Disease Assessment in Cotton Leaves Using an Optimized CNN-Based Detection System
Bibliographic record
Abstract
Improving cotton yield greatly depends on the early and accurate detection of diseases affecting cotton leaves. In recent years, deep learning has gained momentum in agricultural research, offering promising solutions across various applications. One major area of interest is the real-time diagnosis of cotton leaf diseases. Despite the significant advances made using Convolutional Neural Networks (CNNs) for plant disease classification, there remains a considerable gap in delivering accessible and effective tools for farmers and agricultural experts.Manual disease detection in crops is not only time-consuming and labor-intensive but also prone to human error, which can lead to misdiagnosis, ineffective treatment strategies, and financial losses. To address these challenges, this work proposes leveraging a Faster R-CNN model trained on a cotton leaf image dataset for precise disease identification and classification. The Plant Village dataset is used as a standard reference to evaluate different feature extraction networks such as VGG-16, InceptionV1, and InceptionV2 for performance comparison.Given the critical role agriculture plays in India’s economy, modernizing disease detection methods is essential. As climate change and environmental variability continue to impact crop health, it is crucial to replace traditional, manual inspection methods with automated, intelligent solutions that can support sustainable farming and food security.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".