An object detection solution for early detection of taro leaf blight disease in the West African sub-region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 it