Identification of Banana Fruit Types Using the Backpropagation Method
Bibliographic record
Abstract
Identification of types of bananas and assessment of their maturity level is an important process in the agricultural and distribution industries. In an effort to automate this process, the authors propose an approach to identify bananas and their level of ripeness using a Backpropagation neural network. Through digital image processing, images or pictures of bananas will be extracted with images such as RGB (red green blue), metric and eccentricity (shape features). The results of the image data training process are as many as 55 image data input, obtained by the training process data on banana types with 11 iterations from the maximum input epoch 10000, target error or performance 0.00642 with an accuracy value of 80%. Furthermore, the training process obtained data on the maturity level of bananas with 4 iterations from the maximum input epoch 10000, the target error or performance is 0.00606 with an accuracy value of 90%. From the test image process that has been carried out, the system can identify the type of banana and its maturity level based on the feature extraction input from the image of the banana. This study also aims to test and determine the accuracy of the application of the Backpropagation method in identifying the types of bananas and their level of maturity.
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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) | 0.001 | 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".