Convolutional Neural Network-Based Classification Model of Corn Leaf Disease
Bibliographic record
Abstract
The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".