Development of a YOLOv11-Based Deep Learning System for Insect Pest Detection and Classification in Oil Palm Plantation
Bibliographic record
Abstract
Pest diseases are serious global agricultural issues that lead to lower crop yields, increased cost of production and excessive pesticide use. Traditional methods of identifying pest infestation (e.g., using field scouting methods) rely on intensive labor, human time, and human errors, thus making them impractical for large-scale and sustainable farming. This project is a structured deep learning-based system for automatically identifying pest diseases and pests through image identification. The system is developed using the YOLOv11 state-of-the-art model for object identification and has been trained on a custom-dataset from the objects of three pest species - bagworms, aphids, and whiteflies. The images representing each pest were pre-processed and augmented in order to equalize data and optimal modeling performance. The experimental evaluation of the trained model archieved a precision of 0.88, recall of 0.80, and mAP@0.5 of 0.85, outperforming conventional detection methods and demonstrating strong reliability even with imbalanced classes., thus demonstrating the proposed system is viable for use in real-world agricultural environment. The proposed system can provide an intervention to pest infestation enabling early and timely diagnoses of pest infestation, which in turn may help reduce over-use of pesticides, and contribute to more targeted use of pesticides and sustainable farming practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".