A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field
Why this work is in the frame
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Bibliographic record
Abstract
Plant disease is defined as an abnormal physiological process that distorts the plant's normal structure, growth and function. Disease reduces quality as well as quantity of the crops which in turn affects the economy of country like Bangladesh where agriculture is the main occupation. Since Rice is the major crop, classification of disease in paddy is very important as it prevents the losses in the yields and quantity. Classification of rice disease includes visually observable patterns and color of the affected portion. Manual observation of patterns and colors to classify the diseases require excessive work and appears to be less useful while dealing with non-native diseases. This paper presents a new technique to detect and classify the diseases based on percentage of RGB value of the affected portion using image processing. Once the percentage of RGB from the affected region is extracted and grouped into various classes, they are fed to a simple classifier called Naive Bayes which classifies the disease into various categories. This technique has successfully detected and identified three rice diseases namely rice brown spot, rice bacterial blight, and rice blast. This technique is efficient and faster because it uses only one feature i.e. RGB values of the affected portion which requires minimum computation time to identify and classify the diseases. Rather than processing the whole leaf, this technique even successfully detects the diseases using only a small sample of leaf containing the affected portion for rice disease.
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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