Deep Learning Models for Wheat Diseases Detection and Recognition
Bibliographic record
Abstract
Wheat is a grass widely cultivated for its seed, a cereal that is a staple food around the world.However, cereal wheat is subject to many wheat diseases, including bacterial, viral and fungal diseases, as well as parasitic infestations.The need to use Deep Learning methods to identify automatically wheat diseases has become a challenge.In this paper, we proposed and compared two models based on Convolutional Neural Network (CNN) for wheat diseases detection and recognition.The convolutional layers of a CNN can be considered as matching filters derived directly from data images (images of healthy and unhealthy wheat).CNNs thus produce a hierarchy of visual representations optimized for our task.As a result of CNN training, a model is obtained -a set of weights and biases -which then responds to the specific task for which it was designed.One of the main strengths of CNNs is their ability to generalize, that is, the ability to process data never seen before.This allows a certain robustness to the heterogeneity of the background, to the image acquisition conditions and to the intra-class variability.A large image dataset of various wheat diseases, including healthy wheat, was used for training our models to learn, recognize and detect diseases and/or abnormalities in wheat.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".