ExE-Net: Explainable Ensemble Network for Potato Leaf Disease Classification
Bibliographic record
Abstract
Potato cultivation faces significant harvest loss from several diseases, emphasizing the importance of early and accurate disease detection. Analyzing potato leaf images using computer vision tools and machine learning algorithms offers a promising way for early disease identification and continuous plant health monitoring. However, previous approaches in potato leaf disease classification have overlooked key aspects such as feature learning potentials of recent deep learning models, benefits of ensemble learning, and explainability techniques, leading to limitations in accuracy, interpretability, and practicality. To this end, We introduce the Explainable Ensemble Network (ExE-Net) for potato leaf disease classification, which outperforms existing methods in accuracy and generalization. Our key contributions include an ensemble architecture combining Xception, DenseNet201, and InceptionResNet to capture diverse features, and integrating explainability techniques like Grad-CAM, LIME, and SHAP to enhance transparency. Data augmentation further boosts ExE-Net’s performance, resulting in superior accuracy and robustness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".