Deep Learning and Grey Wolf Optimization Technique for Plant Disease Detection: A Novel Methodology for Improved Agricultural Health
Bibliographic record
Abstract
Plant disease outbreaks have a profound impact on the agricultural sector, leading to substantial economic implications, compromised crop yields and quality, and potential food scarcity.Consequently, the development of effective disease prevention and management strategies is crucial.This study introduces a novel methodology employing deep learning for the identification and diagnosis of plant diseases, with a focus on mitigating the associated detrimental effects.In this investigation, Convolutional Neural Networks (CNNs) were utilized to devise a disease identification method applicable to three types of plant leaves -peppers (two classes), potato (three classes), and tomato (nine classes).Preprocessing techniques, including image resizing and data augmentation, were adopted to facilitate the analysis.Additionally, three distinct feature extraction methods -Haralick feature, Histogram of Gradient (HOG), and Local Binary Patterns (LBP) -were implemented.The Grey Wolf Optimization (GWO) technique was employed as a feature selection strategy to identify the most advantageous features.This approach diverges from traditional methodologies that solely rely on CNNs for feature extraction, instead extracting features from the dataset through multiple extractors and passing them to the GWO for selection, followed by CNN classification.The proposed method demonstrated high efficiency, with classification accuracies reaching up to 99.8% for pepper, 99.9% for potato, and 95.7% for tomato.This study thus provides a progressive shift in plant disease detection, offering promising potential for improving agricultural health management.In conclusion, the integration of deep learning and the Grey Wolf Optimization technique presents a compelling approach for plant disease detection, demonstrating high accuracy and efficiency.This research contributes a significant advancement in the field of agricultural health and disease management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".