Location-Supported Lesions Representation with Potato Leaf Blight Disease Detection Using Dual-Head Convolutional Neural Network
Bibliographic record
Abstract
A deep learning framework designed for classifying potato leaf blight diseases using the Plant Village Dataset, we developed the Location-Supported Lesions Representation with Potato Leaf Blight Disease Detection Using Dual-Head Convolutional Neural Network (LLPLDC) model which consists of 2,152 processed images. To improve model generalization and tackle data imbalance, the dataset undergoes noise removal and augmentation. A Diffusion-based IGN-Net generates synthetic images of diseased leaves from healthy ones, the DH-CNN features dual outputs: one for identifying healthy versus diseased leaves and another for differentiating between early and late blight. Training utilizes stochastic gradient descent with learning rate adjustments and ReLU activations, enhancing classification accuracy. The confusion matrix, training and validation loss and accuracy of DHCNN and ROC curve are used to calculate the performance of LLPLDC.
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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) | 0.000 | 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) | 0.001 | 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".