Detection of Wheat Diseases Using Modified Canny Edge Detection
Bibliographic record
Abstract
Wheat crops are the most important cereals for millions of people because they do not only ensure food security but also serve as source of livelihood. However, wheat crop diseases pose a significant risk to livelihood and food security, necessitating the use of automated and effective detection methods. Due to noise and lighting conditions, traditional edge detection techniques such as Sobel, Prewitt, Roberts, Laplacian, and Canny are predominantly unable to accurately detect faulty region. We present a Modified Canny Edge Detection method in this work that combines adaptive thresholding and Gaussian smoothing to improve disease feature extraction. Several image quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Entropy, and Edge Pixel Count, are used to compare Modified Canny with traditional edge detection techniques to perform a thorough performance evaluation. Furthermore, graphical representations and histogram comparisons are used to examine edge-preserving skills. The results of the experiments show that the Modified Canny method performs better than conventional methods, attaining higher PSNR and SSIM while keeping MSE and RMSE values lower, guaranteeing better edge recognition in images of wheat disease. This study demonstrates how enhanced edge detection techniques work for diagnosing agricultural diseases and lays the groundwork for incorporating edge-based techniques into deep learning frameworks for better crop health monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".