Green Bean Classification: Fully Convolutional Neural Network with Adam Optimization
Bibliographic record
Abstract
The main issue faced by coffee growers is the lack of accuracy in manual sorting.Green Bean photos are classified into three categories (dark, green, light) using the convolutional neural network approach.This study employs 360 datasets, each consisting of 120 data points per class.The CNN model uses a 512×512×3 image as its input.The number three represents the use of three specific channels: red, green, and blue.RGB is an acronym that represents the colors Red, Green, and Blue.During the feature learning phase, the input image is subjected to conversion and pooling processes.The convolutions exhibit variations in the utilization of filters and kernel sizes.The model was developed utilizing the relay activation function and pooling techniques.The dropout technique entails transforming the feature maps derived from the pooling layer into a vector shape by flattening them.This study utilized three data scenarios: 70:30, 80:20, and 90:10.The number of epochs used for each scenario was 30.This analysis compared the performance of three optimization algorithms: Adam, RMSprop, and Nadam.The optimization model using Adam with a parameter of Epoch 30 and a data scenario of 80:20 achieved the highest accuracy result of 81.67%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".