ATRLeNet: A Deep Learning Model for Enhanced Classification of Oryza Sativa Pathologies
Bibliographic record
Abstract
Diseases affecting the Oryza Sativa (rice) plant result in substantial agricultural losses, leading to a decline in crop productivity by up to 25% and posing a significant threat to global food security.Hence, the rapid and accurate diagnosis of such diseases is paramount to ensure effective treatment and to enhance overall plant health.This has led to an increased interest among plant pathologists in developing reliable methods for identifying diseases in Oryza Sativa crops.In this study, an innovative disease classification model for the Oryza Sativa plant is proposed, leveraging the Optimal Adaptive Boosting Cascade Classifier (OABCC) and the efficient-artificial fish swarm optimization (EAFSO).A weighted image fusion technique is utilized in the pre-processing stage for image denoising, combining the outcomes of homomorphic filtering (HAF), Laplace filtering (LAF), and the Kuwahara Filter (KF).The diseased portions of the Oryza Sativa plant leaf are localized using the OABCC, while Soft Non-Maximum Suppression (SN-MS) is deployed to select the optimal detection box for each item.The LeNet model, bolstered with an atrous-convolution layer, is integrated into the OABCC for improved disease classification.Further enhancement in model accuracy is achieved through the application of the EAFSO optimization strategy.When applied to the OABCC-ATRLeNet model for rice disease classification, the EAFSO optimization strategy outperforms other strategies such as WSSO, CSO, AFSO, and PSO.This research underscores the potential of deep learning approaches for robust and accurate classification of plant diseases, contributing significantly to the efforts in securing global food resources.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".