CTPlantNet: A Hybrid CNN-Transformer Architecture for Plant Disease Classification
Bibliographic record
Abstract
An accurate detection of plant diseases is important for increasing crop yields. Incorrect detection of plant diseases can lead to inappropriate use of herbicides. Visual inspection of plant diseases is a challenging task for agronomists and plant pathologists as it requires strong observation skills, time and resources. The use of deep learning models for plant disease detection and classification has shown high performances. In this paper, we propose an efficient deep learning (Convolutional Neural Networks and Transformer) hybrid architecture called “CTPlantNet” for multi-classification of apple leaf diseases using a dataset of 3,526 images (Plant Pathology 2020 FGVC-7). Our model showed impressive results, outperforming state-of-the-art models by achieving an accuracy (ACC) of 98.28% and an area under curve (AUC) of 99.82%.
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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) | 0.001 | 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".