Residual attention based multi-label learning for apple leaf disease identification
Bibliographic record
Abstract
Recent studies suggest that plant disease identification via machine learning approach is vital for preventing the spread of diseases. Identifying multiple diseases simultaneous on a single leaf is one of the most irritating issues in agricultural production. However, the existing approaches are difficult to meet the requirements of production practice in accuracy or interpretability. Here, we present residual attention based multi-label learning framework (RAMDI), a method for predicting apple leaf diseases in natural environment. Built upon an attention based multi-label learning framework, the channel and spatial attention mechanisms are investigated and embedded in residual network for multi-label disease prediction, which takes advantage of channel-wise and spatial-wise attention weights. Experimental results indicate that the RAMDI achieves 0.976 accuracy, 0.986 F-score, and 0.979 mAPs, outperforms the existing state-of-the-art apple leaf disease identification models. RAMDI not only predicts multi-disease on a single leaf simultaneously, but also reveals the interpretability among positive predictions that contribute most to identify the key features that are significant for the leaf diseases. This method achieves the following two achievements. Firstly, it provides a solution for detecting multiple diseases on a single leaf. Secondly, this approach gains an interpretable understanding for apple leaf disease identification.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".