XSE-TomatoNet: An explainable AI based tomato leaf disease classification method using EfficientNetB0 with squeeze-and-excitation blocks and multi-scale feature fusion
Bibliographic record
Abstract
Tomatoes are globally valued for their nutritional benefits and unique taste, playing a crucial role in agricultural productivity. Accurate diagnosis of tomato leaf diseases is vital to avoid ineffective treatments that can harm plants and ecosystems. While deep learning models excel in classifying these diseases, distinguishing subtle variations remains challenging. This study introduces XSE-TomatoNet, an enhanced version of EfficientNetB0, incorporating Squeeze-and-Excitation (SE) blocks and multi-scale feature fusion to boost classification performance. XSE-TomatoNet extracts multi-scale features, refines them with SE blocks, and merges them through Global Average Pooling, providing detailed and broad insights for precise disease classification. Our approach achieves an impressive accuracy of 99.11%, with 99% precision and recall, outperforming models like MobileNet and VGG19, especially when combined with data augmentation and ablation studies. The model achieved an average training accuracy of 99.41% and a validation accuracy of 98.88% in 10-fold cross-validation, showing strong generalization to unseen data. We also used LIME and SHAP for model interpretability, offering insights into the decision-making process, and employed Grad-CAM and Grad-CAM++ to visually highlight key areas in leaf images. Finally, the best model was integrated into a web-based system for practical use by tomato cultivators.•XSE-TomatoNet is an enhanced version of EfficientNetB0 which incorporates Squeeze-and-Excitation (SE) blocks and multi-scale feature fusion.•XSE-TomatoNet outperformed MobileNet (87.44%) and VGG-19 (95.50%), in terms of accuracy, achieving 99.41%.•Integration of interpretation using LIME and SHAP models gives higher level understanding of the diseases and employment of Grad-CAM and Grad-CAM++ shows visual representation of the diseased leaves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".