Identification of Rice Plant Diseases Using Convolutional Neural Network Method
Bibliographic record
Abstract
Rice plants (Oryzae Sativa sp.) are rice-producing food plants which play an important role in economic life in Asian countries, especially Indonesia.This primary need is very difficult to replace with other staples, such as corn, tubers, sago and other sources of carbohydrates.However, global climate change which has an impact on climate anomalies causes rice diseases to develop rapidly.The aim of this research is to create an application on an Android-based cell phone that can identify and classify rice plant diseases using the Convolutional Neural Network (CNN) method.The research material was 1600 images of rice leaves consisting of 4 classes, namely 400 images of rice affected by Leaf Blight disease, 400 images of rice affected by Brown Spot disease, 400 images of rice affected by Leaf Smut disease, and 400 images of healthy rice.In each class, the images are divided into 380 for training and 20 for testing.During the rice image training process using the Python programming language, the accuracy results were 83%.Then the results are saved in the form of a model file, and entered as training data into the program in Android Studio to be used as an application.Testing the application with 20 rice leaf images for each class resulted in an actual accuracy of 94%.This application can also be run on all versions of Android.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".