A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".