An object detection solution for early detection of taro leaf blight disease in the West African sub-region
Bibliographic record
Abstract
Taro Leaf Blight (TLB) poses a significant threat to food security and economic stability in West Africa, where taro is a staple crop. This research presents an object detection system utilizing the YOLOv8 deep learning model to detect TLB early in taro plants. The methodology involved developing a unique dataset comprising images of taro leaves at various stages of infection, collected from farms in Nigeria and Ghana. Fine-tuning the YOLOv8 model with this dataset resulted in a notable improvement, achieving an 85.7% mean Average Precision (mAP) across all classes—a significant enhancement over existing generic plant disease detection models, which typically achieve mAP values of around 70-75% on similar datasets. This 15-20% improvement enables more accurate early detection, crucial for timely interventions. The system was subsequently integrated into an Android application, allowing farmers real-time diagnosis and disease management access. Field tests demonstrated the application's effectiveness and user-friendly design, making it a practical tool for early disease intervention. This research highlights the potential of combining deep learning and mobile technology to address agricultural challenges and improve food security in the region.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".