The Impact of Convolutional Layer Selection in ResNet-50v2 Architecture on Corn Leaf Disease Classification
Bibliographic record
Abstract
Agriculture is a significant economic sector in many nations, including Indonesia, with corn being one of the primary food crops.Leaf infections in corn plants can result in substantial losses for farmers and disruptions in food production.This disease diagnosis application can enable early disease detection for smallholder farmers.This is because current procedures, which rely on manual knowledge from agronomists, are time-consuming and costly.Advances in Agricultural AI (Artificial Intelligence) and image processing have enabled autonomous identification of plant diseases using the convolutional neural networks (CNN) technique, with ResNet-50v2 being one of the established architectures.The main purpose of this research is to investigate the selection and design of appropriate convolutional layers in the ResNet-50v2 model.In order to identify corn leaf diseases, the following layers were chosen: activation, batch normalization, and pooling.There are 4,000 entries in the dataset, distributed among four categories: gray leaf spot, common rust, northern leaf blight, and healthy.The data will be separated into three categories: learning, validation, and testing.According to the study's findings, the chosen convolutional layer using ResNet-50v2 obtained a 95.7% accuracy, precision, recall, and F1-score.The activation layer obtained 92.4% precision and 92.1% accuracy, recall, and F1-score.The batch normalization layer obtained 98.1% precision 98% accuracy, recall, and F1-score.The pooling layer obtained 94.7% precision and 94.5% accuracy, recall, and F1-score.The achievement of this study reveals that the performance of the Batch Normalization convolutional layer outperforms other layers.The findings of this study show that batch normalization layers in the ResNet-50v2 model outperform other layers in Corn leaf disease classification.This highlights the effectiveness of batch normalization in mitigating overfitting, a frequent challenge in deep learning systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".