Hybrid CNN–SVM Model for Intelligent Early Detection of Pest Infestation in Crops
Bibliographic record
Abstract
Pest infestation is one of the primary causes of reduced crop productivity worldwide, resulting in up to 40% yield losses annually. Traditional pest detection methods depend on manual inspection, which is inefficient and error-prone. To address this, we propose a Hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) model for intelligent early detection of pest infestation in crops. The CNN automatically extracts spatial and texture features from leaf images, while the SVM classifier enhances decision boundaries for accurate classification. The framework was evaluated on a dataset of 12,000 images comprising five major pest categories and healthy crop samples. Experimental results demonstrate that the proposed hybrid model achieves an overall accuracy of 96.8%, outperforming standalone CNN (94.2%) and conventional SVM (89.5%). The hybrid model also reports improvements in precision (96.3%), recall (97.1%), and F1-score (96.7%). These findings highlight the potential of the CNN–SVM hybrid approach in supporting smart agriculture by enabling early detection, reducing pesticide misuse, and improving crop yield.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".