AI-Driven Detection of Potato Leaf Diseases and Yield Optimization
Bibliographic record
Abstract
In recent times, the usage of machine learning models in agribusiness has significantly expanded intelligent farming techniques. This paper outlines our research towards improving the detection of diseases in potato plants in indoor greenhouse environments using advanced object detection models. Using this set of data, we evaluate and compare three object identification models: Faster Region-based Convolutional Neural Network (Faster R-CNN), You Only Look Once version 11 (YOLOv11), and YOLO Neural Architecture Search (YOLO-NAS) for accurately identifying and categorizing potato leaf diseases. Our approach enables us to train a model on a more realistic set of images, making disease diagnosis more automated and timelier for farmers. With a Average Precision at $\mathbf{5 0 \%}$ Intersection over Union (mAP50) score of $99.5 \%$, our results indicate that the YOLOv11 is the most effective, followed by our Faster R-CNN achieving an average mAP50 of $\mathbf{9 5. 9 3 \%}$, in detecting diseased leaves.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".