AI-based Plant Disease Detection: Development and Deployment of YOLO Models for Agricultural Innovation
Bibliographic record
Abstract
This research focuses on enhancing plant disease detection by developing four models based on YOLOv11, the latest iteration in the “You Only Look Once” series, renowned for real-time object detection capabilities. A comparative analysis is conducted with the open-source YOLOX model and the YOLOvME model to evaluate performance metrics such as accuracy, precision, recall, and mean Average Precision. Notably, the Apple disease detection model outperforms the others, achieving an impressive accuracy of $\mathbf{9 3. 8 \%}$. The models are trained using a comprehensive dataset comprising images of various plant diseases, enabling the identification and classification of multiple disease types. The deployment of these models on ALIVEculture.ca (a dedicated application for plant disease detection) allows users to perform real-time analyses through multiple imaging devices, including smartphone cameras and remote monitoring systems in greenhouses. Additionally, we develop a mobile application that leverages these AI models, providing on-the-go disease detection and immediate feedback.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".