Over Classes} & Neural Network & 0.9914 & 0.8927 & 0.8925 & 0.8924 & 0.8927 & 0.8714 \\  & KNN & 0.9858 & 0.8800 & 0.8796 & 0.8817 & 0.8800 & 0.8565 \\  & Random Forest & 0.9878 & 0.8584 & 0.8555 & 0.8540 & 0.8584 & 0.8300 \\  & Stack & 0.9946 & 0.9253 & 0.9252 & 0.9259 & 0.9253 & 0.9105 \\  & SVM & 0.8594 & 0.4952 & 0.4867 & 0.5191 & 0.4952 & 0.4212 \\ \hline \multirow{8}{*}{Banded Chlorosis} & Neural Network & 0.9849 & 0.9628 & 0.7586 & 0.7748 & 0.7431 & 0.7387 \\  & KNN & 0.9702 & 0.9603 & 0.7639 & 0.7169 & 0.8176 & 0.7442 \\  & Random Forest & 0.9775 & 0.9560 & 0.7052 & 0.7460 & 0.6686 & 0.6827 \\  & Stack & 0.9859 & 0.9703 & 0.8081 & 0.8214 & 0.7952 & 0.7921 \\  & SVM & 0.8687 & 0.8767 & 0.3512 & 0.2995 & 0.4245 & 0.2906 \\ \hline \multirow{8}{*}{Brown Spot} & Neural Network & 0.9927 & 0.9798 & 0.9652 & 0.9607 & 0.9697 & 0.9509 \\  & KNN & 0.9911 & 0.9687 & 0.9466 & 0.9345 & 0.9590 & 0.9246 \\  & Random Forest & 0.9922 & 0.9601 & 0.9326 & 0.9103 & 0.9559 & 0.9048 \\  & Stack & 0.9968 & 0.9820 & 0.9690 & 0.9643 & 0.9738 & 0.9563 \\  & SVM & 0.7784 & 0.7503 & 0.4428 & 0.6225 & 0.3436 & 0.3207 \\ \hline \multirow{8}{*}{Brown Rust} & Neural Network & 0.9874 & 0.9730 & 0.7479 & 0.7358 & 0.7603 & 0.7337 \\  & KNN & 0.9784 & 0.9716 & 0.7268 & 0.7381 & 0.7159 & 0.7120 \\ \cline{1-1}  & Random Forest & 0.9784 & 0.9688 & 0.6882 & 0.7281 & 0.6524 & 0.6729 \\ \cline{1-1}  & Stack & 0.9915 & 0.9785 & 0.7975 & 0.7919 & 0.8032 & 0.7861 \\ \cline{1-1}  & SVM & 0.8671 & 0.9454 & 0.3576 & 0.4714 & 0.2881 & 0.3419 \\ \hline \multirow{8}{*}{Grassy Shoot} & Neural Network & 0.9993 & 0.9949 & 0.9557 & 0.9585 & 0.9529 & 0.9530 \\ \cline{1-1}  & KNN & 0.9954 & 0.9928 & 0.9382 & 0.9358 & 0.9406 & 0.9344 \\ \cline{1-1}  & Random Forest & 0.9977 & 0.9886 & 0.9033 & 0.8863 & 0.9210 & 0.8975 \\ \cline{1-1}  & Stack & 0.9997 & 0.9958 & 0.9636 & 0.9678 & 0.9594 & 0.9614 \\ \cline{1-1}  & SVM & 0.9890 & 0.9503 & 0.6711 & 0.5427 & 0.8790 & 0.6678 \\ \hline \multirow{8}{*}{Pokkah Boeng} & Neural Network & 0.9873 & 0.9670 & 0.6603 & 0.6719 & 0.6492 & 0.6431 \\ \cline{1-1}  & KNN & 0.9740 & 0.9683 & 0.7053 & 0.6516 & 0.7686 & 0.6913 \\ \cline{1-1}  & Random Forest & 0.9725 & 0.9603 & 0.5804 & 0.6061 & 0.5568 & 0.5601 \\ \cline{1-1}  & Stack & 0.9953 & 0.9872 & 0.8623 & 0.9227 & 0.8093 & 0.8577 \\ \cline{1-1}  & SVM & 0.9230 & 0.9411 & 0.3541 & 0.3860 & 0.3271 & 0.3247 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics for DeepLoc embedding across different classes.