Bean Sorting Assessment for Quality Recognition Using CNN withYOLOv8 and Roboflow
Bibliographic record
Abstract
In today's agricultural industry, product sorting has become an increasingly important task.More specifically, bean grading is a job done mainly by hand, which has caused great limitations in terms of efficiency and speed of these processes.This project presents a solution through the development of a CNN, designed to analyze images of red beans in good condition, red beans in bad condition and contaminants or dirt to evaluate accurately and effectively.The objective of this project was to promote the incorporation of new technologies in the automation of processes in this sector, promoting cleaner and more sustainable practices in the long term.Using Deep Learning platforms, such as YOLOv8 and RoboFlow, a network capable of performing accurate analysis to identify these 3 elements and in turn ensure the accuracy of the process would be trained, This research used an incremental methodology to perform the analyses, segmented into 4 increments analyzing each element individually and a fourth increment to combine the previous ones.A dataset of more than 4,500 images was used, with 13 variations in the background of the images, variations in illuminations, camera angles and distance of photographs, thus obtaining a %mAP of 98.7% highlighting the effectiveness obtained.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".