Enhanced Tomato Leaf Disease Classification Using the EfficientNetB3 and ResNet50 Fusion
Bibliographic record
Abstract
Crop yield and quality are significantly impacted by tomato leaf diseases, which necessitate prompt and precise diagnosis for effective disease management. In this paper, we describe a hybrid deep learning architecture for classifying diseases in tomato leaves using transfer learning. The method is based on the EfficientNetB3 and ResNet50 architectures, which are both pretrained on ImageNet to benefit from their complementary feature extraction capabilities. To enhance disease-related features, the input images are preprocessed using sharpening filters and Contrast Limited Adaptive Histogram Equalization (CLAHE). The final validation experiment, using a tomato leaf disease dataset with 10 distinct classes, yielded accuracies of 99.30 for EfficientNetB3, 99.50 for ResNet50, and 100% for hybrid models. The classification outputs were examined via confusion matrices and extensive classification reports, showing the high consistency of the model in identifying various disease classes. The suggested hybrid model outperforms individual models in classification, indicating potential for real-time plant monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".