Optimizing Disease Detection Models in School Greenhouses: An AI and IoT-Based Approach for Smart Agriculture
Bibliographic record
Abstract
In recent years, the integration of Internet of Things technologies and machine learning models in agriculture has significantly advanced smart farming practices. This paper presents our research on enhancing disease detection in tomato plants within a greenhouse environment using advanced object detection models. Collaborating with the University of Montreal greenhouse, we developed a realistic dataset comprising images from both the PlantVillage repository and over 1,900 manually labeled leaf images taken from the greenhouse. Using this dataset, we evaluated and compared three object detection models: Faster R-CNN, YOLOv10, and SSD, to accurately detect and classify tomato leaf diseases. Our approach enables us to train a model on a more realistic set of images, facilitating automatic and earlier disease detection for farmers. Our results show that the Faster R-CNN model with a ResNet-50 backbone and Feature Pyramid Network is the most effective for detecting diseased leaves, achieving a mAP50 score of 93.13%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".