# 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
model shows significant inconsistencies; we notice the superiority of the healthy and settt rot classes in terms of precision, recall, and the F1-score, while others, such as grassy shoot, show lower and more imbalanced metrics, indicating challenges in capturing the features of these classes. Figure 8: Performance metrics for deep learning models (RNN, LSTM, and GRU) across all classes trained from scratch. The LSTM model demonstrates high scores for classes such as sett rot, while some classes, such as pokkah boeng, still exhibit poor results. The GRU model exhibits the best overall balance, achieving consistently high precision, recall, and F1-scores for most classes, particularly sett rot, viral diseases, and healthy leaves, suggesting its superiority in handling this dataset. However, classes such as brown rust remain challenging for all models, indicating that these categories may require additional feature engineering or data augmentation for improved classification performance. Overall, the GRU model demonstrates the most robust performance, while the RNN shows significant room for improvement. More details of the CNN's performance on all imbalanced classes are illustrated in Figure 9. The performance metrics--the precision, recall, and F1-score--achieved by the CNN model are shown. The results indicate that the model performs consistently well across most classes, achieving near-perfect scores for classes such as healthy, brown spot, grassy shoot, settt rot, and viral diseases. However, some classes, such as banded chlorosis, exhibits slightly lower recall and F1-scores, suggesting challenges in accurately detecting these classes. The error bars in Figures 5, 6, and 9 represent the standard deviations of the performance metrics over two independent runs, each conducted with shuffled data to assess the robustness of the models. In this case, the CNN model demonstrates robust performance in classifying sugarcane diseases, although specific underperforming classes may benefit from further data augmentation or feature refinement. Embedded Feature Methodologies Affecting Imbalanced Disease Classes Followed by Conventional ML Methods  
Table 4 compares different machine learning models applied to the InceptionV3 embedded features across all classes. The stacking model consistently outperforms other models, achieving the highest average F1-score (0.9562), CA, and MCC across all classes. For brown spot and settt rot, the stacking model achieves near-perfect performance, with an F1-score of 0.9882 and 0.9998, respectively. However, for more challenging categories like smut and pokkah boeng, the stacking model still outperforms others, but the F1-scores (0.8767 and 0.9272) reflect the inherent complexity of these classes. Overall, the stacking model demonstrates robustness across all metrics and categories, while simpler models like random forest and KNN exhibit lower performance, particularly in handling more imbalanced or complex classes. These results highlight the superior capacity of ensemble methods like stacking in leveraging feature representations for complex classification tasks. Figure 9: Performance metrics for CNN across all classes trained from scratch.