Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection
Why this work is in the frame
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Bibliographic record
Abstract
Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.
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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 | 0.000 |
| 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