Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method
Bibliographic record
Abstract
Eggs are widely consumed due to their high content of vitamin B12, choline and iron.External factors can be detected by observing the eggshell or taken into consideration.The nutrition in eggs is influenced by egg quality, which can be directly observed from the shell.If the shell is cracked, it can be inferred that the egg has poor quality because Salmonella bacteria are dangerous pathogens that can enter the egg.The current issue lies in the complexity and inefficiency of individually classifying eggs by workers, as it is a complicated, time-consuming, frustrating, and inefficient task.Therefore, it is important to separate them automatically.The selection of cracked and intact eggs in this research is an innovative approach to classification using a highly accurate machine learning method.The application of the GLCM-CNN method is an innovative strategy employed for selecting and classifying cracked eggs, as outlined in this research.VGG 19, one of the computational methods, is utilized as a comparative method alongside RESNET 50 and VGG 16.The GLCM-CNN algorithm in this research employed 1,000 images for each class, with a validation set of 20% for each class, resulting in an accuracy of 98%.The inefficient classification process and complexity of automated egg quality classification can be significantly addressed through the findings presented in this research.
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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 | 0.001 |
| 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".