Optimized Faster R-CNN with Weighted ABC for High-Accuracy Gova Leaf Disease Identification
Bibliographic record
Abstract
The Govas are very vulnerable to several forms of leaf diseases such as fungal, bacterial, and viral diseases, which may have severe effects on crop production and quality. The timely and proper identification of these diseases is of great importance to ensure the survival of agriculture and reducing the high consumption of chemical treatments. The proposed study is aimed at optimizing the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a Weighted Artificial Bee Colony (WABC) algorithm to enhance the accuracy and stability of detecting Gova leaf disease. The Faster R-CNN architecture is particularly useful at identifying diseased cells by extracting effective features, whereas the WABC algorithm is also used to improve the network hyperparameters and anchor box settings to increase the convergence rate and improve classification. It is experimentally evaluated that the proposed WABC-optimized Faster R-CNN is more accurate, precise, recalls, and has a higher F1-score than conventional CNN and standard Faster R-CNN models with publicly available Gova leaf datasets. Also, the technique is known to be good in separating several classes of diseases in different levels of illumination and environment. The study offers a smart and automated system of managing the Gova leaf disease, and this reduces the reliance on manual inspection and promotes sustainable farming methods. The suggested approach does not only improve the quality of disease detection, but it is also useful in precise farming to protect crops and increase their quality.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".