Gaussian Filtering-Based Local Ternary Pattern for Efficient Classification of Crop Diseases
Bibliographic record
Abstract
Accurate and reliable disease recognition in plants can assist in taking immediate remedial action, and thus improve the overall productivity. In this work, we develop an intelligent machine-learning system to accurately identify the diseases using leaf images of tomato plant. The images are represented in the hue, saturation, value (HSV) format, and the V component is subjected to sub-band decomposition using Gaussian filters. Local ternary patterns (LTPs) are computed directly on the H and S components, and also on the decomposed images obtained from the V component. The local texture information is augmented by global information captured using histograms computed directly from the H, S, and V components, to build a comprehensive feature representation. The significant features are selected using the minimum redundancy maximum relevance (mRMR) algorithm and machine-learning techniques are applied for classification. The proposed feature identifies the various crop diseases more accurately than the existing methods.
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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 | 0.000 | 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 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".