Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants
Bibliographic record
Abstract
Potato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strains of PVY cause mild symptoms in the foliage, making it challenging to detect infected plants. Consequently, identifying and disposing of infected plants (known as “roguing”) has become more difficult. There is a growing demand to create solutions that aid growers in identifying potato plants that have been infected with PVY. In past studies, deep learning-based convolutional neural networks (CNNs) have shown the ability to successfully make distinctions between various healthy plants, disease plants, and weeds. In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. Different deep learning models, specifically EfficientNet, VGGNet-19, DenseNet-201 and ResNet-101 are trained on the imagery dataset of healthy and PVY-infected potato plants grown under greenhouse conditions. The evaluation metrics used were accuracy, precision, recall, and F1 Score. The trained models achieved classification accuracy scores of 85% while classifying the healthy and PVY-infected potato plants. The models were also able to accurately detect PVY-infected plants even when the symptoms were mild, which is essential for early detection and prevention of the spread of the virus. These models may assist roguers in the real-time identification of PVY-infected plants that may help in controlling the disease spread and improving the crop yield.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".