Detection of Sugarcane Mosaic Diseases Using Deep Learning Architecture to Avoid Annealing Temperature of PCR Primer in Laboratory Testing
Bibliographic record
Abstract
The sugarcane leaf diseases such as mosaic and streak mosaic are difficult to differentiate using Image processing techniques because both diseases show similar visual attributes such as pattern and color. To identify the type of diseases, we need to perform Polymerase Chain Reaction (PCR) testing which is used for the classification of diseases in laboratories. The accuracy of the PCR test depends on reaction mix preparation, reaction time, and DNA/RNA extraction. The major problem influencing the PCR test accuracy is the Annealing temperature of the primers and needs a standardized set of samples. In addition, it is a time-consuming process. In this paper, we proposed a Diversified Deep Learning Architecture (DDLA) which is developed with the input images after various pre-processing steps such as denoising using Discrete Wavelet Transform (DWT) and enhancing using histogram equalization in HSI color space to improve the similar pattern disease prediction accuracy. The performance of the proposed model is analyzed for a set of diseased leaves and the results are compared with the output of the popular pertained models such asVGG16, InceptionV3, ResNET50, Inception ResNET, and DenseNET201 with and without pre-processing. The training accuracy of the proposed model is 97% and the testing accuracy is 87%. The DDLA model produces ground truth test results with an accuracy of 88.7% for mosaic and 85.7% for streak mosaic with a less computational time of 152sec compared to the lab test duration of 6 hrs. The performance of the model is also measured in terms of Precision, F1 Score, Specificity, and Sensitivity. The Proposed DDLA model’s F1 score is higher than the pre-trained models with a minimum test loss of 1.167. Moreover, the DDLA structure occupies less memory space when compared to the pertained models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".