Deep Learning-Based Rice Leaf Disease Detection Using Mobilenetv2 For Agricultural Health Monitoring
Bibliographic record
Abstract
Rice is a vital crop to millions, chiefly in low-income economies where it is an significant main food for safeguarding food security. Conventional manual diagnosis events are labour-intensive, need authorities, and are not scalable, particularly in the countryside. The proposed task is to fold a rice leaf disease dataset and shape a classification model based on MobileNetV2 to classify efficiently if a disease exists or not. The methodology starts with the gaining of Rice lease diseases Dataset and preprocessing via data augmentation, normalization, and resizing for image training for model training. Feature extraction is existence performed by the Vision Transformer (ViT) model in order to extract local and global image features using self-attention mechanisms. These extracted features are then classified with the MobileNetV2 in this hybrid structure, which upholds a stability between accuracy and cost of computation. The model has experienced further generalization, with an accuracy of 96.8%, precision of 97.1%, recall of 96.5%, F1-score of 96.8%, and ROC AUC of 97.3%, thus exceptional the baseline models. This hybrid model greatly improves the accuracy and speed of rice leaf disease classification, thus if a good tool for helping in early disease recognition and crop group.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".